CN116866833A - Fingerprint database generation method and device for indoor positioning - Google Patents

Abstract

The application discloses a method and a device for generating a fingerprint database for indoor positioning. Wherein the method comprises the following steps: dividing the target area into a line-of-sight area and a non-line-of-sight area; determining a first fingerprint database corresponding to each coordinate point in the sight distance area, wherein fingerprint information in the first fingerprint database is used for indicating positioning information of each coordinate point included in the target area; determining a target running track in a non-line-of-sight area by utilizing a pedestrian dead reckoning algorithm, and determining a second fingerprint database corresponding to each coordinate point in the target running track; and determining fingerprint databases of all coordinate points in the target area according to the first fingerprint database and the second fingerprint database. The application solves the technical problems of excessive expenditure in time, manpower and cost of the updating method of the fingerprint library in the existing indoor positioning technology.

Description

Fingerprint database generation method and device for indoor positioning

Technical Field

The application relates to the field of indoor positioning, in particular to a method and a device for generating a fingerprint database for indoor positioning.

Background

Ultra Wide Band (UWB) technology is a wireless carrier communication technology, which does not use a sinusoidal carrier, but uses non-sinusoidal narrow pulses of nanosecond order to transmit data. The UWB technology has the advantages of low system complexity, low power spectrum density of the transmitted signal, insensitivity to channel fading, low interception capability, high positioning accuracy and the like, and is particularly suitable for high-speed wireless access in indoor and other dense multipath places.

The current indoor positioning technology based on the UWB technology mainly uses a plurality of UWB base stations, collects the distance information of different indoor places and constructs a fingerprint database; and then, carrying out online position inquiry based on a pre-established fingerprint database when in use, and calculating the current indoor position of the traveler.

However, in the existing indoor positioning based on UWB fingerprint, in the indoor environment with more reflective materials and complex signal environment such as a machine room, the traditional method for performing triangulation positioning matching by using a fingerprint database cannot meet the requirement of accurate positioning, and often has the problem of position error or signal loss. The problem can only be solved by adding more base stations or enlarging the fingerprint library. These methods all require a significant amount of labor, time or cost overhead. Currently, some researchers replace human beings with robots with high-precision laser radar SLAM devices to perform cumbersome fingerprint acquisition work, but high-precision instruments are expensive, still consume a lot of time and cannot perform fingerprint library updating in a short time.

Therefore, the updating method of the fingerprint database in the prior art has the technical problem of overlarge expenditure on time, manpower and cost.

In view of the above problems, no effective solution has been proposed at present.

Disclosure of Invention

The embodiment of the application provides a method and a device for generating a fingerprint database for indoor positioning, which at least solve the technical problems of excessive expenditure in time, manpower and cost of a fingerprint database updating method in the existing indoor positioning technology.

According to an aspect of the embodiment of the present application, there is provided a method for generating a fingerprint database for indoor positioning, including: dividing a target area into a sight distance area and a non-sight distance area, wherein the target area is an area for indoor positioning, signals transmitted by positioning tags in the sight distance area can be directly acquired by a wireless communication base station, and signals transmitted by the positioning tags in the non-sight distance area cannot be directly acquired by the wireless communication base station due to shielding; determining a first fingerprint database corresponding to each coordinate point in the sight distance area, wherein fingerprint information in the first fingerprint database is used for indicating positioning information of each coordinate point included in the target area; determining a target running track in a non-line-of-sight area by utilizing a pedestrian dead reckoning algorithm, and determining a second fingerprint database corresponding to each coordinate point in the target running track; and determining fingerprint databases of all coordinate points in the target area according to the first fingerprint database and the second fingerprint database.

Optionally, dividing the target area into a line-of-sight area and a non-line-of-sight area includes: acquiring the signal strength of a communication signal between a positioning tag and a wireless communication base station; acquiring distance information between the positioning tag and the wireless communication base station under the condition that the signal intensity is smaller than the preset signal intensity; calculating a rank of a matrix composed of distance information; and dividing coordinate points where the positioning labels are positioned into a line-of-sight area or a non-line-of-sight area according to the rank of the matrix.

Optionally, dividing the coordinate point where the positioning tag is located into a line-of-sight area or a non-line-of-sight area according to the rank of the matrix includes: dividing coordinate points where the positioning labels are positioned into non-line-of-sight areas under the condition that the rank of the matrix does not meet the preset condition; under the condition that the rank of the matrix meets a preset condition, determining a first coordinate of a coordinate point where the positioning label is located; acquiring a second coordinate of a coordinate point of the positioning label at a time before the current time; determining the distance between a coordinate point corresponding to the first coordinate and a coordinate point corresponding to the second coordinate according to the first coordinate and the second coordinate; dividing a coordinate point corresponding to the first coordinate into a viewing distance area under the condition that the distance is smaller than a preset measurement interval; and dividing the coordinate point corresponding to the first coordinate into a non-line-of-sight region under the condition that the distance is greater than or equal to the preset measurement interval.

Optionally, determining a first fingerprint database corresponding to each coordinate point in the line-of-sight area includes: respectively acquiring a first distance matrix formed by the distances between each coordinate point in the line-of-sight area and the wireless communication base station; respectively acquiring a first signal intensity matrix formed by the signal intensities of communication signals between each coordinate point in the line-of-sight area and the wireless communication base station; respectively acquiring first line-of-sight weights of all coordinate points of the wireless communication base station in a line-of-sight area; and storing the first distance matrix, the first signal intensity matrix and the first line-of-sight weight into a first fingerprint database.

