CN106871893A - Distributed INS/UWB tight integrations navigation system and method - Google Patents
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Abstract
The invention discloses a kind of distributed INS/UWB tight integrations navigation system and method, system includes inertial navigation device, pseudorange detection unit, wireless system for transmitting data and data handling system；Inertial navigation device, the navigation information for measuring pedestrian, navigation information includes position, speed and attitude information；Pseudorange detection unit, for obtaining pseudo-range information：Wireless system for transmitting data, it is connected with inertial navigation device and pseudorange detection unit, data for inertial navigation device and pseudorange detection unit to be gathered are delivered in data handling system by being wirelessly transferred, and the control command that data handling system sends is delivered in the inertial navigation device；The data handling system：Using Distributed Database cluster estimation unit, for carrying out data fusion estimation to the data for collecting, and control command is sent to inertial navigation device, the present invention can reduce influence of the indoor complexity navigational environment to integrated navigation precision, obtain the optimal predictor of target pedestrian's navigation information.
Description
Technical Field
The invention relates to the technical field of combined positioning in a complex environment, in particular to a distributed INS/UWB tightly-combined navigation system and method.
Background
In recent years, Pedestrian Navigation (PN) has been receiving more and more attention from various researchers as a new field to which Navigation technology is applied, and has become a research focus in this field. However, in indoor environments such as tunnels, large warehouses and underground parking lots, factors such as weak external radio signals and strong electromagnetic interference have great influence on accuracy, instantaneity and robustness of target pedestrian navigation information acquisition. How to effectively fuse the limited information acquired in the indoor environment to eliminate the influence of the indoor complex environment and ensure the continuous and stable navigation precision of the pedestrian has important scientific theoretical significance and practical application value.
Among the existing positioning methods, Global Navigation Satellite System (GNSS) is the most commonly used method. Although the GNSS can continuously and stably obtain the position information with high precision, the application range of the GNSS is limited by the defect that the GNSS is easily influenced by external environments such as electromagnetic interference and shielding, and particularly in some closed and environment-complex scenes such as indoor and underground roadways, GNSS signals are seriously shielded, and effective work cannot be performed. In recent years, uwb (ultra wideband) has shown great potential in the field of short-distance local positioning due to its high positioning accuracy in a complex environment. Researchers have proposed the use of UWB-based target tracking for pedestrian navigation in GNSS-disabled environments. Although indoor positioning can be realized by the method, because the indoor environment is complicated and changeable, UWB signals are easily interfered to cause the reduction of positioning accuracy and even the unlocking; meanwhile, because the communication technology adopted by the UWB is generally a short-distance wireless communication technology, if a large-range indoor target tracking and positioning is to be completed, a large number of network nodes are required to complete together, which inevitably introduces a series of problems such as network organization structure optimization design, multi-node multi-cluster network cooperative communication, and the like. UWB-based object tracking at the present stage therefore still faces many challenges in the field of indoor navigation.
In the aspect of navigation models, a loose integrated navigation model is widely applied in the field of indoor pedestrian integrated navigation. The model has the advantage of easy implementation, but it is noted that the implementation of the model requires multiple technologies participating in the integrated navigation to be able to perform navigation positioning independently. For example, UWB devices are required to provide navigation information of pedestrians, which requires that an environment where a target pedestrian is located must be able to acquire information of at least 3 reference nodes, which greatly reduces an application range of the integrated navigation model, and meanwhile, the sub-technologies participating in navigation perform positioning independently, introduce new errors, and are not beneficial to improving the accuracy of the integrated navigation technology. In order to overcome the problem, students propose to apply a tight combination model to the indoor pedestrian navigation field, and the tight combination model directly applies the original sensor data of the sub-technologies participating in the combined navigation to the final calculation of the navigation information, so that the risk of introducing new errors in the self-calculation of the sub-technologies is reduced, and the precision of the combined navigation is improved.
