CN110062334A - WLAN indoor position accuracy based on user behavior characteristics limits estimation method - Google Patents

WLAN indoor position accuracy based on user behavior characteristics limits estimation method Download PDF

Info

Publication number
CN110062334A
CN110062334A CN201910304248.XA CN201910304248A CN110062334A CN 110062334 A CN110062334 A CN 110062334A CN 201910304248 A CN201910304248 A CN 201910304248A CN 110062334 A CN110062334 A CN 110062334A
Authority
CN
China
Prior art keywords
user
wlan
area
comentropy
target
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.)
Pending
Application number
CN201910304248.XA
Other languages
Chinese (zh)
Inventor
王烟濛
周牧
高罗莹
田增山
张小娅
袁慧
李垚鲆
耿小龙
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.)
Chongqing University of Post and Telecommunications
Original Assignee
Chongqing University of Post and Telecommunications
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 Chongqing University of Post and Telecommunications filed Critical Chongqing University of Post and Telecommunications
Priority to CN201910304248.XA priority Critical patent/CN110062334A/en
Publication of CN110062334A publication Critical patent/CN110062334A/en
Pending legal-status Critical Current

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H04W16/225—Traffic simulation tools or models for indoor or short range network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
    • H04W84/10—Small scale networks; Flat hierarchical networks
    • H04W84/12—WLAN [Wireless Local Area Networks]

Abstract

The invention discloses a kind of, and the WLAN indoor position accuracy based on user behavior characteristics limits estimation method, analyzes first the user movement trend in target environment；Secondly, emulating to the user movement path in target localization region, the relationship between user behavior characteristics and user's affiliated area comentropy is constructed；It again, is the information communication process in colored Gaussian noise channels by WLAN indoor positioning process simulation；Finally, deriving that WLAN indoor position accuracy limits using the constraint relationship between the channel capacity of simulation system and the comentropy of user's affiliated area.The present invention solves the problems, such as stability difference existing for traditional WLAN positioning accuracy limit appraisal procedure and application range limitation.