Optionally, determining the target moving track in the non-line-of-sight area by using a pedestrian dead reckoning algorithm includes: determining a first target coordinate point corresponding to the positioning label in the process that the positioning label moves from the sight distance area to the non-sight distance area, and determining the first target coordinate point as an autonomous positioning control point, wherein the first target coordinate point is the last coordinate point in a moving track of the positioning label in the sight distance area, and the sight distance weight corresponding to the first target coordinate point is higher than a preset weight; and determining a target running track in the non-line-of-sight area by using the self-positioning control point as a starting point and utilizing a pedestrian dead reckoning algorithm.

Optionally, determining a second fingerprint database corresponding to each coordinate point in the target running track includes: respectively acquiring a second distance matrix formed by the distances between each coordinate point in the target running track and the wireless communication base station; respectively acquiring a second signal intensity matrix formed by the signal intensities of communication signals between each coordinate point in the target running track and the wireless communication base station; respectively acquiring second line-of-sight weights of all coordinate points of the wireless communication base station in a target running track; and storing the second distance matrix, the second signal intensity matrix and the second line-of-sight weight into a second fingerprint database.

Optionally, after determining the first fingerprint database corresponding to each coordinate point in the line-of-sight area, the method further includes: detecting whether a second target coordinate point in the viewing distance area still belongs to the viewing distance area according to a preset duration, wherein the second target coordinate point is any coordinate point in the viewing distance area; under the condition that the second target coordinate point still belongs to the line-of-sight area is detected, recording a distance measurement error between the second target coordinate point and the wireless communication base station; and under the condition that the accumulated distance measurement errors are larger than the preset measurement interval, updating the average value of the accumulated distance measurement errors to a first distance matrix corresponding to the second target coordinate point.

Optionally, the method further comprises: under the condition that the second target coordinate point is detected not to belong to the line-of-sight area, determining an autonomous positioning control point again; the re-determined autonomous positioning control point is taken as a starting point, and a pedestrian dead reckoning algorithm is utilized to re-determine a target running track in a non-line-of-sight area; and establishing a second fingerprint database of each coordinate point in the redetermined target running track.

Optionally, after determining the second fingerprint database corresponding to each coordinate point in the target running track, the method further includes: detecting whether an autonomous positioning control point corresponding to a non-line-of-sight area changes; under the condition that the autonomous positioning control point corresponding to the non-line-of-sight area is detected to change, determining the similarity between the current signal intensity of the coordinate point needing to be updated in the non-line-of-sight area and the corresponding signal intensity in the second fingerprint data; and updating the data of the coordinate points to be updated according to the preset probability under the condition that whether the similarity is larger than the preset similarity threshold value.

Optionally, the method further comprises: under the condition that the autonomous positioning control points corresponding to the non-line-of-sight areas are not changed, acquiring position measurement errors of the autonomous positioning control points corresponding to the non-line-of-sight areas; and updating the data of the coordinate points to be updated according to the preset probability under the condition that the accumulated quantity of the position measurement errors exceeds the preset threshold value.

Optionally, the preset probability is a ratio of a current signal intensity of the coordinate point to be updated in the non-line-of-sight region to a corresponding signal intensity in the second fingerprint data.

According to another aspect of the embodiment of the present application, there is also provided a generating apparatus of a fingerprint database for indoor positioning, including: the dividing module is used for dividing the target area into a sight distance area and a non-sight distance area, wherein the target area is an area for indoor positioning, signals transmitted by positioning tags in the sight distance area can be directly acquired by the wireless communication base station, and signals transmitted by the positioning tags in the non-sight distance area cannot be directly acquired by the wireless communication base station due to shielding; the first determining module is used for determining a first fingerprint database corresponding to each coordinate point in the line-of-sight area, wherein fingerprint information in the first fingerprint database is used for indicating positioning information of each coordinate point included in the target area; the second determining module is used for determining a target running track in a non-line-of-sight area by utilizing a pedestrian dead reckoning algorithm and determining a second fingerprint database corresponding to each coordinate point in the target running track; and the processing module is used for determining the fingerprint database of each coordinate point in the target area according to the first fingerprint database and the second fingerprint database.

According to still another aspect of the embodiment of the present application, there is also provided an indoor positioning apparatus, including: the wireless communication system comprises a wireless communication base station and a tag, wherein the tag is used for transmitting signals at any coordinate point in a target area; a wireless communication base station, which communicates with the tag, for executing the fingerprint database generation method for indoor positioning in any one of the above embodiments.

According to still another aspect of the embodiments of the present application, there is further provided a nonvolatile storage medium, in which a program is stored, wherein when the program runs, a device on which the nonvolatile storage medium is controlled to execute the method for generating a fingerprint database for indoor positioning in any one of the above embodiments.

