[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

CN108168563B - WiFi-based large-scale shopping mall indoor positioning and navigation method - Google Patents

WiFi-based large-scale shopping mall indoor positioning and navigation method Download PDF

Info

Publication number
CN108168563B
CN108168563B CN201810128807.1A CN201810128807A CN108168563B CN 108168563 B CN108168563 B CN 108168563B CN 201810128807 A CN201810128807 A CN 201810128807A CN 108168563 B CN108168563 B CN 108168563B
Authority
CN
China
Prior art keywords
wifi
mobile terminal
label
reached
scanned
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201810128807.1A
Other languages
Chinese (zh)
Other versions
CN108168563A (en
Inventor
闫秀英
张晨
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xian University of Architecture and Technology
Original Assignee
Xian University of Architecture and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xian University of Architecture and Technology filed Critical Xian University of Architecture and Technology
Priority to CN201810128807.1A priority Critical patent/CN108168563B/en
Publication of CN108168563A publication Critical patent/CN108168563A/en
Application granted granted Critical
Publication of CN108168563B publication Critical patent/CN108168563B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20Instruments for performing navigational calculations
    • G01C21/206Instruments for performing navigational calculations specially adapted for indoor navigation
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W64/00Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Navigation (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses a WiFi-based indoor positioning and navigation method for superstores, which comprises the steps of firstly scanning AP signals of WiFi labels at the periphery through a mobile terminal with an AP signal scanner to obtain the MAC address of the scanned AP signals; sampling and measuring the strength of the scanned AP signal to obtain measurement data; calculating the positions of all the scanned Wifi labels according to the MAC address of the AP signal scanned in the step 1 and the measurement data in the step 2, and then positioning by using a positioning algorithm, wherein the specific process comprises the following steps: estimating the position of a Wifi label of the next position to be reached by the mobile terminal; predicting the position of a Wifi label of the next position to be reached by the mobile terminal; and then positioning is carried out according to the estimated position and the predicted position. According to the method, the positions of the nodes are dynamically predicted, the RPs without similar RSS vectors at the labels are filtered from the map to search the nearest neighbors, the time and the calculation complexity of the KNN algorithm are reduced, and the positioning accuracy is greatly improved.

