CN108810823A - For the collaboration update information screening technique of wireless sensor network co-positioned - Google Patents
For the collaboration update information screening technique of wireless sensor network co-positioned Download PDFInfo
- Publication number
- CN108810823A CN108810823A CN201810765729.6A CN201810765729A CN108810823A CN 108810823 A CN108810823 A CN 108810823A CN 201810765729 A CN201810765729 A CN 201810765729A CN 108810823 A CN108810823 A CN 108810823A
- Authority
- CN
- China
- Prior art keywords
- information
- node
- nodes
- collaborative
- positioning
- 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
Links
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000012216 screening Methods 0.000 title claims abstract description 17
- 238000009826 distribution Methods 0.000 claims abstract description 16
- 238000005315 distribution function Methods 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000005259 measurement Methods 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 claims description 2
- 238000001914 filtration Methods 0.000 claims 1
- 230000001186 cumulative effect Effects 0.000 description 2
- 230000008030 elimination Effects 0.000 description 2
- 238000003379 elimination reaction Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 230000035939 shock Effects 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Classifications
-
- 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
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Traffic Control Systems (AREA)
- Navigation (AREA)
Abstract
The invention discloses a kind of collaboration update information screening techniques for wireless sensor network co-positioned, belong to positioning and navigation field.This approach includes the following steps：(1) position probability distribution of all nodes and anchor point is initialized；(2) all nodes predict the distribution function of self-position according to the motion state of itself；(3) each node is connected by network with other nodes, exchanging orientation data between different nodes；(4) adjacent node carries out ranging, the collaboration update information between calculate node by radio, vision means；(5) contribution degree for calculating all cooperative informations carries out priority ordered to collaboration update information according to the size of contribution degree, excludes low contribution degree information；(6) edge posterior probability of the calculate node i in t moment；(7) it returns to step (4) and is iterated calculating, until location informationConvergence.The present invention has higher real-time when interstitial content is more larger with location estimation range in a network.
Description
Technical Field
The invention discloses a collaborative correction information screening method for collaborative positioning of a wireless sensor network, and belongs to the technical field of positioning and navigation.
Background
In a wireless sensor network, position information is used as important information of network service, and has an important position in application fields of emergency rescue, disaster relief, traffic management, indoor navigation and the like. The wireless sensor network cooperative positioning technology is one of mainstream means for providing continuous and reliable position information, can break through the limitation of the non-cooperative positioning technology, and can achieve the aim of improving the positioning accuracy of the wireless sensor network cooperative positioning technology by transmitting cooperative correction information among a plurality of user nodes even if part of the user nodes are outside the working range of a positioning base station.
The belief propagation algorithm is one of the mainstream algorithms in network cooperative positioning, and the algorithm determines the position coordinates of each user node by solving the edge posterior probability density of the user node. However, since the algorithm uses a particle approximation method to solve the distribution function, a large amount of calculation and a high algorithm execution complexity are generated when the number of user nodes in the network is large or the coordinate estimation range is large.
By utilizing a collaborative correction information screening mechanism, the calculated amount and the complexity of the belief propagation algorithm can be greatly reduced by excluding collaborative information with low contribution to the solution of the distribution function, and the practicability of the belief propagation algorithm in the field of collaborative positioning of the wireless sensor network is improved.
Disclosure of Invention
The collaborative correction information screening method for the wireless sensor network collaborative positioning overcomes the defects of huge calculation amount and execution complexity of a belief propagation algorithm, provides a collaborative correction information screening method for the collaborative positioning of the wireless sensor network by utilizing the characteristics of Fisher information and relative entropy of the collaborative correction information, and carries out priority ranking on the collaborative correction information by calculating the contribution degree of the collaborative correction information of all the inflow nodes, thereby realizing the elimination of low contribution information and realizing the balance of calculation amount and precision.
The invention adopts the following technical scheme for solving the technical problems:
a collaborative correction information screening method aiming at collaborative positioning of a wireless sensor network comprises the following steps:
(1) initializing location probability distributions for all nodes and anchorsWherein the probability distribution of the anchor points is an impulse function;
(2) all the nodes predict the distribution function of the positions of the nodes according to the motion states of the nodes;
(3) each node is connected with other nodes through a network, and positioning data is exchanged among different nodes;
(4) the adjacent nodes carry out distance measurement through radio and visual means, and the cooperative correction information among the nodes is calculated;
(5) calculating contribution degree of all collaborative informationPerforming priority ranking on the collaborative correction information according to the contribution degree, and excluding low contribution degree information;
(6) according to the formulaCalculating the edge posterior probability of the node i at the time t;
(7) returning to the step (4) for iterative computation until the bitPlacing informationAnd (6) converging.
Calculating contribution degrees of all collaborative information in step (5)The calculation method of (c) is as follows:
wherein,for the Fisher information value of the ranging distribution function,for correcting information in coordinationThe distribution of (a) to (b) is,is the coordinate of the node i at the time t;for correcting information in coordinationAnd the self position prediction information of the i nodeThe relative entropy value between the two or more of the two,is the mean value of the positioning results of the node i,a positioning variance for node i;
wherein:for the information including the self state prediction of the node i at the time t and the recursion state estimation of the node i from 0 to t-1,for the state estimation information of the node i at the time t after self prediction and cooperative correction,the cooperation information indicating that the node j flows from the node i at time t includes the self-state prediction information and the relative ranging information of the node j at time t.
The invention has the following beneficial effects:
the method is characterized in that the contribution degree of the collaborative correction information is calculated, all collaborative correction information is subjected to priority ranking, so that the elimination of low-contribution-degree information is realized, only the collaborative correction information with higher contribution degree is used for belief propagation calculation, and higher collaborative positioning precision is maintained under the condition of greatly reducing the calculation amount, so that the method is suitable for the practical application of engineering.
Drawings
FIG. 1 is a factor graph in the implementation of the present invention.
Fig. 2 is a flow of collaborative correction information screening.
FIG. 3 is a comparison curve of the distribution of the height error accumulation of the belief propagation cooperative positioning algorithm for the user node, wherein the distribution is coordinated with least square positioning (C-WLS), Non-cooperative positioning (Non-cooperative), Non-screening information (BP), screening 6 information (BP (CM ≦ 6)), and screening 3 information (BP (CM ≦ 3)).
FIG. 4 is a graph of cumulative distribution versus longitude error for 5 methods.
FIG. 5 is a comparison plot of cumulative distribution of latitude error for 5 methods.
Detailed Description
The technical scheme of the invention is explained in detail in the following with reference to the attached drawings.
Fig. 1 and fig. 2 show a collaborative correction information screening method for collaborative positioning of a wireless sensor networkIn the process of node self state transition, the step predicts the node state through self measurement information.Representing the likelihood function of ranging information between node i and node j.The method comprises the information of self state prediction of the node i at the time t and the recursive state estimation of the node i from 0 to t-1.And estimating information of the state of the node i at the time t after self prediction and cooperative correction.The cooperation information indicating that the node j flows from the node i at time t includes the self-state prediction information and the relative ranging information of the node j at time t.
The method mainly comprises the following steps:
(1) initializing location probability distributions for all nodes and anchorsWherein the probability distribution of the anchor points is impulseA shock function.
(2) All nodes predict the distribution function of the positions of the nodes according to the motion states of the nodes:
wherein,is the conditional probability of a node moving from a time t-1 position to a time t position,is the conditional probability of the node velocity.
(3) Each node is connected with other nodes through a network, and positioning data can be exchanged among different nodes.
(4) The neighboring nodes perform ranging by means of radio, vision, etc., if ranging can be performed between some two nodes (assuming that the node i and the node j) then the cooperative correction information between these two nodes can be calculated, and when the errors in the system are all regarded as gaussian distribution, the cooperative information can be expressed as:
wherein,the coordinates of the node i at time t,is the average of the ranges between node i and node j,is the average of the positions of the node j,in order to measure the variance value of the distance,is the positioning variance of node j.
(5) Calculating contribution degree of all collaborative informationThe calculation method is as follows:
wherein,for the Fisher information value of the ranging distribution function,for correcting information in coordinationThe distribution of (a);
for correcting information in coordinationAnd the self position prediction information of the i nodeThe relative entropy value between the two or more of the two,is the mean value of the positioning results of the node i,is the positioning variance of node i.
If the estimation precision of the cooperative correction information is high and the distribution difference with the prediction information of the node is large, a large cooperative correction contribution can be provided, and according to the Fisher information and the definition of the relative entropy, a large contribution degree calculated by the cooperative correction information is obtained
And carrying out priority sequencing on the collaborative correction information according to the contribution degree, and excluding the low contribution degree information.
(6) According to the formulaCalculating the posterior probability of the edge of the node i at the time t, wherein j belongs to SiThe collaborative information in the calculation is represented as a screened information set Si。
(7) Due to the limitation of the ranging range, it cannot be guaranteed that the position information of the anchor point has been delivered to all nodes through belief propagation, and therefore, the iterative calculation needs to be performed by returning to the step (4) until the position information reachesAnd (6) converging.