In the patent document IMU/WSN combined navigation method for indoor mobile robot, a local filter is used in a WSN pseudo-range estimation part, so that pseudo-range estimation accuracy is effectively improved, but a centralized mode is still adopted in a subsequent data fusion part, and the centralized mode makes the centralized combined navigation mode still unable to improve fault-tolerant capability, so that it is necessary to study on a related problem of how to improve fault-tolerant capability of a navigation system.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a distributed INS/UWB tightly-combined navigation system, which can reduce the influence of an indoor complex navigation environment on the combined navigation precision and obtain the optimal estimation of the navigation information of a target pedestrian.
The technical scheme adopted by the invention is as follows:
a distributed INS/UWB tightly-combined navigation system is characterized by comprising an inertial navigation device, a pseudo-range detection unit, a wireless data transmission system and a data processing system;
the inertial navigation device is used for measuring navigation information of the pedestrian, and the navigation information comprises position, speed and attitude information;
the pseudo-range detection unit is used for acquiring pseudo-range information:
the wireless data transmission system is connected with the inertial navigation device and the pseudo-range detection unit and is used for transmitting data acquired by the inertial navigation device and the pseudo-range detection unit to the data processing system through wireless transmission and transmitting a control command sent by the data processing system to the inertial navigation device;
the data processing system: and a distributed data fusion estimation unit is adopted for carrying out data fusion estimation on the acquired data and sending a control command to the inertial navigation device.
Furthermore, the distributed data fusion estimation unit comprises a sub-data fusion unit arranged in each wireless communication channel, the result of the sub-data fusion unit is sent to the main data fusion unit, and the optimal position information of the target pedestrian at the current moment is obtained according to the result of the main data fusion unit and the result measured by the inertial navigation device.
Further, the data fusion unit adopts an extended kalman filter, and performs data fusion estimation on the navigation information of the target pedestrian acquired by the inertial navigation device and the pseudo-range information acquired by the UWB positioning tag in a wireless communication channel through the extended kalman filter.
Further, the pseudo-range detection unit comprises a UWB positioning tag and a UWB reference node; the UWB reference node is placed at a set position in advance, and the inertial navigation device and the UWB positioning tag are respectively fixed on a pedestrian; and measuring the distance between the UWB positioning tag and the UWB reference node to obtain pseudo-range information.
Further, the UWB reference node can be placed at any position.
Further, the extended kalman filter performs data fusion estimation by iteration of an observation equation and a state equation.
The invention also provides a distributed INS/UWB tightly-combined navigation method, which adopts the following steps:
(1) taking an error vector of an inertial navigation device and position information of each UWB reference node as state quantities, and taking position information and pseudo-range information of a target pedestrian acquired by the inertial navigation device as system observed quantities, and constructing an INS/UWB tight combination navigation model;
(2) according to the INS/UWB tight combination navigation model, performing data fusion estimation on navigation information and pseudo range information of the target pedestrian in each wireless communication channel to obtain the current wireless communication channel and the optimal navigation information estimation of the target pedestrian at the current moment;
(3) and performing data fusion estimation on the obtained optimal estimation in each wireless communication channel to obtain the error prediction of the optimal position information of the target pedestrian, and subtracting the error prediction of the optimal position information of the target pedestrian from the position information of the target pedestrian acquired by the inertial navigation device to obtain the optimal position information of the target pedestrian at the current moment.
Further, the data fusion estimation adopts an extended kalman filter algorithm.
Further, the state quantity comprises an error vector of the inertial navigation device and east and north position information of each UWB reference node.
Further, the system observation is the difference between the square sum of the east and north position information of the target pedestrian and the square of the pseudo-range information.
Furthermore, the error vector sum of the inertial navigation device and the east and north position information of each UWB reference node have initial values, and the initial values are self-defined values.
Further, the extended kalman filtering algorithm performs data fusion estimation by iteration of an observation equation and a state equation.