In the embodiment of the application, the target area is divided into the sight distance area and the non-sight distance area, wherein the target area is an area for indoor positioning, the signals transmitted by the positioning tags in the sight distance area can be directly acquired by the wireless communication base station, and the signals transmitted by the positioning tags in the non-sight distance area cannot be directly acquired by the wireless communication base station due to shielding; determining a first fingerprint database corresponding to each coordinate point in the sight distance area, wherein fingerprint information in the first fingerprint database is used for indicating positioning information of each coordinate point included in the target area; determining a target running track in a non-line-of-sight area by utilizing a pedestrian dead reckoning algorithm, and determining a second fingerprint database corresponding to each coordinate point in the target running track; according to the mode that the fingerprint databases of all coordinate points in the target area are determined by the first fingerprint database and the second fingerprint database, the fingerprint database with the confidence weight is built by acquiring position information with higher timeliness accuracy by adopting a pedestrian track residual error network, the purposes that the whole fingerprint database can be built and updated in real time only by a single person holding a UWB label are achieved, the technical effects of reducing the expenditure of manpower, expense and time when the fingerprint database is updated in the indoor positioning technology are achieved, and the technical problems that the time is saved and the expenditure is overlarge in the manpower are solved in the fingerprint database updating method in the existing indoor positioning technology.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:

fig. 1 shows a hardware block diagram of a computer terminal (or mobile device) for implementing a method of generating a fingerprint database for indoor positioning;

FIG. 2 is a flow chart providing a method of generating a fingerprint database for indoor positioning according to an embodiment of the present application;

FIG. 3 is a block diagram showing a structure of a fingerprint database generating apparatus for indoor positioning according to an embodiment of the present application;

fig. 4 is a block diagram of an indoor positioning apparatus according to an embodiment of the present application.

Detailed Description

In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In order to better understand the embodiments of the present application, technical terms related to the embodiments of the present application are explained as follows:

fingerprint database: the fingerprint database comprises positioning fingerprint information corresponding to each indoor space, and the positioning fingerprint information comprises the wireless network equipment scanned in the indoor space and the intensity values of the wireless network equipment.

In the process of realizing the technical scheme of the application, the application mainly solves the problems of three aspects existing in the prior art:

problem of high indoor positioning cost: the technical scheme provided by the application only needs to comprise a UWB transmitting chip and an inertial sensor (Intertial Measurement Unit, IMU) UWB positioning tags when the fingerprint library is updated. Therefore, the cost of other redundant equipment can be greatly reduced under the condition of the same number of UWB base stations, and a fingerprint library which is more accurate and updated in real time can be established in a larger range under the condition of the same cost. The application solves the problem of high cost in other current solutions, and realizes higher real-time performance and lower cost.

Problem of timeliness of fingerprint library: in the existing indoor positioning method with complex environment, the fingerprint library matching method is a necessary positioning means, so that the accuracy of indoor positioning is directly related to the quality of the fingerprint library. However, in other solutions, the fingerprint database is generally updated after a long time after the fingerprint database is established, so that the quality of the fingerprint database updated in non-real time is degraded due to the lapse of time, thereby generating errors in the positioning result. The application can realize the real-time updating of the fingerprint library, and the fingerprint library can be updated when the change is accumulated to a certain number of products, thereby greatly reducing the maintenance cost of the system and improving the positioning accuracy.

General purpose problem: other better solutions at present are often optimized only for a specific environment, and the limitation of changing to another indoor layout method may be reflected. The technical scheme provided by the application is not specially designed for a special environment at the beginning, so that the method can be used efficiently and normally only in places meeting UWB positioning conditions.

The following is a detailed description of specific embodiments:

according to an embodiment of the present application, there is provided an embodiment of a method of generating a fingerprint database for indoor positioning, it being noted that the steps shown in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowchart, in some cases the steps shown or described may be performed in an order different from that herein.

The method embodiments provided by the embodiments of the present application may be performed in a mobile terminal, a computer terminal, or similar computing device. Fig. 1 shows a block diagram of a hardware architecture of a computer terminal (or mobile device) for implementing a method of generating a fingerprint database for indoor positioning. As shown in fig. 1, the computer terminal 10 (or mobile device 10) may include one or more processors 102 (shown as 102a, 102b, … …,102 n) which may include, but are not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA, a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, the method may further include: a display, an input/output interface (I/O interface), a Universal Serial BUS (USB) port (which may be included as one of the ports of the BUS), a network interface, a power supply, and/or a camera. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.

It should be noted that the one or more processors 102 and/or other data processing circuits described above may be referred to generally herein as "data processing circuits. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. Furthermore, the data processing circuitry may be a single stand-alone processing module, or incorporated, in whole or in part, into any of the other elements in the computer terminal 10 (or mobile device). As referred to in embodiments of the application, the data processing circuit acts as a processor control (e.g., selection of the path of the variable resistor termination connected to the interface).

The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the method for generating a fingerprint database for indoor positioning in the embodiment of the present application, and the processor 102 executes the software programs and modules stored in the memory 104 to perform various functional applications and data processing, that is, implement the method for generating a fingerprint database for indoor positioning of the application program. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission means 106 is arranged to receive or transmit data via a network. The specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 10 (or mobile device).

In the above operating environment, the embodiment of the present application provides a method for generating a fingerprint database for indoor positioning, as shown in fig. 2, the method includes the following steps:

in step S202, the target area is divided into a line-of-sight area and a non-line-of-sight area, wherein the target area is an area for indoor positioning, the signal transmitted by the positioning tag in the line-of-sight area can be directly acquired by the wireless communication base station, and the signal transmitted by the positioning tag in the non-line-of-sight area cannot be directly acquired by the wireless communication base station due to being blocked.