In order to verify the performance of the method provided by the present invention, fig. 3, fig. 4, and fig. 5 respectively compare the cooperative positioning accuracy of 5 methods, and the comparison groups respectively are:
the method comprises the following steps of firstly, carrying out belief propagation algorithm (③) without screening information, secondly, screening belief propagation algorithm (③ (CM is less than or equal to 6)) of 6 information, thirdly, screening belief propagation algorithm (③ (CM is less than or equal to 3)) of 3 information, thirdly, carrying out collaborative least square positioning (C-WLS) and fifthly, carrying out Non-collaborative positioning (Non-collaborative).
According to the data in the graph, under a collaborative navigation mechanism based on belief propagation, the navigation performance of the nodes is remarkably improved, and the positioning accuracy of the nodes is higher than that of a C-WLS and a non-collaborative mode. In general, after the collaborative information is screened, the improvement effect of the collaborative navigation on the node precision is slightly reduced, but the performance reduction is not obvious, so that the method provided by the invention realizes the balance of the computation amount and the precision, and is suitable for the practical application of the collaborative algorithm in the engineering.
Claims (2)
1. A collaborative correction information screening method aiming at collaborative positioning of a wireless sensor network is characterized by comprising the following steps:
(1) initializing location probability distributions for all nodes and anchorsWherein the probability distribution of the anchor points is an impulse function;
(2) all the nodes predict the distribution function of the positions of the nodes according to the motion states of the nodes;
(3) each node is connected with other nodes through a network, and positioning data is exchanged among different nodes;
(4) the adjacent nodes carry out distance measurement through radio and visual means, and the cooperative correction information among the nodes is calculated;
(5) calculating contribution degree of all collaborative informationPerforming priority ranking on the collaborative correction information according to the contribution degree, and excluding low contribution degree information;
(6) according to the formulaCalculating the edge posterior probability of the node i at the time t;
(7) returning to the step (4) for iterative calculation until the position informationAnd (6) converging.
2. The collaborative filtering method for collaborative positioning according to claim 1, wherein the contribution degree of all collaborative information is calculated in step (5)The calculation method of (c) is as follows:
wherein,for the Fisher information value of the ranging distribution function,for correcting information in coordinationThe distribution of (a) to (b) is,is the coordinate of the node i at the time t;for correcting information in coordinationAnd the self position prediction information of the i nodeThe relative entropy value between the two or more of the two,is the mean value of the positioning results of the node i,a positioning variance for node i;
wherein:for the information including the self state prediction of the node i at the time t and the recursion state estimation of the node i from 0 to t-1,for the state estimation information of the node i at the time t after self prediction and cooperative correction,the cooperation information indicating that the node j flows from the node i at time t includes the self-state prediction information and the relative ranging information of the node j at time t.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810765729.6A CN108810823A (en) | 2018-07-12 | 2018-07-12 | For the collaboration update information screening technique of wireless sensor network co-positioned |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810765729.6A CN108810823A (en) | 2018-07-12 | 2018-07-12 | For the collaboration update information screening technique of wireless sensor network co-positioned |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108810823A true CN108810823A (en) | 2018-11-13 |
Family
ID=64076366
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810765729.6A Pending CN108810823A (en) | 2018-07-12 | 2018-07-12 | For the collaboration update information screening technique of wireless sensor network co-positioned |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108810823A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109901108A (en) * | 2019-03-19 | 2019-06-18 | 南京航空航天大学 | A kind of formation unmanned plane co-located method based on posteriority linearisation belief propagation |
| CN110139211A (en) * | 2019-05-21 | 2019-08-16 | 北京邮电大学 | A kind of co-located method and system |
| CN112995898A (en) * | 2021-03-10 | 2021-06-18 | 南京航空航天大学 | Unmanned aerial vehicle cluster belief propagation cooperative positioning method based on CASSA (computer-aided single-station analysis) optimization |