Further, the state equation of the extended kalman filter in the ith wireless communication channel is:
wherein,respectively measuring speed errors in two directions, namely east direction and north direction, of a target pedestrian under a navigation coordinate system at the moment k and the moment k +1 by using the INS; position errors of the target pedestrian in the east direction and the north direction under the navigation coordinate system at the moment k and the moment k +1 are respectively; omegakFor state noise, its covariance matrix is Qith(ii) a T is the sampling period.
Further, an observation equation of the extended kalman filter in the ith wireless communication channel is:
wherein,east and north positions calculated for the inertial navigation unit IMU at the moment k;measuring a pseudo range from an unknown node to an ith reference node by the IMU at the moment k;obtaining a pseudo range from an unknown node to an ith reference node by UWB measurement at the moment k;is the coordinates of the ith reference node,to observe noise, its covariance matrix is Qith。
Further, an iterative equation of an extended kalman algorithm in the extended kalman filter in the wireless communication channel is as follows:
wherein,
further, the iterative equation of the main data fusion estimation unit is as follows:
compared with the prior art, the invention has the beneficial effects that:
(1) the invention adopts a federal filtering structure, and builds a model between pseudo-range (namely the distance between an anchor node and a target pedestrian) information acquired by a positioning label, target pedestrian navigation information calculated by an inertial device and other sensor original data and finally acquired pedestrian navigation information and navigation environment information in a wireless channel, so as to reduce the influence of indoor complex navigation environment on integrated navigation precision and lay a foundation for completing high-precision estimation of the navigation information by a local filter. And the pre-estimated navigation information of each communication channel is subjected to data fusion through a main filter so as to complete the optimal pre-estimation of the navigation information of the target pedestrian.
(2) The integrated navigation method adopts an improved INS/UWB tight integrated model, and the model uses the difference between the square sum of east and north position information of a target pedestrian obtained by INS resolving and the square of a pseudo-range obtained by UWB positioning label measurement as a system observed quantity; on the basis, data fusion is carried out on the navigation information obtained by the INS and the UWB through EKF filtering, and finally the optimal navigation information at the current moment and the prediction of the position information of the reference node are obtained. The precision and the robustness of the data fusion filter are improved.
(3) Compared with an indoor mobile robot-oriented IMU/WSN integrated navigation method in the patent document, the distributed integrated filter is adopted in the data fusion estimation part by constructing the distributed integrated navigation system, and the data fusion filter is applied to a UWB wireless communication channel, so that the fault tolerance of the system is improved.
(4) The method can be used for high-precision positioning of pedestrians in indoor environment.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a schematic diagram of a distributed INS/UWB tightly-integrated navigation system;
FIG. 2 is a diagram of a distributed INS/UWB tightly-integrated navigation method.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
EKF: an extended Kalman filter;
an IMU: an inertial navigation device;
INS: an inertial navigation system.
Example 1: as shown in fig. 1, a distributed INS/UWB tightly-integrated navigation system includes an inertial navigation device, a pseudo-range detection unit, a wireless data transmission system, and a data processing system;
the inertial navigation device is used for measuring navigation information of the pedestrian, and the navigation information comprises position, speed and attitude information; a pseudo-range detection unit for acquiring pseudo-range information: the wireless data transmission system is connected with the inertial navigation device and the pseudo-range detection unit and is used for transmitting the data acquired by the inertial navigation device and the pseudo-range detection unit to the data processing system through wireless transmission and transmitting a control command sent by the data processing system to the inertial navigation device; a data processing system: and a distributed data fusion estimation unit is adopted for carrying out data fusion estimation on the acquired data and sending a control command to the inertial navigation device.
The pseudo-range detection unit comprises a UWB positioning label and a UWB reference node; the UWB reference node is placed at a set position in advance, and the inertial navigation device and the UWB positioning tag are respectively fixed on a pedestrian; and measuring the distance between the UWB positioning tag and the UWB reference node to obtain pseudo-range information.
UWB reference nodes may also be placed at arbitrary locations.
The distributed data fusion estimation unit comprises sub-data fusion units arranged in each wireless communication channel, the result of each sub-data fusion unit is sent to the main data fusion unit, and the optimal position information of the target pedestrian at the current moment is obtained according to the result of the main data fusion unit and the result measured by the inertial navigation device.