Description

WLAN indoor position accuracy based on user behavior characteristics limits estimation method
Technical field
The invention belongs to indoor positioning technologies, and in particular to a kind of WLAN indoor position accuracy based on user behavior characteristics Limit estimation method.
Background technique
With the development of wireless communication technique, demand of the people to wireless location is growing day by day.In outdoor environment, the whole world is fixed Extensive utilization has been obtained in the outdoor positionings systems such as position system (Global Positioning System, GPS).But Interior, due to indoor environment is complicated and changeable, multipath effect is obvious etc., the performance difference strong man of the outdoor positionings system such as GPS Meaning.In recent years, it with the universal and application of WLAN (Wireless Local Area Network, WLAN), utilizes Existing WLAN infrastructure carries out positioning and gets more and more people's extensive concerning.At the same time, WLAN indoor position accuracy limit is to fixed The deployment and optimization of position network have stronger Engineering Guidance effect, therefore the concern by it is enough scholar.
Currently, being based on carat Metro lower limit (Cramer Rao to most of WLAN indoor position accuracies limit estimation method Lower Bound, CRLB) method development.Believe by using wireless access point each at reference point (Access Point, AP) Number Joint Distribution probability construct Fisher's information matrix (Fisher Information Matrix, FIM), derive WLAN The CRLB of position error.But such method not only needs to construct FIM using signal propagation model, but also needs to know AP The prior information set.When signal propagation model or the position AP inaccuracy, this method will lead to biggish evaluated error.In addition, This method can not work in the case where single AP.In order to solve problem above, it is necessary to develop a kind of new based on user behavior The WLAN indoor position accuracy of feature limits estimation method.
Summary of the invention
The object of the present invention is to provide a kind of, and the WLAN indoor position accuracy based on user behavior characteristics limits estimation method, it It can solve stability difference present in traditional WLAN positioning accuracy limit appraisal procedure and application range limitation problem.
WLAN indoor position accuracy of the present invention based on user behavior characteristics limits estimation method, comprising the following steps:
Step 1: the movement tendency to user in target environment at each time end is analyzed；
Step 2: emulation generates N in target localization regiontraceUser movement path；
Step 3: target localization region is divided into M sub-regions, wherein each subregionArea be 4d2 (=2d × 2d), d is the half of subregion side length, and S is the area of target environment, M=S/4d2；
Step 4: calculating user's affiliated area comentropyWith the relationship between sub-district domain sizes d；Specifically include with Lower step:
When step 4 (one), group area size are d, it is based on NtraceThe simulation result in user movement path, counts All motion paths pass through each subregionQuantity
Step 4 (two) calculates target and is located at subregionProbability
Step 4 (three) calculates user's affiliated areaComentropy
Step 4 (four), repeating said steps four (one) arrive the step 4 (three), calculate in different sub-district domain sizes d Lower affiliated area comentropyValue；
Step 4 (five), to allLogistic fit is carried out with the value of d, obtains user's affiliated area comentropyWith the relational expression between sub-district domain sizes dWherein, α and β is respectively coefficient and cuts Away from.
Step 5: indoor WLAN position fixing process to be modeled as to the information communication process of communication system, position error limit is obtainedWith institute's analog channel capacityBetween relational expression
Step 6: in reference point wiThe RSS data RSS from k AP is acquired on (i=1 ..., n)i；
Step 7: calculating the practical covariance matrix K for receiving RSS signalY；
Step 8: calculating the covariance matrix K of RSS signal noiseZ；
Step 9: portraying the channel of institute's analog communication system for colored Gaussian noise channels, calculating channel capacity
Step 10: by channel capacity in the step 9Result substitute into the relational expression of the step 5, obtain mesh Mark the positioning accuracy limit of environment
The invention has the following advantages that the present invention first analyzes the user movement trend in target environment；Secondly, User movement path in target localization region is emulated, building user behavior characteristics and user's affiliated area comentropy Between relationship；It again, is the information communication process in colored Gaussian noise channels by WLAN indoor positioning process simulation；Most Afterwards, it using the constraint relationship between the channel capacity of simulation system and the comentropy of user's affiliated area, derives in the room WLAN Positioning accuracy limit.Compared to traditional location fingerprint positioning method, stability of the invention is strong, accuracy is high, and application range Extensively.The present invention can apply to radio circuit environment, be mainly directed towards indoor WLAN localization method, solve tradition WLAN positioning accuracy limits stability difference existing for appraisal procedure and application range limits to problem.
Detailed description of the invention
Fig. 1 be the present invention in step 1 to step 10 flow chart；
Fig. 2 is the indoor WLAN position fixing process under colored Gaussian noise channels；
Fig. 3 is colored Gaussian noise channels model；
Fig. 4 is 600 user movement path simulation results；
Fig. 5 is the position area information entropy of different sub-district domain sizes.
Specific embodiment
The present invention will be further explained below with reference to the attached drawings.
WLAN indoor position accuracy based on user behavior characteristics as shown in Figure 1 limits estimation method, comprising the following steps:
Step 1: the movement tendency to user in target environment at each time end is analyzed；
Step 2: emulation generates N in target localization regiontraceUser movement path；
Step 3: target localization region is divided into M sub-regions, wherein each subregionArea be 4d2(= 2d × 2d), d is the half of subregion side length, and S is the area of target environment, M=S/4d2；
Step 4: calculating user's affiliated area comentropyWith the relationship between sub-district domain sizes d；Specifically include with Lower step:
When step 4 (one), group area size are d, it is based on NtraceThe simulation result in user movement path, counts All motion paths pass through each subregionQuantity
Step 4 (two) calculates target and is located at subregionProbability
Step 4 (three) calculates user's affiliated areaComentropy
Step 4 (four), repeating said steps four (one) arrive the step 4 (three), calculate in different sub-district domain sizes d Lower affiliated area comentropyValue；
Step 4 (five), to allLogistic fit is carried out with the value of d, obtains user's affiliated area comentropyWith the relational expression between sub-district domain sizes dWherein, α and β is respectively coefficient and cuts Away from.
Step 5: indoor WLAN position fixing process to be modeled as to the information communication process of communication system, position error limit is obtainedWith institute's analog channel capacityBetween relational expression:
Step 6: in reference point wiThe RSS data RSS from k AP is acquired on (i=1 ..., n)i:
Wherein, n is number of sampling points, and k is AP quantity, and N is number of samples.
Step 7: calculating the practical covariance matrix K for receiving RSS signalY:
Wherein,(j=1 ..., k), E [] are mean operation.
Step 8: calculating the covariance matrix K of RSS signal noiseZ:
Wherein,AndFor
Step 9: portraying the channel of institute's analog communication system for colored Gaussian noise channels, calculating channel capacity
Step 10: by channel capacity in the step 9Result substitute into the relational expression of the step 5, obtain mesh Mark the positioning accuracy limit of environment
As shown in Fig. 2, for the indoor WLAN position fixing process under colored Gaussian noise channels, it is assumed that the actual position of target is wi, then its position affiliated areaIt can be received coded by signal amplitude (i.e. input codeword sequence) by ideal without making an uproar.By coloured silk After the transmission of color Gaussian noise channels, output codons sequence is used to estimate the affiliated area of target.
As shown in figure 3, for colored Gaussian noise channels model, every sub-channels corresponding an AP, position wiLocate different AP Desired received signal amplitude between, between noise amplitude be all relevant.At the same time, ideal in each sub-channels Signal amplitude is received to be independent from each other with noise amplitude.
As shown in figure 4, representing what user occurred in the position as a result, color is deeper for 600 user movement path simulations Frequency is higher.
As shown in figure 5, being the position area information entropy of different sub-district domain sizes, by the next to different sub-district domain sizes d Set area information entropyIntermediate value carry out logarithmic curve-fitting, obtainRelational expression with d is The corresponding coefficient of determination R of the curve being fitted2=0.969, this indicates that the matched curve can Comentropy is portrayed wellWith the variation tendency of sub-district domain sizes d[49]。