Description

WiFi-based large-scale shopping mall indoor positioning and navigation method
Technical Field
The invention relates to the technical field of indoor positioning navigation, in particular to a WiFi-based shopping mall indoor positioning navigation method and system.
Background
With the development of the internet of things and smart cities, a large amount of information services need to be supported by position information, namely, WiFi-based large-scale shopping mall indoor positioning and navigation services. Similarly, in a large supermarket or a shopping mall, due to the fact that the field is large and the commodities are various, functions of positioning navigation, commodity searching and the like are needed to improve customer experience.
At present, documents [ Zhang Yongzhu ] indoor pedestrian positioning and tracking technology research [ D ] Qingdao [ China ocean university, 2014 ] and [ Xuwei ] indoor positioning technology research and realization [ D ] Wuhan [ China Master university, 2014 ] based on Android mobile phones and the like realize indoor positioning navigation by utilizing RSSI signals, personal navigation position guess (PDR) and particle filtering of WIFI. Since wifi tags in the environment are far from a certain location, wifi tags in certain locations may not be heard, and the RSS vector at each location may not include signals received by all AP receivers. Therefore, there may be a similar RSS vector in the vicinity position reference point rp (reference point). In the K Nearest Neighbor (KNN) algorithm, all RPs in the map only consider finding nearest neighbors and do not consider this phenomenon; on the other hand, neighbors found in the KNN algorithm may be scattered beyond the measured environment because the AP signal attenuation of each wifi tag is not only distance dependent but also affected by many indoor environmental factors, which results in the statistical minimum signal distance between the found RSS marker position vector and the individual RPs not being equal to the minimum physical distance of the actual marker position and the recorded RP position.
The study is prone to cumulative errors due to the absence of a reasonable mathematical model and effective error correction for PDR.
And because the traditional GPS positioning navigation is difficult to play the role of indoor positioning due to poor indoor signals, the indoor positioning navigation technology needs to be solved urgently.
Disclosure of Invention
Aiming at the problems of poor signal, low positioning precision, large indoor propagation signal fluctuation, unstable positioning result and the like commonly existing in the current indoor positioning, and aiming at improving the unstable factors, the invention aims to provide the WiFi-based indoor positioning and navigation method for the large-scale shopping mall.
The technical scheme adopted by the invention is as follows:
in order to solve the problems, the invention adopts the following technical scheme:
a WiFi-based shopping mall indoor positioning and navigation method comprises the following steps:
step 1, a mobile terminal with an AP signal scanner scans AP signals of wifi tags around to obtain MAC addresses of the AP signals which can be scanned;
step 2, sampling and measuring the strength of the scanned AP signal to obtain measurement data, and determining a Wifi label of the current position of the mobile terminal according to the measurement data;
step 3, calculating the positions (x) of all the scanned Wifi tags according to the MAC addresses of the AP signals scanned in the step 1 and the measurement data in the step 2i,yi) Wherein i represents a label of any scanned Wifi label, and positioning is performed by using a positioning algorithm, and the specific process is as follows:
adopting a K-weighted neighbor algorithm to utilize the positions (x) of all scanned Wifi labelsi,yi) Estimating the position of a Wifi label of the next position to be reached by the mobile terminal;
predicting the position of a Wifi label of the next position to be reached by the mobile terminal according to the position of the Wifi label of the current position of the mobile terminal and the position of the Wifi label of the destination to be reached by the mobile terminal;
if the position of the Wifi label of the next position to be reached by the mobile terminal is predicted, taking the arithmetic mean value of the position of the Wifi label of the next position to be reached by the mobile terminal and the predicted position of the Wifi label of the next position to be reached by the mobile terminal as the position of the mobile terminal in the moving process;
and if the position of the Wifi label at the next position to be reached by the mobile terminal is not predicted, taking the position of the Wifi label at the next position to be reached by the mobile terminal as the position of the mobile terminal in the moving process.
Adopting a K-weighted neighbor algorithm to utilize the positions (x) of all scanned Wifi labelsi,yi) Estimating the position (x, y) of the Wifi tag of the next position to be reached by the mobile terminal is as formula (1):
Figure BDA0001574311160000031
according to the position (x) of the Wifi label of the current position of the mobile terminalpre1,ypre1) And the position (x) of the Wifi tag of the destination to which the mobile terminal is goingpre2,ypre2) Predicting the position (x) of the Wifi tag of the next position to be reached by the mobile terminalnext,ynext) Wherein the Wifi tag of the next location is located at the location (x)next,ynext) The calculation process is as follows (2):