And the data fusion unit adopts an extended Kalman filter, and carries out data fusion estimation on the navigation information of the target pedestrian acquired by the inertial navigation device and the pseudo-range information acquired by the UWB positioning tag in a wireless communication channel through the extended Kalman filter.
The extended kalman filter performs data fusion estimation by iteration of an observation equation and a state equation.
Example 2: as shown in fig. 2, a distributed INS/UWB tightly-combined navigation method adopts the following steps:
(1) taking an error vector of an inertial navigation device and position information of each UWB reference node as state quantities, and taking position information and pseudo-range information of a target pedestrian acquired by the inertial navigation device as system observed quantities, and constructing an INS/UWB tight combination navigation model;
(2) according to the INS/UWB tight combination navigation model, performing data fusion estimation on navigation information and pseudo range information of the target pedestrian in each wireless communication channel to obtain the current wireless communication channel and the optimal navigation information estimation of the target pedestrian at the current moment;
(3) and performing data fusion estimation on the obtained optimal estimation in each wireless communication channel to obtain the error prediction of the optimal position information of the target pedestrian, and subtracting the error prediction of the optimal position information of the target pedestrian from the position information of the target pedestrian acquired by the inertial navigation device to obtain the optimal position information of the target pedestrian at the current moment.
And the data fusion estimation adopts an extended Kalman filtering algorithm. The state quantity comprises an error vector of the inertial navigation device and east and north position information of each UWB reference node. The system observation is the difference between the square sum of the east and north position information of the target pedestrian and the square of the pseudo-range information.
The error vector sum of the inertial navigation device and the east and north position information of each UWB reference node have initial values, and the initial values are self-defined values. The extended Kalman filtering algorithm adopts iteration of an observation equation and a state equation to carry out data fusion estimation.
Example 3: a distributed INS/UWB tightly-integrated navigation, comprising: the system comprises an inertial navigation device IMU, a UWB positioning tag, a UWB reference node and a data processing system;
the inertial navigation device INS and the UWB positioning tag are respectively arranged on the cap of the pedestrian, the UWB reference node is arranged at any position, and the inertial navigation device INS and the UWB positioning tag are respectively connected with the data processing system.
Wherein the inertial navigation device INS: the system is used for measuring navigation information such as the position, the speed and the posture of a pedestrian;
UWB positioning tag: the device is used for measuring distance information between the UWB positioning tag and a reference node, namely pseudo-range information;
UWB reference node: the positioning device is placed at a known position in advance, so that the distance between the positioning device and the positioning label can be measured conveniently;
a data processing system: the method is used for carrying out data fusion on the acquired sensor data.
The data processing system comprises an EKF filter, and navigation information of a target pedestrian acquired by the inertial navigation device INS in a local relative coordinate system and pseudo-range information acquired by the UWB positioning tag in the local relative coordinate system are subjected to data fusion through the EKF.
Example 4: a distributed INS/UWB tightly-combined navigation method comprises the following steps:
(1) taking an error vector of an inertial navigation device INS as a state quantity, taking a difference between a square sum of east and north position information of a target pedestrian measured by the inertial navigation device INS and a square sum of a pseudo-range measured by a UWB positioning tag as a system observed quantity, and constructing a distributed INS/UWB tight combination model in a communication channel;
(2) performing data fusion on navigation information of a target pedestrian, which is acquired by an inertial navigation device INS and a UWB positioning tag in a local relative coordinate system, through an EKF filter; and the output of the EKF filter is used for obtaining the optimal navigation information of the target pedestrian and the optimal estimation of the reference node position, which are obtained based on the wireless channel at the current moment.
The state equation of the EKF filter in the ith wireless communication channel is as follows:
wherein,respectively measuring speed errors in two directions, namely east direction and north direction, of a target pedestrian under a navigation coordinate system at the moment k and the moment k +1 by using the INS; position errors of the target pedestrian in the east direction and the north direction under the navigation coordinate system at the moment k and the moment k +1 are respectively; omegakFor state noise, its covariance matrix is Qith(ii) a T is the sampling period.