Claims (2)

1. the WLAN indoor position accuracy based on user behavior characteristics limits estimation method, which comprises the following steps:
Step 1: the movement tendency to user in target environment at each time end is analyzed；
Step 2: emulation generates N in target localization regiontraceUser movement path；
Step 3: target localization region is divided into M sub-regions, wherein each subregionArea be 4d2(=2d × 2d), d is the half of subregion side length, and S is the area of target environment, M=S/4d2；
Step 4: calculating user's affiliated area comentropyWith the relationship between sub-district domain sizes d；
Step 5: indoor WLAN position fixing process to be modeled as to the information communication process of communication system, position error limit is obtainedWith Institute's analog channel capacityBetween relational expression
Step 6: in reference point wiThe RSS data RSS from k AP is acquired on (i=1 ..., n)i；
Step 7: calculating the practical covariance matrix K for receiving RSS signalY；
Step 8: calculating the covariance matrix K of RSS signal noiseZ；
Step 9: portraying the channel of institute's analog communication system for colored Gaussian noise channels, calculating channel capacity
Step 10: by channel capacity in the step 9Result substitute into the relational expression of the step 5, obtain target ring The positioning accuracy in border limits
2. the WLAN indoor position accuracy according to claim 1 based on user behavior characteristics limits estimation method, feature Be, the step 4 the following steps are included:
When step 4 (one), group area size are d, it is based on NtraceThe simulation result in user movement path, counts all Motion path passes through each subregionQuantity
Step 4 (two) calculates target and is located at subregionProbability
Step 4 (three) calculates user's affiliated areaComentropy
Step 4 (four), repeating said steps four (one) arrive the step 4 (three), calculate affiliated at different sub-district domain sizes d Area information entropyValue；
Step 4 (five), to allLogistic fit is carried out with the value of d, obtains user's affiliated area comentropyWith Relational expression between sub-district domain sizes dWherein, α and β is respectively coefficient and intercept.
CN201910304248.XA 2019-04-16 2019-04-16 WLAN indoor position accuracy based on user behavior characteristics limits estimation method Pending CN110062334A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910304248.XA CN110062334A (en) 2019-04-16 2019-04-16 WLAN indoor position accuracy based on user behavior characteristics limits estimation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910304248.XA CN110062334A (en) 2019-04-16 2019-04-16 WLAN indoor position accuracy based on user behavior characteristics limits estimation method