Figure BDA0001574311160000032
in equation (2), Δ X and Δ Y represent possible displacement amounts of the mobile terminal in the X direction and the Y direction, respectively.
The displacement amount Δ X of the mobile terminal in the X direction and the displacement amount Δ Y of the mobile terminal in the X direction are calculated by equation (3):
Figure BDA0001574311160000033
in equation (3), m is a slope of a moving path of the mobile terminal, and D is an offset of the Wifi tag in the rectangular coordinate system.
The calculation process of the slope m of the mobile terminal moving path is as follows (4):
m=(ypre2-ypre1)/(xpre2-xpre1) (4);
the calculation process of the offset D of the Wifi tag in the rectangular coordinate system is as follows (5):
D=Vt (5);
in equation (5), V is a speed at which the mobile terminal passes a distance between the Wifi tag of the previous location and the Wifi tag of the current location within time t.
The current velocity V of the mobile terminal is calculated by equation (6):
V=(Vhuman+Vpre)/2 (6);
in the formula (6), VhumanHuman walking speed, VpreIs the speed at which the mobile terminal has passed the Wifi tags corresponding to the two previous locations.
Speed V when the mobile terminal has passed through Wifi tags corresponding to two previous positionspreThe calculation process of (2) is as follows:
Figure BDA0001574311160000041
calculating the offset D of the Wifi label in the rectangular coordinate system and the speed V when the mobile terminal passes through the Wifi labels corresponding to the two previous positionspreTime t is set to 1 s.
Compared with the prior art, the invention has the following beneficial effects:
in order to obtain better accuracy, the method performs sampling measurement on the strength of the scanned AP signal; the dynamic prediction improved positioning algorithm P-KNN (predicted KNN) based on the KNN is adopted, namely the P-KNN searches nearest neighbors by filtering out the RP without similar RSS vectors at the labels from a map so as to reduce the time and the calculation complexity of the KNN algorithm, and meanwhile, the positioning accuracy is increased, and the method is particularly suitable for indoor positioning navigation of supermarkets and shopping malls, and specifically: if the ith AP Received Signal Strength (RSSi) in the RSS vector at the marker is not null, then the AP signal value received from the wifi tag is also not null in the RSS vector set for the selected RP. By using a subset of the RPs as the Filter-RPet, which subset has a greater likelihood of being selected as nearest neighbors when building the KNN algorithm, then the location of the current marker is estimated by the RSS vector at the marker and the Filter-RPet. The positioning navigation method adopts the wireless signal intensity ranging and the pedestrian navigation position conjecture, and realizes accurate positioning by utilizing an improved KNN algorithm on the basis. The positioning and navigation method has the advantages of small positioning error, no accumulated error, stable signal, accurate positioning and navigation, high commodity searching speed, great improvement on commodity searching efficiency of customers, and suitability for popularization and use in large supermarkets and sales places.
Drawings
FIG. 1 is an indoor plan view of a mall;
FIG. 2 is a graph illustrating the variation of received signal strength with distance;
FIG. 3 is a diagram illustrating finding predicted neighbor nodes according to the present invention;
FIG. 4 is a comparison graph of the drift of the positioning results obtained by the positioning algorithm of the present invention and an unmodified positioning algorithm;
FIG. 5 is an overall flowchart of the positioning method of the present invention;
FIG. 6 is a functional implementation diagram of the positioning method of the present invention on the measurement interface;
fig. 7 is a diagram of an experimental scene of the positioning method in a shopping mall.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
As shown in fig. 5, the WiFi-based indoor positioning navigation method for a large mall of the present invention is performed through the following steps:
step 1, as shown in fig. 3, requesting a mobile terminal with an AP signal scanner to scan AP signals of wifi tags around through a bottom-layer wireless network, acquiring an MAC address of the AP signal that can be scanned, and storing the MAC address of the AP signal that is scanned;
step 2, as shown in fig. 2, up to now, WiFi indoor positioning also mainly depends on the strength of the measurement signal, and the distance estimation is realized by the attenuation of the measurement signal strength or the manner of matching the received signal strength; however, the measurement variability of the signal strength is very large, which limits the accuracy of WiFi indoor positioning; in order to obtain better accuracy, sampling and measuring the strengths of the scanned AP signals at different positions to obtain measurement data, storing the data measured by the sampling points, and determining a Wifi label of the current position of the mobile terminal according to the measurement data;
step 3, calculating the positions (x) of all the scanned Wifi tags according to the MAC addresses of the AP signals scanned in the step 1 and the measurement data in the step 2i,yi) Wherein i represents a label of any scanned Wifi label, and positioning is performed by using a positioning algorithm, and the specific process is as follows:
step 3.1, as shown in fig. 4, using the K-weighted nearest neighbor algorithm (WKNN) to utilize the positions (x) of all the scanned Wifi tagsi,yi) The calculation process of estimating the position (x, y), (x, y) of the Wifi tag at the next position to be reached by the mobile terminal is as follows:
Figure BDA0001574311160000061
in the WKNN algorithm, K is averaged using equation (1) to find nearest neighbors;
using the functionality that a WiFi tag can be configured to report the received signal strength from the AP within a predetermined time period, the location of the tag can be predicted from the previous characteristics by deriving a smooth location curve from the received signal sequence, step 3.2. Therefore, if the distance between the position at which the WKNN is estimated and the previously predicted tag position exceeds the maximum possible offset, max _ displacement, then the estimation of WKNN is unreliable and such unexpected sudden changes or scattering can be reduced by prediction. Using the historical information of the tag position, and according to the position (x) of the Wifi tag of the current position of the mobile terminalpre1,ypre1) And the position (x) of the Wifi tag of the destination to which the mobile terminal is goingpre2,ypre2) Predicting the position (x) of the Wifi tag of the next position to be reached by the mobile terminalnext,ynext) Wherein the Wifi tag of the next location is located at the location (x)next,ynext) The calculation process is as follows (2):
Figure BDA0001574311160000062
in the formula (2), Δ X and Δ Y represent possible displacement amounts of the mobile terminal in the X direction and the Y direction, respectively;
the displacement amount Δ X of the mobile terminal in the X direction and the displacement amount Δ Y of the mobile terminal in the X direction are calculated by equation (3):
Figure BDA0001574311160000063
in the formula (3), m is the slope of the moving path of the mobile terminal, and D is the offset of the Wifi tag in the rectangular coordinate system;
the calculation process of the slope m of the mobile terminal moving path is as follows (4):
m=(ypre2-ypre1)/(xpre2-xpre1) (4);
the calculation process of the offset D of the Wifi tag in the rectangular coordinate system is as follows (5):
D=Vt (5);
in the formula (5), V is a speed at which the mobile terminal passes a distance between the Wifi tag at the previous location and the Wifi tag at the current location within time t;
the current velocity V of the mobile terminal is calculated by equation (6):
V=(Vhuman+Vpre)/2 (6);
in the formula (6), VhumanHuman walking speed, VpreIs the speed at which the mobile terminal has passed the Wifi tags corresponding to the two previous locations.
Speed V when the mobile terminal has passed through Wifi tags corresponding to two previous positionspreThe calculation process of (2) is as follows:
Figure BDA0001574311160000071
when calculating the offset D of the Wifi tag in the rectangular coordinate system and the Wifi tags corresponding to the two previous positions passed by the mobile terminalVelocity VpreWhen the time t is set to 1s, the possible position of the scanned label is at the next position (x) due to the influence of noise in the indoor environmentnext,ynext) And previous position (x)pre2,ypre2) Somewhere in between, the next location must be modified as the predicted location. Thus, as shown in fig. 3, some predicted neighbor RPs are found in the intersection space of two circles centered at the previous and next tag positions, respectively, and at a radius of D.
Step 3.3, if the position (x, y) of the Wifi label at the next position to be reached by the mobile terminal is predicted, estimating the position (x, y) of the Wifi label at the next position to be reached by the mobile terminal and the position (x, y) of the Wifi label at the predicted next position to be reached by the mobile terminalnext,ynext) The arithmetic mean value of the mobile terminal is used as the position of the mobile terminal in the moving process;
if the next position (x) to be reached by the mobile terminal is not predictednext,ynext) And if the position of the Wifi tag is not calculated, the position (x, y) of the Wifi tag at the next position to be reached by the mobile terminal is used as the position of the mobile terminal in the moving process.
As shown in fig. 1, 6 and 7, the point with the aperture in the figure is the positioning result point, and the other points are the sample points acquired during the measurement.
The invention is analyzed from a quantitative angle, only the comparison between the test coordinate and the actual coordinate position is needed, and the following groups of experiments are carried out, wherein the table 1 shows the comparison between the collected position and the actual coordinate:
TABLE 1
Number of times/coordinate Test coordinates (x, y)
1 348.2769,1546.5085
2 348.2883,1546.5000
3 348.2439,1546.8516
4 348.7743,1544.9774
5 336.8340,1450.7119
6 343.7672,1522.8147
7 349.2508,1547.0031
8 349.2195,1547.1186
9 349.2417,1547.2193
10 349.0228,1547.8543
Actual position 350.1600
As can be seen from the test results of table 1: the positioning and navigation method has the advantages of small positioning error, no accumulated error, stable signal, accurate positioning and navigation, high commodity searching speed, great improvement of commodity searching efficiency of customers, and suitability for popularization and use in large supermarkets and sales places.