The observation equation of the EKF filter in the ith wireless communication channel is as follows:
wherein,east and north positions for the inertial device IMU at time k;measuring a pseudo range from an unknown node to an ith reference node by the IMU at the moment k;obtaining a pseudo range from an unknown node to an ith reference node by UWB measurement at the moment k;is the coordinates of the ith reference node,to observe noise, its covariance matrix is Qith。
The iteration equation of the EKF algorithm in the EKF filter in the wireless communication channel is as follows:
wherein,
the iteration equation of the main filter is as follows:
the above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A distributed INS/UWB tightly-combined navigation system is characterized by comprising an inertial navigation device, a pseudo-range detection unit, a wireless data transmission system and a data processing system;
the inertial navigation device is used for measuring navigation information of the pedestrian, and the navigation information comprises position, speed and attitude information;
the pseudo-range detection unit is used for acquiring pseudo-range information:
the wireless data transmission system is connected with the inertial navigation device and the pseudo-range detection unit and is used for transmitting data acquired by the inertial navigation device and the pseudo-range detection unit to the data processing system through wireless transmission and transmitting a control command sent by the data processing system to the inertial navigation device;
the data processing system: and a distributed data fusion estimation unit is adopted for carrying out data fusion estimation on the acquired data and sending a control command to the inertial navigation device.
2. The system of claim 1, wherein: the pseudo-range detection unit comprises a UWB positioning tag and a UWB reference node; the UWB reference node is placed at a set position in advance, and the inertial navigation device and the UWB positioning tag are respectively fixed on a pedestrian; and measuring the distance between the UWB positioning tag and the UWB reference node to obtain pseudo-range information.
3. The system of claim 2, wherein: the UWB reference node may also be placed at any location.
4. The system of claim 1, wherein: the distributed data fusion estimation unit comprises sub-data fusion units arranged in each wireless communication channel, the result of each sub-data fusion unit is sent to the main data fusion unit, and the optimal position information of the target pedestrian at the current moment is obtained according to the result of the main data fusion unit and the result measured by the inertial navigation device.
5. The system of claim 1, wherein: and the data fusion unit adopts an extended Kalman filter, and carries out data fusion estimation on the navigation information of the target pedestrian acquired by the inertial navigation device and the pseudo-range information acquired by the UWB positioning tag in a wireless communication channel through the extended Kalman filter.
6. A distributed INS/UWB tightly-combined navigation method is characterized by comprising the following steps:
(1) taking an error vector of an inertial navigation device and position information of each UWB reference node as state quantities, and taking position information and pseudo-range information of a target pedestrian acquired by the inertial navigation device as system observed quantities, and constructing an INS/UWB tight combination navigation model;
(2) according to the INS/UWB tight combination navigation model, performing data fusion estimation on navigation information and pseudo range information of the target pedestrian in each wireless communication channel to obtain the current wireless communication channel and the optimal navigation information estimation of the target pedestrian at the current moment;
(3) and performing data fusion estimation on the obtained optimal estimation in each wireless communication channel to obtain the error prediction of the optimal position information of the target pedestrian, and subtracting the error prediction of the optimal position information of the target pedestrian from the position information of the target pedestrian acquired by the inertial navigation device to obtain the optimal position information of the target pedestrian at the current moment.
7. The method of claim 4, wherein the data fusion estimation employs an extended Kalman filter algorithm.
8. The method of claim 4, wherein the state quantities comprise an error vector of the inertial navigation device and east and north position information of each UWB reference node.
9. The method of claim 5, wherein the error vector of the inertial navigation device and the east and north position information of each UWB reference node have initial values, and the initial values are custom values.
10. The method of claim 4, wherein the system observation is a difference between a sum of squares of east and north position information and a square of pseudorange information of the target pedestrian.
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