Publications (1)

Publication Number Publication Date
CN110062334A true CN110062334A (en) 2019-07-26

Family

ID=67317784

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910304248.XA Pending CN110062334A (en) 2019-04-16 2019-04-16 WLAN indoor position accuracy based on user behavior characteristics limits estimation method

Country Status (1)

Country Link
CN (1) CN110062334A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111866709A (en) * 2020-06-29 2020-10-30 重庆邮电大学 Indoor Wi-Fi positioning error bound estimation method for moving target

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102508197A (en) * 2011-09-29 2012-06-20 哈尔滨工程大学 Passive target positioning method based on channel capacity
US20160048710A1 (en) * 2013-07-12 2016-02-18 Lawrence Livermore National Security, Llc Long-range uwb remote powering capability at fcc regulated limit using multiple antennas

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102508197A (en) * 2011-09-29 2012-06-20 哈尔滨工程大学 Passive target positioning method based on channel capacity
US20160048710A1 (en) * 2013-07-12 2016-02-18 Lawrence Livermore National Security, Llc Long-range uwb remote powering capability at fcc regulated limit using multiple antennas

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
周牧，张巧，邱枫: "基于物理邻近点辅助的无线局域网指纹定位方法", 《计算机应用》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111866709A (en) * 2020-06-29 2020-10-30 重庆邮电大学 Indoor Wi-Fi positioning error bound estimation method for moving target
CN111866709B (en) * 2020-06-29 2022-05-17 重庆邮电大学 Indoor Wi-Fi positioning error bound estimation method for moving target

Similar Documents

Publication Publication Date Title
CN106604228B (en) A kind of fingerprint positioning method based on LTE signaling data
CN108696932A (en) It is a kind of using CSI multipaths and the outdoor fingerprint positioning method of machine learning
CN105911518A (en) Robot positioning method
CN106793060A (en) A kind of UWB indoor localization method
CN103297924B (en) Based on novel location fingerprint method for designing and the indoor locating system of channel estimating
CN102928860B (en) Method for improving GPS (Global Positioning System) positioning precision on the basis of local positioning information
CN103428629B (en) Mixed positioning realization method and system
CN103220777A (en) Mobile device positioning system
CN107743296A (en) A kind of RSSI area segmentation formula localization methods based on compressed sensing
CN102984745A (en) Combined estimation method for Wi-Fi AP (wireless fidelity access point) position and path loss model
CN109041218B (en) Method for predicting user position and intelligent hardware
CN109743701A (en) Indoor 3-D positioning method based on ultra-wideband communications
CN107179525A (en) A kind of location fingerprint construction method of the Kriging regression based on Thiessen polygon
CN105911521A (en) Over-the-horizon target direct locating method through combining radio signal complex envelop and carrier phase information
CN109581281A (en) Moving objects location method based on reaching time-difference and arrival rate difference
CN102395195A (en) Method for raising indoor positioning precision under non-line-of-sight environment
CN111866709B (en) Indoor Wi-Fi positioning error bound estimation method for moving target
CN106792516B (en) 3-D positioning method based on radio communication base station
CN110247719A (en) The playback of 5G time varying channel and emulation mode based on machine learning
CN109428657A (en) A kind of localization method and device
CN105099588A (en) Device and method for simulating interference propagation channel of aviation communication system
CN109495150A (en) A kind of millimetre-wave attenuator codebook design method
CN110062334A (en) WLAN indoor position accuracy based on user behavior characteristics limits estimation method
CN103096465A (en) Environment self-adaption multi-target direct locating method
CN114353795A (en) Indoor three-dimensional positioning system and method based on UWB equipment

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
WD01 Invention patent application deemed withdrawn after publication
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20190726