Claims (1)

1. A WiFi-based shopping mall indoor positioning and navigation method is characterized by comprising the following steps:
step 1, a mobile terminal with an AP signal scanner scans AP signals of wifi tags around to obtain MAC addresses of the AP signals which can be scanned;
step 2, sampling and measuring the strength of the scanned AP signal to obtain measurement data, and determining a Wifi label of the current position of the mobile terminal according to the measurement data;
step 3, calculating the positions (x) of all the scanned Wifi tags according to the MAC addresses of the AP signals scanned in the step 1 and the measurement data in the step 2i,yi) Wherein i represents a label of any scanned Wifi label, and positioning is performed by using a positioning algorithm, and the specific process is as follows:
adopting a K-weighted neighbor algorithm to utilize the positions (x) of all scanned Wifi labelsi,yi) Estimating the position of a Wifi label of the next position to be reached by the mobile terminal;
predicting the position of a Wifi label of the next position to be reached by the mobile terminal according to the position of the Wifi label of the current position of the mobile terminal and the position of the Wifi label of the destination to be reached by the mobile terminal;
if the position of the Wifi label of the next position to be reached by the mobile terminal is predicted, taking the arithmetic mean value of the position of the Wifi label of the next position to be reached by the mobile terminal and the predicted position of the Wifi label of the next position to be reached by the mobile terminal as the position of the mobile terminal in the moving process;
if the position of the Wifi label at the next position to be reached by the mobile terminal is not predicted, taking the position where the Wifi label at the next position to be reached by the mobile terminal is estimated as the position of the mobile terminal in the moving process;
adopting a K-weighted neighbor algorithm to utilize the positions (x) of all scanned Wifi labelsi,yi) Estimating the position (x, y) of the Wifi tag of the next position to be reached by the mobile terminal is as formula (1):
Figure FDA0003062737020000011
according to the position (x) of the Wifi label of the current position of the mobile terminalpre1,ypre1) And the position (x) of the Wifi tag of the destination to which the mobile terminal is goingpre2,ypre2) Predicting the position (x) of the Wifi tag of the next position to be reached by the mobile terminalnext,ynext) Wherein, the position (x) of the Wifi label of the next positionnext,ynext) The calculation process is as follows (2):
Figure FDA0003062737020000021
in the formula (2), Δ X and Δ Y represent possible displacement amounts of the mobile terminal in the X direction and the Y direction, respectively;
the displacement amount Δ X of the mobile terminal in the X direction and the displacement amount Δ Y of the mobile terminal in the Y direction are calculated by equation (3):
Figure FDA0003062737020000022
in the formula (3), m is the slope of the moving path of the mobile terminal, and D is the offset of the Wifi tag in the rectangular coordinate system;
the calculation process of the slope m of the mobile terminal moving path is as follows (4):
m=(ypre2-ypre1)/(xpre2-xpre1) (4);
the calculation process of the offset D of the Wifi tag in the rectangular coordinate system is as follows (5):
D=Vt (5);
in the formula (5), V is a speed at which the mobile terminal passes a distance between the Wifi tag at the previous location and the Wifi tag at the current location within time t;
the current velocity V of the mobile terminal is calculated by equation (6):
V=(Vhuman+Vpre)/2 (6);
in the formula (6), VhumanHuman walking speed, VpreThe speed is the speed when the mobile terminal passes through the Wifi tags corresponding to the two previous positions;
speed V when the mobile terminal has passed through Wifi tags corresponding to two previous positionspreThe calculation process of (2) is as follows:
Figure FDA0003062737020000023
calculating the offset D of the Wifi label in the rectangular coordinate system and the speed V when the mobile terminal passes through the Wifi labels corresponding to the two previous positionspreTime t is set to 1 s.
CN201810128807.1A 2018-02-08 2018-02-08 WiFi-based large-scale shopping mall indoor positioning and navigation method Active CN108168563B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810128807.1A CN108168563B (en) 2018-02-08 2018-02-08 WiFi-based large-scale shopping mall indoor positioning and navigation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810128807.1A CN108168563B (en) 2018-02-08 2018-02-08 WiFi-based large-scale shopping mall indoor positioning and navigation method