In this step, the line-of-sight area is distinguished from the non-line-of-sight area, and this step will enable the use of the UWB base station to directly locate the accurately, i.e., the line-of-sight area; the signal emitted by the tag cannot reach the base station directly due to the occlusion and can only reach the area of the base station, i.e. the non line of sight area, by one or more reflections.

Step S204, determining a first fingerprint database corresponding to each coordinate point in the line-of-sight region, where fingerprint information in the first fingerprint database is used to indicate positioning information of each coordinate point included in the target region.

And S206, determining a target running track in a non-line-of-sight area by utilizing a pedestrian dead reckoning algorithm, and determining a second fingerprint database corresponding to each coordinate point in the target running track.

Step S208, determining fingerprint databases of all coordinate points in the target area according to the first fingerprint database and the second fingerprint database.

Through the steps, the position information with higher timeliness accuracy is acquired by adopting the pedestrian track residual error network, the fingerprint library with the confidence weight is built, and the purposes of building the whole fingerprint library and updating in real time can be achieved by only holding the UWB label by a single person, so that the technical effects of reducing the expenditure of manpower, cost and time when the fingerprint library is updated in the indoor positioning technology are achieved.

According to some optional embodiments of the application, performing step S202 to divide the target area into a line-of-sight area and a non-line-of-sight area comprises the steps of: acquiring the signal strength of a communication signal between a positioning tag and a wireless communication base station; acquiring distance information between the positioning tag and the wireless communication base station under the condition that the signal intensity is smaller than the preset signal intensity; calculating a rank of a matrix composed of distance information; and dividing coordinate points where the positioning labels are positioned into a line-of-sight area or a non-line-of-sight area according to the rank of the matrix.

According to other optional embodiments of the present application, dividing coordinate points where the positioning tag is located into a line-of-sight area or a non-line-of-sight area according to a rank of the matrix includes: dividing coordinate points where the positioning labels are positioned into non-line-of-sight areas under the condition that the rank of the matrix does not meet the preset condition; under the condition that the rank of the matrix meets a preset condition, determining a first coordinate of a coordinate point where the positioning label is located; acquiring a second coordinate of a coordinate point of the positioning label at a time before the current time; determining the distance between a coordinate point corresponding to the first coordinate and a coordinate point corresponding to the second coordinate according to the first coordinate and the second coordinate; dividing a coordinate point corresponding to the first coordinate into a viewing distance area under the condition that the distance is smaller than a preset measurement interval; and dividing the coordinate point corresponding to the first coordinate into a non-line-of-sight region under the condition that the distance is greater than or equal to the preset measurement interval.

Firstly, a measurer walks at will in a deployment area, and a system can continuously acquire distance data and signal intensity data between a base station and a tag in the walking process. And analyzing whether the data is the visual direct distance according to the information measured by the base station on the action track, so as to divide the vision range area. First, the signal intensity Q of the communication between the tag and the base station n is acquired _n If Q _n Is stronger than the set trusted threshold Q _min The distance measured by the tag from the base station is proved to be possibly a visual direct distance.

Next, distance information D between the tag and each base station is collected _t Calculating the rank of the matrix, if r (D) =3 can be made in the error range, if the condition is not satisfied, dividing the point into non-line-of-sight areas directly, if the condition is satisfied, further solving the position coordinate information A of the point _now (i _now ,j _now ) And is matched with the last position coordinate A _last (i _last ,j _last ) Comparing, if the distance between two points is less than or equal to the measurement interval d _gap The point is divided into line-of-sight regions. And if the video signal is larger than the video signal, dividing the video signal into non-line-of-sight areas.

As some optional embodiments of the present application, executing step S204 to determine a first fingerprint database corresponding to each coordinate point in the line-of-sight area includes: respectively acquiring a first distance matrix formed by the distances between each coordinate point in the line-of-sight area and the wireless communication base station; respectively acquiring a first signal intensity matrix formed by the signal intensities of communication signals between each coordinate point in the line-of-sight area and the wireless communication base station; respectively acquiring first line-of-sight weights of all coordinate points of the wireless communication base station in a line-of-sight area; and storing the first distance matrix, the first signal intensity matrix and the first line-of-sight weight into a first fingerprint database.

For coordinate points in the range reachable region, the distance matrix D, the signal intensity matrix A and the range weight of the base station at the point are calculatedThe information is stored in a fingerprint library, wherein the calculation method of the sight distance weight is as follows:

the point set of the line-of-sight area can be positioned quickly by directly using the base station data during positioning, so that the positioning time is saved, and the timeliness of positioning information is improved.

In some alternative embodiments of the present application, performing step S206 to determine a target trajectory in a non-line-of-sight region using a pedestrian dead reckoning algorithm includes: determining a first target coordinate point corresponding to the positioning label in the process that the positioning label moves from the sight distance area to the non-sight distance area, and determining the first target coordinate point as an autonomous positioning control point, wherein the first target coordinate point is the last coordinate point in a moving track of the positioning label in the sight distance area, and the sight distance weight corresponding to the first target coordinate point is higher than a preset weight; and determining a target running track in the non-line-of-sight area by using the self-positioning control point as a starting point and utilizing a pedestrian dead reckoning algorithm.

When the label enters the non-line-of-sight area, the control point is taken as a starting point, and the relative position on the track given by a PDR method is a trusted position.

For non line-of-sight areas, the reliability of the point set to build a fingerprint library using the data of the UWB base station is low. Therefore, the application combines with the IMU sensor, designs the residual error network to calculate the pedestrian track, and ensures the track accuracy. The application adopts a pedestrian dead reckoning algorithm (Pedestrian Dead Reckoning, PDR) to maintain a high available walking route for sample collection work. The technology utilizes an inertial sensor IMU (inertial sensor unit) built in a tag or other handheld terminal equipment to calculate gait information of pedestrians in each walking period based on a step size model. The IMU takes the handheld terminal equipment as a reference, takes the upward direction perpendicular to the screen of the equipment as a Z axis, takes the direction of the upper edge of the screen of the mobile phone as a Y axis, and takes the direction of the normal vector of the YZ plane as an X axis to construct an IMU coordinate system. In this coordinate system, the IMU may acquire positioning-related data based on acceleration, angular velocity, and the like in this coordinate system. Through the gesture information, the IMU coordinate system may be converted into a navigation coordinate system or a body coordinate system. The former is a coordinate system corresponding to the relative track output by the PDR, and the latter is a coordinate system constructed by taking the body of the pedestrian as a reference. The data information to be processed can be acquired under the coordinate system.

And in the resolving process, resolving is carried out in a quaternion attitude expression form so as to avoid mapping caused by the problem of universal lock. It is generally expressed as (cos (θ/2), xsin (θ/2), ysin (θ/2), zsin (θ/2)), where θ represents the rotation angle and (x, y, z) represents the rotation axis unit vector. For vector a, rotation can be performed using quaternions

a′＝q×a×q ^*

Wherein q ^* Representing the conjugated quaternion of q. Meanwhile, for visual representation, the quaternion and the Euler angle are mutually converted, the rotation in the sequence of 'ZYX' is agreed, the Euler angle is (gamma, beta, alpha), the quaternion is (w, x, y, z), and the conversion formula is as follows:

β＝arcsin(2(wy-zx))

in practical application, the magnetometer can calculate the horizontal heading alpha of the IMU under the global coordinate system, the attitude quaternion is converted into the Euler angle to obtain the horizontal heading beta of the IMU under the navigation coordinate system, and the two headings are added to obtain the rotation angle gamma from the navigation coordinate system to the global coordinate system. According to gamma calculation horizontal rotation quaternion, IMU data can be unified to a global coordinate system according to the following formula

Wherein,,

so far, the positioning data in the current environment can be accurately calculated based on the IMU data in the global coordinate system.

In other optional embodiments of the present application, the determining of the second fingerprint database corresponding to each coordinate point in the target running track in step S206 is implemented by the following method: respectively acquiring a second distance matrix formed by the distances between each coordinate point in the target running track and the wireless communication base station; respectively acquiring a second signal intensity matrix formed by the signal intensities of communication signals between each coordinate point in the target running track and the wireless communication base station; respectively acquiring second line-of-sight weights of all coordinate points of the wireless communication base station in a target running track; and storing the second distance matrix, the second signal intensity matrix and the second line-of-sight weight into a second fingerprint database.

Meanwhile, a distance matrix D, a signal intensity matrix A,and the weight matrix W is recorded in the fingerprint library. Wherein for each ranging information vector in the fingerprint library, the weight matrix W corresponds to one weight vector

For each value of the weight vector, the following is calculated:

wherein,,for the line-of-sight weight of the location control point, K represents the length of the ranging information vector, K represents the sample size contained in each control feature, and λ is an introduced smoothing term to avoid the problem caused by zero removal. For coordinate points with larger measured position deviation and larger data instability by two methods of PDR and UWB base station positioning in the track, the method also uses a DQN network to assist in re-planning a route according to the existing track information, and obtains the route to be measured next time. Thus, the fingerprint library of the non-line-of-sight area is also built.

In some optional embodiments of the present application, after determining a first fingerprint database corresponding to each coordinate point in the line-of-sight area, detecting whether a second target coordinate point in the line-of-sight area still belongs to the line-of-sight area according to a preset duration, where the second target coordinate point is any coordinate point in the line-of-sight area; under the condition that the second target coordinate point still belongs to the line-of-sight area is detected, recording a distance measurement error between the second target coordinate point and the wireless communication base station; and under the condition that the accumulated distance measurement errors are larger than the preset measurement interval, updating the average value of the accumulated distance measurement errors to a first distance matrix corresponding to the second target coordinate point.

As some optional embodiments of the present application, if it is detected that the second target coordinate point does not belong to the line-of-sight region, the autonomous positioning control point is redetermined; the re-determined autonomous positioning control point is taken as a starting point, and a pedestrian dead reckoning algorithm is utilized to re-determine a target running track in a non-line-of-sight area; and establishing a second fingerprint database of each coordinate point in the redetermined target running track.

In order to ensure that the fingerprint database of the line-of-sight area is still reliable and effective when the indoor environment changes, the part of the line-of-sight area in the fingerprint database is updated in the using process of the fingerprint database. When the fingerprint database is used, the system can collect base station information in the line-of-sight area and compare the information in the fingerprint database, if the point is detected to not belong to the line-of-sight area according to the above-mentioned dividing method, the autonomous positioning control point is immediately revised, IMU sensor data are collected, an accurate position is obtained by using a PDR method, the distance and signal intensity information coverage is updated to the database, and a weight vector is reset. If it still belongs to the vision distance area, recording the measurement error, when the error accumulation is greater than the measurement interval d _gap Average matrix of accumulated errorsAnd incrementally updating the distance matrix D of the corresponding point fingerprint library, so that the instantaneity of the sight distance area fingerprint library is ensured.

In other optional embodiments of the present application, after determining the second fingerprint database corresponding to each coordinate point in the target moving track, detecting whether the autonomous positioning control point corresponding to the non-line-of-sight area changes; under the condition that the autonomous positioning control point corresponding to the non-line-of-sight area is detected to change, determining the similarity between the current signal intensity of the coordinate point needing to be updated in the non-line-of-sight area and the corresponding signal intensity in the second fingerprint data; and updating the data of the coordinate points to be updated according to the preset probability under the condition that whether the similarity is larger than the preset similarity threshold value.

As some optional embodiments of the present application, under the condition that no change is detected to occur to the autonomous positioning control point corresponding to the non-line-of-sight area, acquiring a position measurement error of the autonomous positioning control point corresponding to the non-line-of-sight area; and updating the data of the coordinate points to be updated according to the preset probability under the condition that the accumulated quantity of the position measurement errors exceeds the preset threshold value.

Optionally, the preset probability is a ratio of a current signal intensity of the coordinate point to be updated in the non-line-of-sight region to a corresponding signal intensity in the second fingerprint data.

Judging whether the non-line-of-sight area fingerprint library needs to be updated or not: firstly, detecting whether an autonomous positioning control point set corresponding to an area changes or not, if any point generates updating, reducing the weight of a fingerprint database of the area, directly updating according to the following steps, if the control point set is stable, recording the positioning information of the point positions in the area each time the area is measured, and updating when errors are accumulated to a certain number of products.

The method for updating the non-line-of-sight region fingerprint library comprises the following steps: for the location coordinate point to be updated, determining the similarity of the signal intensity distribution feature and the location data, wherein the calculation mode is thatJudging the relation between the similarity and a preset priori threshold, if the similarity is smaller than the priori threshold, discarding the similarity, and if the similarity is larger than the priori threshold, obtaining probability +.>Wherein rf ^* And rf is the signal intensity characteristic of the corresponding fingerprint in the fingerprint library for the signal intensity characteristic of the corresponding fingerprint during positioning.

The whole set of updating formula is as follows:

wherein f is the updated fingerprint, f ^* For the fingerprint data generated during positioning, N is the amount of data contained in the fingerprint library.

And judging whether all data are involved in updating at present, if so, ending the incremental updating operation, and if not, returning to continue to judge the residual data.

As an alternative embodiment of the application, the target area may be an information center supercomputer room, where both the cabinet and walls have a great impact on the basic UWB positioning method, where the measured data of the conventional positioning method is almost completely unavailable.

And respectively deploying 8 wireless communication base stations in the machine room to enable the wireless communication base stations to cover the whole room as much as possible. A certain base station is wired to the network for the background to acquire data.

The measuring staff holds the label to walk in the computer lab, and the system calculates the autonomous positioning control point and establishes fingerprint storehouse automatically, if detect personnel stop and still have the regional incomplete sampling, the backstage can draw next measuring route in the front end, reminds measuring staff's next direction of walking. And after the fingerprint library is established, starting to measure the accuracy of the positioning data by walking at will.

Compared with the prior art, the application has the following advantages and effects:

the application has the advantages of low deployment cost, low maintenance cost, good real-time performance and high accuracy.

The method for selecting the autonomous positioning control point provided by the application is different from other methods, points with poor visual distance positioning effect in other methods are usually manually calibrated, so that the labor cost is greatly increased, and the method can automatically distinguish between visual distance and non-visual distance areas by combining the known base station information and distance matrix operation, so that the step of manual calibration is omitted, and the labor cost is reduced.

The PDR pedestrian track residual error network method designed by the application effectively utilizes the time dimension information during indoor walking, and can infer more accurate position information, so that compared with other methods, the application can save the cost of other high-precision measuring instruments such as SLAM and the like, and can complete tasks only by being provided with relatively low-cost IMU sensors, and compared with other methods, the cost in equipment is greatly reduced.

Meanwhile, the PDR pedestrian track residual error network is adopted in the scheme, and different from other schemes, the IMU sensor arranged in the tag can transmit information of two dimensions in time and space into the system during positioning, so that the system can record errors of the real position and the matching position, and when a certain number of products are accumulated, the fingerprint library is directly and automatically updated, and compared with other schemes, the fingerprint library has stronger timeliness, and meanwhile, a large amount of subsequent manual maintenance work is omitted, so that the positioning cost is further reduced.

The weight fingerprint library matrix provided by the application is similar to some other schemes in concept, but structurally redesigned, and the utilization of the weight information obtained in the first two steps of the scheme and the historical data and the time dimension data are combined, meanwhile, because the sight distance area and the non-sight distance area are distinguished, the traditional positioning calculation can be directly used for the sight distance area during positioning, and the fingerprint library does not need to be traversed, so that the application has higher positioning success rate than other schemes, and has greatly improved accuracy and instantaneity.

Fig. 3 is a block diagram of a fingerprint database generating apparatus for indoor positioning according to an embodiment of the present application, as shown in fig. 3, the apparatus includes:

the dividing module 30 is configured to divide the target area into a line-of-sight area and a non-line-of-sight area, where the target area is an area for indoor positioning, a signal transmitted by a positioning tag in the line-of-sight area can be directly acquired by the wireless communication base station, and a signal transmitted by a positioning tag in the non-line-of-sight area cannot be directly acquired by the wireless communication base station due to being blocked.

The sight distance area and the non-sight distance area are distinguished, and the UWB base station can be used for directly and accurately positioning the area, namely the sight distance area; the signal emitted by the tag cannot reach the base station directly due to the occlusion and can only reach the area of the base station, i.e. the non line of sight area, by one or more reflections.

The first determining module 32 is configured to determine a first fingerprint database corresponding to each coordinate point in the line-of-sight area, where fingerprint information in the first fingerprint database is used to indicate positioning information of each coordinate point included in the target area.

The second determining module 34 is configured to determine a target moving track in the non-line-of-sight area by using a pedestrian dead reckoning algorithm, and determine a second fingerprint database corresponding to each coordinate point in the target moving track.

The processing module 36 is configured to determine a fingerprint database of each coordinate point in the target area according to the first fingerprint database and the second fingerprint database.

The respective modules in the fingerprint database generating device for indoor positioning may be program modules (for example, a set of program instructions for implementing a specific function), or may be hardware modules, and for the latter, the modules may be represented by the following forms, but are not limited thereto: the expression forms of the modules are all a processor, or the functions of the modules are realized by one processor.

Fig. 4 is a block diagram illustrating an indoor positioning apparatus according to an embodiment of the present application, as shown in fig. 4, including: a wireless communication base station 40, and a tag 42, wherein,

a tag 42 for transmitting a signal at any one coordinate point in the target area;

the wireless communication base station 40 communicates with the tag 42 for performing the fingerprint database generation method for indoor positioning in any of the above embodiments.

The embodiment of the application also provides a nonvolatile storage medium, wherein a program is stored in the nonvolatile storage medium, and the device where the nonvolatile storage medium is located is controlled to execute the generation method of the fingerprint database for indoor positioning in any embodiment when the program runs.

The above-described nonvolatile storage medium is used to store a program that performs the following functions: dividing a target area into a sight distance area and a non-sight distance area, wherein the target area is an area for indoor positioning, signals transmitted by positioning tags in the sight distance area can be directly acquired by a wireless communication base station, and signals transmitted by the positioning tags in the non-sight distance area cannot be directly acquired by the wireless communication base station due to shielding; determining a first fingerprint database corresponding to each coordinate point in the sight distance area, wherein fingerprint information in the first fingerprint database is used for indicating positioning information of each coordinate point included in the target area; determining a target running track in a non-line-of-sight area by utilizing a pedestrian dead reckoning algorithm, and determining a second fingerprint database corresponding to each coordinate point in the target running track; and determining fingerprint databases of all coordinate points in the target area according to the first fingerprint database and the second fingerprint database.

The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.

In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed technology may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the related art or all or part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application, which are intended to be comprehended within the scope of the present application.

Claims (14)

1. A method for generating a fingerprint database for indoor positioning, comprising:

dividing a target area into a sight distance area and a non-sight distance area, wherein the target area is an area for indoor positioning, signals transmitted by positioning tags in the sight distance area can be directly acquired by a wireless communication base station, and signals transmitted by the positioning tags in the non-sight distance area cannot be directly acquired by the wireless communication base station due to shielding;

determining a first fingerprint database corresponding to each coordinate point in the line-of-sight region, wherein fingerprint information in the first fingerprint database is used for indicating positioning information of each coordinate point included in the target region;

determining a target running track in the non-line-of-sight area by utilizing a pedestrian dead reckoning algorithm, and determining a second fingerprint database corresponding to each coordinate point in the target running track;

and determining fingerprint databases of all coordinate points in the target area according to the first fingerprint database and the second fingerprint database.

2. The method of claim 1, wherein dividing the target area into a line-of-sight area and a non-line-of-sight area comprises:

Acquiring the signal strength of a communication signal between the positioning tag and the wireless communication base station;

acquiring distance information between the positioning tag and the wireless communication base station under the condition that the signal intensity is smaller than a preset signal intensity;

calculating a rank of a matrix formed by the distance information;

and dividing coordinate points where the positioning labels are located into the line-of-sight area or the non-line-of-sight area according to the rank of the matrix.

3. The method of claim 2, wherein dividing the coordinate point where the positioning tag is located into the line-of-sight region or the non-line-of-sight region according to the rank of the matrix comprises:

dividing coordinate points where the positioning labels are located into the non-line-of-sight areas under the condition that the rank of the matrix does not meet the preset condition;

determining a first coordinate of a coordinate point where the positioning tag is located under the condition that the rank of the matrix meets the preset condition;

acquiring a second coordinate of a coordinate point of the positioning label at a moment before the current moment;

determining a distance between a coordinate point corresponding to the first coordinate and a coordinate point corresponding to the second coordinate according to the first coordinate and the second coordinate;

Dividing a coordinate point corresponding to the first coordinate into the sight distance area under the condition that the distance is smaller than a preset measurement interval;

and dividing the coordinate point corresponding to the first coordinate into the non-line-of-sight region under the condition that the distance is greater than or equal to the preset measurement interval.