| CN113411883A (en) * | 2021-06-17 | 2021-09-17 | 北京理工大学 | Distributed cooperative positioning method for determining convergence |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1988550A (en) * | 2005-12-21 | 2007-06-27 | 中国科学院电子学研究所 | Distributing realizing method for radio sensor network no-anchor point location |
| CN101868026A (en) * | 2010-05-24 | 2010-10-20 | 北京航空航天大学 | Multi-hop positioning method for lightweight wireless sensor networks |
| US20120302273A1 (en) * | 2011-05-27 | 2012-11-29 | International Business Machines Corporation | Reducing spectrum interference between wireless networks |
| CN106341886A (en) * | 2016-10-20 | 2017-01-18 | 河海大学常州校区 | Cooperative positioning method based on sequential consistent weighted sum-product algorithm |
| CN104394573B (en) * | 2014-11-26 | 2018-03-09 | 北京邮电大学 | A kind of wireless sensor network Cooperative Localization Method |
-
2018
- 2018-07-12 CN CN201810765729.6A patent/CN108810823A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1988550A (en) * | 2005-12-21 | 2007-06-27 | 中国科学院电子学研究所 | Distributing realizing method for radio sensor network no-anchor point location |
| CN101868026A (en) * | 2010-05-24 | 2010-10-20 | 北京航空航天大学 | Multi-hop positioning method for lightweight wireless sensor networks |
| US20120302273A1 (en) * | 2011-05-27 | 2012-11-29 | International Business Machines Corporation | Reducing spectrum interference between wireless networks |
| CN104394573B (en) * | 2014-11-26 | 2018-03-09 | 北京邮电大学 | A kind of wireless sensor network Cooperative Localization Method |
| CN106341886A (en) * | 2016-10-20 | 2017-01-18 | 河海大学常州校区 | Cooperative positioning method based on sequential consistent weighted sum-product algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 熊骏等: "一种基于置信传播和协同信息筛选的无人机编队协同导航方法", 《无人载体与控制技术发展及应用学术研讨峰会文集》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109901108A (en) * | 2019-03-19 | 2019-06-18 | 南京航空航天大学 | A kind of formation unmanned plane co-located method based on posteriority linearisation belief propagation |
| CN110139211A (en) * | 2019-05-21 | 2019-08-16 | 北京邮电大学 | A kind of co-located method and system |
| CN110139211B (en) * | 2019-05-21 | 2020-05-01 | 北京邮电大学 | Cooperative positioning method and system |
| CN112995898A (en) * | 2021-03-10 | 2021-06-18 | 南京航空航天大学 | Unmanned aerial vehicle cluster belief propagation cooperative positioning method based on CASSA (computer-aided single-station analysis) optimization |
| CN113411883A (en) * | 2021-06-17 | 2021-09-17 | 北京理工大学 | Distributed cooperative positioning method for determining convergence |
| CN113411883B (en) * | 2021-06-17 | 2022-05-17 | 北京理工大学 | Distributed cooperative positioning method for determining convergence |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108810823A (en) | For the collaboration update information screening technique of wireless sensor network co-positioned | |
| Liu et al. | Mercury: An infrastructure-free system for network localization and navigation | |
| Li et al. | New efficient indoor cooperative localization algorithm with empirical ranging error model | |
| US20150341895A1 (en) | Positioning | |
| CN110933599B (en) | Self-adaptive positioning method fusing UWB and WIFI fingerprints | |
| CN109141427B (en) | EKF positioning method based on distance and angle probability model under non-line-of-sight environment | |
| CN102142085B (en) | Robust tracking method for moving flame target in forest region monitoring video | |
| JP2013186127A5 (en) | ||
| CN109116299B (en) | Fingerprint positioning method, terminal and computer readable storage medium | |
| CN112632202B (en) | Dynamic map matching method based on high-order hidden Markov model | |
| CN106643724B (en) | Particle filter indoor orientation method based on cartographic information and position adaptive correction | |
| CN103533647A (en) | Radio frequency map self-adaption positioning method based on clustering mechanism and robust regression | |
| US20200145790A1 (en) | Positioning correction method and apparatus | |
| CN103270801A (en) | Method of and system for locating the position of user equipment | |
| CN114727384A (en) | Bluetooth RSSI positioning method based on weighted min-max | |
| KR101268564B1 (en) | Apparatus, method and recoding media for tracking location of mobile device based on particle filter | |
| CN110909303B (en) | Adaptive space-time heterogeneity inverse distance interpolation method | |
| CN112911502B (en) | Base station longitude and latitude estimation method based on MDT technology and RSRP ranging | |
| CN110233608A (en) | A kind of particle filter method and radar system based on Weight number adaptively | |
| CN115685128B (en) | Radar target tracking algorithm and electronic equipment under maneuvering target scene | |
| Zhang et al. | Analysis of the NLOS channel environment of TDOA multiple algorithms | |
| CN112800889A (en) | Target tracking method based on distributed matrix weighting and Gaussian filtering fusion | |
| CN107516007B (en) | Method for calculating residence time of air mass | |
| CN107966697B (en) | Moving target tracking method based on progressive unscented Kalman | |
| CN114979943A (en) | RSSI indoor positioning algorithm based on RAUKF |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181113 |
|
| RJ01 | Rejection of invention patent application after publication |