Publications (2)

Publication Number Publication Date
CN108168563A CN108168563A (en) 2018-06-15
CN108168563B true CN108168563B (en) 2021-06-29

Family

ID=62513696

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810128807.1A Active CN108168563B (en) 2018-02-08 2018-02-08 WiFi-based large-scale shopping mall indoor positioning and navigation method

Country Status (1)

Country Link
CN (1) CN108168563B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111198365A (en) * 2020-01-16 2020-05-26 东方红卫星移动通信有限公司 Indoor positioning method based on radio frequency signal
CN114333410B (en) * 2021-12-30 2023-03-28 中国联合网络通信集团有限公司 Vehicle searching method, device, terminal, server and storage medium

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101505532A (en) * 2009-03-12 2009-08-12 华南理工大学 Wireless sensor network target tracking method based on distributed processing
CN103148855A (en) * 2013-02-27 2013-06-12 东南大学 INS (inertial navigation system)-assisted wireless indoor mobile robot positioning method
KR101354944B1 (en) * 2012-08-07 2014-01-24 한국과학기술원 Methods for prediction and estimation of location of mobile devices using way network
CN104507159A (en) * 2014-11-24 2015-04-08 北京航空航天大学 A method for hybrid indoor positioning based on WiFi (Wireless Fidelity) received signal strength
CN104602342A (en) * 2015-01-13 2015-05-06 浙江大学 IBeacon device based efficient indoor positioning method
CN106483495A (en) * 2016-09-09 2017-03-08 电子科技大学 A kind of indoor sport tag location and speed-measuring method
CN106500696A (en) * 2016-09-14 2017-03-15 中南大学 A kind of method for realizing robot on-vehicle navigation apparatus Measured Coordinates correction with predicting
CN106851539A (en) * 2017-02-03 2017-06-13 上海雅丰信息科技有限公司 Fusion indoor orientation method based on bluetooth and inertial navigation
CN107071901A (en) * 2017-05-10 2017-08-18 桂林电子科技大学 A kind of WIFI indoor orientation method the ratio between dual based on received signal strength
CN107517446A (en) * 2017-09-28 2017-12-26 知谷(上海)网络科技有限公司 Indoor orientation method and device based on Wi Fi focuses

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6944465B2 (en) * 1998-09-22 2005-09-13 Polaris Wireless, Inc. Estimating the location of a mobile unit based on the elimination of improbable locations
US8203487B2 (en) * 2009-08-03 2012-06-19 Xsens Holding, B.V. Tightly coupled UWB/IMU pose estimation system and method
US20100321243A1 (en) * 2009-06-18 2010-12-23 Qualcomm Incorporated Background position fix prior to request
US8441398B2 (en) * 2010-02-03 2013-05-14 Texas Instruments Incorporated Receivers, circuits, and methods to improve GNSS time-to-fix and other performances
CN105627529B (en) * 2016-03-31 2018-07-31 西安建筑科技大学 Air-conditioner control system and method based on PID controller with changing integration rate type Iterative Algorithm
CN107613466B (en) * 2017-09-15 2020-07-03 西安电子科技大学 Indoor positioning method based on fingerprint similarity under ultra-dense wireless network