4. The method of claim 1, wherein determining a first fingerprint database corresponding to each coordinate point in the line-of-sight region comprises:

respectively acquiring a first distance matrix formed by the distances between each coordinate point in the line-of-sight area and the wireless communication base station;

respectively acquiring a first signal intensity matrix formed by the signal intensities of communication signals between each coordinate point in the line-of-sight area and the wireless communication base station;

respectively acquiring first line-of-sight weights of all coordinate points of the wireless communication base station in the line-of-sight area;

and storing the first distance matrix, the first signal intensity matrix and the first line-of-sight weight into the first fingerprint database.

5. The method of claim 1, wherein determining a target trajectory in the non-line-of-sight region using a pedestrian dead reckoning algorithm comprises:

Determining a first target coordinate point corresponding to the positioning label in the process that the positioning label moves from the sight distance area to the non-sight distance area, and determining the first target coordinate point as an autonomous positioning control point, wherein the first target coordinate point is the last coordinate point in a moving track of the positioning label in the sight distance area, and the sight distance weight corresponding to the first target coordinate point is higher than a preset weight;

and determining the target running track in the non-line-of-sight area by using the self-positioning control point as a starting point and using a pedestrian dead reckoning algorithm.

6. The method of claim 5, wherein determining a second fingerprint database corresponding to each coordinate point in the target trajectory comprises:

respectively acquiring a second distance matrix formed by the distances between each coordinate point in the target running track and the wireless communication base station;

respectively acquiring a second signal intensity matrix formed by the signal intensities of communication signals between each coordinate point in the target running track and the wireless communication base station;

respectively acquiring second line-of-sight weights of all coordinate points of the wireless communication base station in the target running track;

And storing the second distance matrix, the second signal intensity matrix and the second line-of-sight weight into the second fingerprint database.

7. The method of claim 2, wherein after determining the first fingerprint database corresponding to each coordinate point in the line-of-sight region, the method further comprises:

detecting whether a second target coordinate point in the viewing distance area still belongs to the viewing distance area according to a preset duration, wherein the second target coordinate point is any coordinate point in the viewing distance area;

recording a distance measurement error between the second target coordinate point and the wireless communication base station under the condition that the second target coordinate point is detected to still belong to the sight distance area;

and under the condition that the accumulated distance measurement errors are larger than the preset measurement interval, updating the average value of the accumulated distance measurement errors to a first distance matrix corresponding to the second target coordinate point.

8. The method of claim 7, wherein the method further comprises:

under the condition that the second target coordinate point is detected not to belong to the sight distance area, determining an autonomous positioning control point again;

The re-determined autonomous positioning control point is taken as a starting point, and the target running track is re-determined in the non-line-of-sight area by utilizing a pedestrian dead reckoning algorithm;

and establishing a second fingerprint database of each coordinate point in the redetermined target running track.

9. The method of claim 1, wherein after determining the second fingerprint database corresponding to each coordinate point in the target moving track, the method further comprises:

detecting whether the autonomous positioning control point corresponding to the non-line-of-sight area changes;

under the condition that the autonomous positioning control point corresponding to the non-line-of-sight area is detected to change, determining the similarity between the current signal intensity of the coordinate point which needs to be updated in the non-line-of-sight area and the signal intensity corresponding to the second fingerprint data;

and updating the data of the coordinate points to be updated according to the preset probability under the condition that whether the similarity is larger than a preset similarity threshold value or not.

10. The method according to claim 9, wherein the method further comprises:

under the condition that the autonomous positioning control point corresponding to the non-line-of-sight area is not changed, acquiring a position measurement error of the autonomous positioning control point corresponding to the non-line-of-sight area;

And under the condition that the accumulated quantity of the position measurement errors exceeds a preset threshold value, updating the data of the coordinate points to be updated according to the preset probability.

11. The method of claim 9, wherein the pre-set probability is a ratio of a current signal strength of a coordinate point in the non-line-of-sight region that needs to be updated to a corresponding signal strength in the second fingerprint data.

12. A fingerprint database generation device for indoor positioning, comprising:

the dividing module is used for dividing a target area into a sight distance area and a non-sight distance area, wherein the target area is an area for indoor positioning, signals transmitted by positioning tags in the sight distance area can be directly acquired by the wireless communication base station, and signals transmitted by the positioning tags in the non-sight distance area cannot be directly acquired by the wireless communication base station due to shielding;

the first determining module is used for determining a first fingerprint database corresponding to each coordinate point in the sight distance area, wherein fingerprint information in the first fingerprint database is used for indicating positioning information of each coordinate point included in the target area;

The second determining module is used for determining a target running track in the non-line-of-sight area by utilizing a pedestrian dead reckoning algorithm and determining a second fingerprint database corresponding to each coordinate point in the target running track;

and the processing module is used for determining the fingerprint database of each coordinate point in the target area according to the first fingerprint database and the second fingerprint database.

13. An indoor positioning apparatus, comprising: the wireless communication system comprises a wireless communication base station and a tag, wherein the tag is used for transmitting signals at any coordinate point in a target area;

the wireless communication base station, which communicates with the tag, is configured to perform the fingerprint database generation method for indoor positioning according to any one of claims 1 to 11.

14. A nonvolatile storage medium, wherein a program is stored in the nonvolatile storage medium, wherein the program, when executed, controls a device in which the nonvolatile storage medium is located to execute the fingerprint database generation method for indoor positioning according to any one of claims 1 to 11.