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101505532A (en) * 2009-03-12 2009-08-12 华南理工大学 Wireless sensor network target tracking method based on distributed processing
KR101354944B1 (en) * 2012-08-07 2014-01-24 한국과학기술원 Methods for prediction and estimation of location of mobile devices using way network
CN103148855A (en) * 2013-02-27 2013-06-12 东南大学 INS (inertial navigation system)-assisted wireless indoor mobile robot positioning method
CN104507159A (en) * 2014-11-24 2015-04-08 北京航空航天大学 A method for hybrid indoor positioning based on WiFi (Wireless Fidelity) received signal strength
CN104602342A (en) * 2015-01-13 2015-05-06 浙江大学 IBeacon device based efficient indoor positioning method
CN106483495A (en) * 2016-09-09 2017-03-08 电子科技大学 A kind of indoor sport tag location and speed-measuring method
CN106500696A (en) * 2016-09-14 2017-03-15 中南大学 A kind of method for realizing robot on-vehicle navigation apparatus Measured Coordinates correction with predicting
CN106851539A (en) * 2017-02-03 2017-06-13 上海雅丰信息科技有限公司 Fusion indoor orientation method based on bluetooth and inertial navigation
CN107071901A (en) * 2017-05-10 2017-08-18 桂林电子科技大学 A kind of WIFI indoor orientation method the ratio between dual based on received signal strength
CN107517446A (en) * 2017-09-28 2017-12-26 知谷(上海)网络科技有限公司 Indoor orientation method and device based on Wi Fi focuses

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
"Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization";Zhenghua Chen等;《Sensors》;20150131;第15卷(第1期);715-732 *
"Using Wi-Fi/magnetometers for indoor location and personal navigation";Yuqi Li等;《 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN)》;20151207;全文 *
"WiFi/RFID室内融合定位方法的研究";纪敏;《•中国优秀硕士学位论文全文数据库 信息科技辑》;20170215(第2期);I136-1511 *
"基于WiFi位置指纹的室内定位算法的研究与优化";毛勤;《中国优秀硕士学位论文全文数据库 信息科技辑》;20161015(第10期);I136-100 *
"基于气候补偿的变风量空调系统节能控制";闫秀英等;《控制工程》;20171031;第24卷(第10期);2077-2081 *

Also Published As

Publication number Publication date
CN108168563A (en) 2018-06-15

Similar Documents

Publication Publication Date Title
CN103402258B (en) Wi-Fi (Wireless Fidelity)-based indoor positioning system and method
KR101457279B1 (en) Concurrent wireless transmitter mapping and mobile station positioning
CN108534779B (en) Indoor positioning map construction method based on track correction and fingerprint improvement
CN109275095B (en) Bluetooth-based indoor positioning system, positioning equipment and positioning method
Tiglao et al. Smartphone-based indoor localization techniques: State-of-the-art and classification
CN112230243B (en) Indoor map construction method for mobile robot
CN109413578B (en) Indoor positioning method based on fusion of WIFI and PDR
CN103916821A (en) Floor distinguishing method based on RSSI difference between floors
WO2019062734A1 (en) Indoor positioning method and device based on wi-fi hot spots
US10567918B2 (en) Radio-location method for locating a target device contained within a region of space
CN101620270A (en) Wireless location method based on cluster-fusion
Xuanmin et al. An improved dynamic prediction fingerprint localization algorithm based on KNN
CN104144495B (en) A kind of fingerprint positioning method based on direction sensor and wlan network
Altintas et al. Indoor location detection with a RSS-based short term memory technique (KNN-STM)
Dashti et al. Rssi localization with gaussian processes and tracking
CN108168563B (en) WiFi-based large-scale shopping mall indoor positioning and navigation method
Dümbgen et al. Multi-modal probabilistic indoor localization on a smartphone
Abdullah et al. Machine learning algorithm for wireless indoor localization
CN101977436A (en) WLAN indoor positioning-based mobile subscriber position coordinate correction method
Chen et al. Path distance-based map matching for Wi-Fi fingerprinting positioning
Zhuang et al. Autonomous WLAN heading and position for smartphones
Ho et al. Real-time indoor positioning system based on RFID Heron-bilateration location estimation and IMU inertial-navigation location estimation
Yuan et al. Efficient WiFi fingerprint training using semi-supervised learning
CN111836188A (en) Online cooperative positioning and system based on Wi-Fi RSS
Kawauchi et al. FineMesh: High-Density Sampling Platform Using an Autonomous Robot

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant