CN106131955A - A kind of based on the mobile human-aided wireless sensor network node locating method of machine - Google Patents

A kind of based on the mobile human-aided wireless sensor network node locating method of machine Download PDF

Info

Publication number
CN106131955A
CN106131955A CN201610545672.XA CN201610545672A CN106131955A CN 106131955 A CN106131955 A CN 106131955A CN 201610545672 A CN201610545672 A CN 201610545672A CN 106131955 A CN106131955 A CN 106131955A
Authority
CN
China
Prior art keywords
node
wireless sensor
robot
sensor network
machine
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.)
Granted
Application number
CN201610545672.XA
Other languages
Chinese (zh)
Other versions
CN106131955B (en
Inventor
陈孟元
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.)
Anhui Polytechnic University
Original Assignee
Anhui Polytechnic University
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 Anhui Polytechnic University filed Critical Anhui Polytechnic University
Priority to CN201610545672.XA priority Critical patent/CN106131955B/en
Publication of CN106131955A publication Critical patent/CN106131955A/en
Application granted granted Critical
Publication of CN106131955B publication Critical patent/CN106131955B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • 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
    • 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
    • H04W4/025—Services making use of location information using location based information parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

The embodiment of the invention discloses a kind of based on the mobile human-aided wireless sensor network node locating method of machine, belong to wireless sensor network node positioning field.Movement robot is combined with wireless sensor network, use the location mode that the cooperation of robot node, node coordinates, make full use of the mobility of mobile robot and the computability of wireless sensor node, incorporate Gaussian Mixture volume Kalman filtering (Gaussian Mixture Cubature Kalman filter, GM CKF) algorithm, it is achieved that the dynamic location to node.The Cooperative Localization Method that the embodiment of the present invention is proposed can realize the location estimation to node, the GM CKF algorithm used can effectively overcome the adverse effect that high non-linearity and anomalous differences cause, reduce owing to system filter dissipates the error caused, improve node locating precision.

Description

A kind of based on the mobile human-aided wireless sensor network node locating method of machine
Technical field
The present invention relates to wireless sensor network node positioning field, particularly relate to a kind of human-aided based on mobile machine Wireless sensor network node locating method.
Background technology
Wireless sensor network (Wireless Sensor Networks, WSNs) is as a radio communication and sensing inspection The emerging technology that survey technology mutually blends, has become as national defense and military, biologic medical, productive life, and the field such as traffic administration is not The strength that can or lack.But in many is used, only node location state is it is known that each node could more effectively be played Monitoring function.Environment is uncertain and under unknown situation, the most more stable, realize node locating accurately and have become as WSNs's One of basis and key technical problem.
WSNs usually contains the sensor node of a large amount of random scatter, can be to use the artificial location mode demarcated or profit The global positioning system (Global Positioning System, GPS) self-contained with sensor realizes.Along with WSNs The scale day by day arranged net, the artificial difficulty demarcated and cost are also improving constantly, and cause each sensor node to load GPS and become Obtain and no longer gear to actual circumstances.What node positioning method mainly used at present has three limit positioning modes based on multiple anchor nodes, DV-HOP Method, Monte Carlo method etc., but the realization of these localization methods realizes based on multiple fixed anchor nodes mostly, wants to realize height The dynamic of precision positions, and deployment and quantity to anchor node have higher requirement, and the increase of quantity also can cause calculated load Increase, the reliability of impact location.
Summary of the invention
It is an object of the invention to provide a kind of based on mobile machine human-aided wireless sensor network node location side Method, to solve the above-mentioned multinomial defect caused in prior art.
The embodiment of the present invention adopts the following technical scheme that
A kind of based on the mobile human-aided wireless sensor network node locating method of machine, it is characterised in that described side Method comprises the following steps:
Step 1) node and the known anchor node of part be in communication with each other location, it is thus achieved that reference location information relatively；
Step 2) mobile robot periodically sends positional information in moving process and sets up and internodal effective sight Survey, set up observed range set and position coordinates set；
Step 3) robot cooperates with node auxiliary positioning, sets up multiple constraint inequality group, ask for estimating position；
Step 4) utilize Gaussian Mixture volume Kalman filtering algorithm to positioning further refinement.
Optionally, described step 1) in, it is in communication with each other between part of nodes, it is thus achieved that relative distance information.Node Mi And MjThe counterpart node range information obtained is di,j, node is represented by with the measurement model of node:
Wherein zi,jRepresent internodal positional information,The Gaussian noise produced for the range finding between node, (xi,yi), (xj,yj) it is the position coordinates of node i and j.
Optionally, described step 2) in, described robot arrives each state XkPlace can set up with each node The measurement of relative efficiency, can obtain the relative distance with node after measurementAnd relative angleThe robot measurement to node Model is:
Wherein qr(Xk,Mj) it is the robot measurement equation to node, (xk,yk) it is the coordinate of k moment robot, (xj, yj) it is the position coordinates of node j,Represent the error that radio communication is brought, for robot and internodal observation Gaussian noise.
Optionally, described step 3) in, in cooperation auxiliary positioning, robotic end is when the data that monitoring computer sends include Between k, robot current location Xk, with the location information of neighbors foundationMeasurement to neighborsExisted by mobile robot The observation of diverse location, each node can obtain a series of inequality constraints about self-position: Thus can produce the inequality group of multiple constraint, by minimizingObtain optimum bit Put and approach.
Optionally, described step 3) in, state space equation corresponding to cooperation auxiliary positioning is:
Wherein XkThe state of etching system, Z when representing kkRepresent the k moment observation to node j, εkFor sensitive zones endogenous cause of ill The position detection noise that environment causes,Represent the Gaussian noise that less radio-frequency observation produces.
Optionally, described step 4) in, it is thus achieved that after estimating positional information, utilize Gaussian Mixture volume Kalman filtering algorithm Location information is carried out State fusion estimation.
Optionally, described step 4) in, Gaussian Mixture volume Kalman filtering algorithm is divided into three parts, Gauss segmentation, door Limit differentiates, forecast updating.
The mobile human-aided wireless sensor network node locating method of machine based on technique scheme, uses machine The location mode that the cooperation of people-node, NODE-NODE coordinates, makes full use of the mobility of robot and wireless sensor node Computability, incorporates Gaussian Mixture volume Kalman filtering, it is achieved that the dynamic location to node, the co-positioned side proposed Method can realize the location estimation to node, and the Gaussian Mixture volume Kalman filtering algorithm of employing can effectively overcome high non-thread Property and the adverse effect that causes of anomalous differences, reduce owing to system filter dissipates the error caused, improve node locating precision.
It should be appreciated that it is only exemplary and explanatory, not that above general description and details hereinafter describe The disclosure can be limited.
Accompanying drawing explanation
Accompanying drawing herein is merged in description and constitutes the part of this specification, it is shown that meet the enforcement of the present invention Example, and for explaining the principle of the present invention together with description.
Fig. 1 is that the present invention is a kind of based on mobile machine human-aided wireless sensor network node locating method system model Schematic diagram；
Fig. 2 is that the present invention is a kind of based on mobile machine human-aided wireless sensor network node locating method flow chart；
Fig. 3 is that the present invention is a kind of based on mobile machine human-aided wireless sensor network node locating method algorithm flow Figure.
Fig. 4 is that the present invention is a kind of based on mobile machine human-aided wireless sensor network node location Gaussian Mixture volume Kalman filtering algorithm flow chart.
Detailed description of the invention
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete Describe, it is clear that described embodiment is a part of embodiment of the present invention rather than whole embodiments wholely.Based on this Embodiment in bright, the every other enforcement that those of ordinary skill in the art are obtained under not making creative work premise Example, broadly falls into the scope of protection of the invention.
According to one embodiment of the invention, as it is shown in figure 1, one is based on the mobile human-aided wireless sensor network of machine Node positioning method, said method comprising the steps of:
Step 1) node and the known anchor node of part be in communication with each other location, it is thus achieved that reference location information relatively；
Step 2) mobile robot periodically sends positional information in moving process and sets up and internodal effective sight Survey, set up observed range set and position coordinates set；
Step 3) robot cooperates with node auxiliary positioning, sets up multiple constraint inequality group, ask for estimating position；
Step 4) utilize Gaussian Mixture volume Kalman filtering algorithm to positioning further refinement.
Whole co-positioned system by move robot and extensive random scatter n WSNs node M1, M2 ... Mn} forms.Part of nodes location status it is known that mutually can measure with adjacent node between each node, communication.Mobile Robot (Move Robot, MR) is unique removable module in whole co-positioned system, can not only be by certainly in movement The sensor that body loads obtains displacement information, it is also possible to observe the neighbors location status of its process.
Step 1) in, it being in communication with each other between part of nodes and known anchor node, node mutually calculates process and obtains phase Information of adjusting the distance also is sent to monitoring computer.Node MiAnd MjThe counterpart node range information obtained is di,j, node and node Measurement model be represented by:
Wherein zi,jRepresent internodal positional information,The Gaussian noise produced for the range finding between node, (xi,yi), (xj,yj) it is the position coordinates of node i and j.
Step 2) in, described robot arrives each state XkPlace can set up the survey of relative efficiency with each node Amount, is processed by robot calculating after measurement and can obtain the relative distance with nodeAnd relative angleRobot is to node Measurement model be:
Wherein qr(Xk,Mj) it is the robot measurement equation to node, (xk,yk) it is the coordinate of k moment robot, (xj, yj) it is the position coordinates of node j,Represent the error that radio communication is brought, for robot and internodal observation Gaussian noise.
Step 3) in, robot cooperates in auxiliary positioning robotic end when the data that monitoring computer sends are included with node Between k, robot current location Xk, with the location information of neighbors foundationMeasurement to neighborsExisted by mobile robot The observation of diverse location, each node can obtain a series of inequality constraints about self-position: Thus can produce the inequality group of multiple constraint, by minimizingObtain optimum bit Putting and approach, now corresponding state space equation is:
Wherein XkThe state of etching system, Z when representing kkRepresent the k moment observation to node j, εkFor sensitive zones endogenous cause of ill The position detection noise that environment causes,Represent the Gaussian noise that less radio-frequency observation produces.
Step 4) in, it is thus achieved that after estimating positional information, utilize Gaussian Mixture volume Kalman filtering algorithm to location information Carry out State fusion estimation.
It addition, as in figure 2 it is shown, move robot in WSNs and move according to certain mobile route, due to mobile machine People periodically issues own location information, therefore can continuous online updating positional information, owing to observation can only provide one The information of dimension, so abundant constraint cannot be obtained from certain measurement once, unknown node receives robot cycle After multiple information such as positional information, node observation information, set up observed range set and position coordinates set, and utilize multiple constraint Inequality group is asked for estimating position.After receiving robot observation, utilize Gaussian Mixture volume Kalman filtering algorithm (GM- CKF) filtering algorithm is to positioning further refinement, thus improves node locating precision.
As it is shown on figure 3, the state of volume Kalman filtering algorithm updates step and measuring process is as follows:
The first step: k-1 moment estimate variance is decomposed
Second step: Cubature point calculates
3rd step: Cubature point is propagated
4th step: try to achieve predicted state and prediction covariance
5th step: prediction covariance matrix is carried out decomposition and obtainsCalculate Cubature point
6th step: measure estimated value and calculate
7th step: calculate the error in measurement variance after updating
8th step: calculate covariance
9th step: calculate Kalman filtering gain
Therefore, state vector and corresponding estimate covariance are:
As shown in Figure 4, described Gaussian Mixture volume Kalman filtering algorithm is divided into three parts, Gauss segmentation, threshold discrimination, Forecast updating.The amount of calculation that robotary is estimated can increase in progression in time, therefore observes all according with at node and robot When closing approximate Gaussian distribution, interval [a, b] equal proportion of observability estimate of filtering initial time is divided into common ratio isN Individual Gaussian component, each component can be as a subfilter, and corresponding priori average and standard deviation are represented by:
The initial weight of each Gaussian component is directly proportional to subinterval size, i.e.It is theoretical by Bayes, Obtaining k moment the n-th component weight is:
Wherein: p (zk|xk, i) it is the likelihood function that the n-th component is corresponding, is represented by:
Wherein σiWithCovariance and premeasuring for i-th Gaussian component.Calculate and estimate that output is represented by Gauss and divides Amount parameter weighting and, it may be assumed that
The profit likelihood function containing Gaussian component, carries out refinement to the weight of subfilter again.By arranging authority γwPermissible By authority be 0 or close to 0 subfilter remove.Relatively strong due to Robotic Dynamic again, have in each moment linearity Institute is different, in being the introduction of overall situation nonlinear degree differentiation amount:
Wherein ForWithCross covariance,ForVariance.Arrange again The metric-threshold γ that nonlinear degree is highernIf,More than γn, it is considered as the nonlinear degree of this this subfilter of moment relatively Height, by this prediction be divided into n gaussian density with:
In formula,For carrying out the prediction average of the n-th component after Gauss selection segmentation,Represent the association side of its correspondence Difference.Whereas if nonlinear degree is not less than γn, do not split.Such priority assignation makes the operand of this algorithm more Few, validity and reliability is also improved.The state of each wave filter and covariance are estimated to be filtered by volume Kalman The state of ripple algorithm updates step and measuring process is updated.
Being described above various embodiments of the present invention, described above is exemplary, and non-exclusive, and It is also not necessarily limited to disclosed each embodiment.In the case of the scope and spirit without departing from illustrated each embodiment, for this For the those of ordinary skill of technical field, many modifications and changes will be apparent from.The selection of term used herein, It is intended to explain the principle of each embodiment, actual application or the improvement to the technology in market best, or makes the art Other those of ordinary skill be understood that each embodiment disclosed herein.
Those skilled in the art, after considering description and putting into practice disclosure disclosed herein, will readily occur to its of the disclosure Its embodiment.The application is intended to any modification, purposes or the adaptations of the disclosure, these modification, purposes or Person's adaptations is followed the general principle of the disclosure and includes the undocumented common knowledge in the art of the disclosure Or conventional techniques means.

Claims (7)

1. one kind based on the mobile human-aided wireless sensor network node locating method of machine, it is characterised in that described method Comprise the following steps:
Step 1) node and the known anchor node of part be in communication with each other location, it is thus achieved that reference location information relatively；
Step 2) mobile robot periodically sends positional information in moving process and sets up and internodal effective observation, building Vertical observed range set and position coordinates set；
Step 3) robot cooperates with node auxiliary positioning, sets up multiple constraint inequality group, ask for estimating position；
Step 4) utilize Gaussian Mixture volume Kalman filtering algorithm to positioning further refinement.
One the most according to claim 1 based on the mobile human-aided wireless sensor network node locating method of machine, It is characterized in that, described step 1) in, it is in communication with each other between part of nodes, it is thus achieved that relative distance information；Node MiAnd MjObtain The counterpart node range information obtained is di,j, node is represented by with the measurement model of node:
z i , j = d i , j + δ k i , j = ( x i - x j ) 2 + ( y i - y j ) 2 + δ k i , j
Wherein zi,jRepresent internodal positional information,The Gaussian noise produced for the range finding between node, (xi,yi), (xj, yj) it is the position coordinates of node i and j.
One the most according to claim 1 based on the mobile human-aided wireless sensor network node locating method of machine, It is characterized in that, described step 2) in, described robot arrives each state XkPlace can set up relative with each node Effective measurement, can obtain the relative distance with node after measurementAnd relative angleThe robot measurement model to node For:
z k r , j = q r ( X k , M j ) + δ k r = ( d k r , j , θ k r , j ) T + δ k r = ( x j - x k ) 2 + ( y j - y k ) 2 arctan y j - y k x j - x k - θ k + δ k r
Wherein qr(Xk,Mj) it is the robot measurement equation to node, (xk,yk) it is the coordinate of k moment robot, (xj,yj) for saving The position coordinates of some j,Represent the error that radio communication is brought, for robot and internodal observation Gaussian noise.
4. according to a kind of based on the mobile human-aided wireless sensor network node locating method of machine described in Claims 2 or 3, its It is characterised by, described step 3) in, in cooperation auxiliary positioning, robotic end includes time k, machine to the data that monitoring computer sends Device people current location Xk, with the location information of neighbors foundationMeasurement to neighborsBy mobile robot at not coordination The observation put, each node can obtain a series of inequality constraints about self-position: Thus can produce the inequality group of multiple constraint, by minimizingObtain optimum bit Put and approach.
One the most according to claim 4 based on the mobile human-aided wireless sensor network node locating method of machine, It is characterized in that, described step 3) in, cooperation state space equation corresponding to auxiliary positioning is:
X k = g ( X k - 1 , u k ) + ϵ k Z k = q r ( X k , M k j ) + δ k r
Wherein XkThe state of etching system, Z when representing kkRepresent the k moment observation to node j, εkFor sensitive zones endogenous cause of ill environment The position detection noise caused,Represent the Gaussian noise that less radio-frequency observation produces.
One the most according to claim 1 based on the mobile human-aided wireless sensor network node locating method of machine, It is characterized in that, described step 4) in, it is thus achieved that after estimating positional information, utilize Gaussian Mixture volume Kalman filtering algorithm to fixed Position information carries out State fusion estimation.
One the most according to claim 6 based on the mobile human-aided wireless sensor network node locating method of machine, It is characterized in that, described step 4) in, Gaussian Mixture volume Kalman filtering algorithm is divided into three parts, Gauss segmentation, thresholding to sentence Not, forecast updating.
CN201610545672.XA 2016-07-12 2016-07-12 A kind of wireless sensor network node locating method based on mobile robot auxiliary Active CN106131955B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610545672.XA CN106131955B (en) 2016-07-12 2016-07-12 A kind of wireless sensor network node locating method based on mobile robot auxiliary

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610545672.XA CN106131955B (en) 2016-07-12 2016-07-12 A kind of wireless sensor network node locating method based on mobile robot auxiliary

Publications (2)

Publication Number Publication Date
CN106131955A true CN106131955A (en) 2016-11-16
CN106131955B CN106131955B (en) 2017-12-26

Family

ID=57282488

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610545672.XA Active CN106131955B (en) 2016-07-12 2016-07-12 A kind of wireless sensor network node locating method based on mobile robot auxiliary

Country Status (1)

Country Link
CN (1) CN106131955B (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106767783A (en) * 2016-12-15 2017-05-31 东软集团股份有限公司 Positioning correction method and device based on vehicle-carrying communication
CN108430105A (en) * 2017-12-28 2018-08-21 衢州学院 Distributed sensor networks cooperate with target state estimator and interference source passive location method
CN109253727A (en) * 2018-06-22 2019-01-22 东南大学 A kind of localization method based on improvement iteration volume particle filter algorithm
CN109655786A (en) * 2018-12-29 2019-04-19 清华大学 Mobile ad hoc network cooperation relative positioning method and device

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1945351A (en) * 2006-10-21 2007-04-11 中国科学院合肥物质科学研究院 Robot navigation positioning system and navigation positioning method
CN102359784A (en) * 2011-08-01 2012-02-22 东北大学 Autonomous navigation and obstacle avoidance system and method of indoor mobile robot
CN103776446A (en) * 2013-10-29 2014-05-07 哈尔滨工程大学 Pedestrian autonomous navigation calculation algorithm based on MEMS-IMU
US20140156125A1 (en) * 2012-12-05 2014-06-05 National Chiao Tung University Autonomous electronic apparatus and navigation method thereof
CN104062973A (en) * 2014-06-23 2014-09-24 西北工业大学 Mobile robot SLAM method based on image marker identification
CN105509755A (en) * 2015-11-27 2016-04-20 重庆邮电大学 Gaussian distribution based mobile robot simultaneous localization and mapping method
CN105737832A (en) * 2016-03-22 2016-07-06 北京工业大学 Distributed SLAM (simultaneous localization and mapping) method on basis of global optimal data fusion

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1945351A (en) * 2006-10-21 2007-04-11 中国科学院合肥物质科学研究院 Robot navigation positioning system and navigation positioning method
CN102359784A (en) * 2011-08-01 2012-02-22 东北大学 Autonomous navigation and obstacle avoidance system and method of indoor mobile robot
US20140156125A1 (en) * 2012-12-05 2014-06-05 National Chiao Tung University Autonomous electronic apparatus and navigation method thereof
CN103776446A (en) * 2013-10-29 2014-05-07 哈尔滨工程大学 Pedestrian autonomous navigation calculation algorithm based on MEMS-IMU
CN104062973A (en) * 2014-06-23 2014-09-24 西北工业大学 Mobile robot SLAM method based on image marker identification
CN105509755A (en) * 2015-11-27 2016-04-20 重庆邮电大学 Gaussian distribution based mobile robot simultaneous localization and mapping method
CN105737832A (en) * 2016-03-22 2016-07-06 北京工业大学 Distributed SLAM (simultaneous localization and mapping) method on basis of global optimal data fusion

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
海丹，李勇，张辉，李迅: "无线传感器网络环境下基于粒子婆婆的移动机器人SLAM算法", 《智能系统学报》 *
陈世明，郑丽楠，吴龙龙，方华京: "面向3维空间的移动机器人快速自适应SLAM算法", 《信息与控制》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106767783A (en) * 2016-12-15 2017-05-31 东软集团股份有限公司 Positioning correction method and device based on vehicle-carrying communication
CN108430105A (en) * 2017-12-28 2018-08-21 衢州学院 Distributed sensor networks cooperate with target state estimator and interference source passive location method
CN109253727A (en) * 2018-06-22 2019-01-22 东南大学 A kind of localization method based on improvement iteration volume particle filter algorithm
CN109253727B (en) * 2018-06-22 2022-03-08 东南大学 Positioning method based on improved iteration volume particle filter algorithm
CN109655786A (en) * 2018-12-29 2019-04-19 清华大学 Mobile ad hoc network cooperation relative positioning method and device

Also Published As

Publication number Publication date
CN106131955B (en) 2017-12-26

Similar Documents

Publication Publication Date Title
Liu et al. Kalman filter-based data fusion of Wi-Fi RTT and PDR for indoor localization
Woo et al. Application of WiFi-based indoor positioning system for labor tracking at construction sites: A case study in Guangzhou MTR
Liu et al. Improving positioning accuracy using GPS pseudorange measurements for cooperative vehicular localization
Gopakumar et al. Localization in wireless sensor networks using particle swarm optimization
Malyavej et al. Indoor robot localization by RSSI/IMU sensor fusion
Zhong et al. Technology and application of real-time compaction quality monitoring for earth-rockfill dam construction in deep narrow valley
CN106131955A (en) A kind of based on the mobile human-aided wireless sensor network node locating method of machine
CN109164411B (en) Personnel positioning method based on multi-data fusion
CN109597864A (en) Instant positioning and map constructing method and the system of ellipsoid boundary Kalman filtering
CN102186194B (en) Method for establishing passive target measurement model based on wireless sensor network
CN105704652A (en) Method for building and optimizing fingerprint database in WLAN/Bluetooth positioning processes
CN111970633A (en) Indoor positioning method based on WiFi, Bluetooth and pedestrian dead reckoning fusion
Gu et al. Trajectory estimation and crowdsourced radio map establishment from foot-mounted imus, wi-fi fingerprints, and gps positions
Cao et al. Improving positioning accuracy of UWB in complicated underground NLOS scenario using calibration, VBUKF, and WCA
CN106353722A (en) RSSI (received signal strength indicator) distance measuring method based on cost-reference particle filter
CN104066179A (en) Improved method for positioning WSN nodes through adaptive iterative UKF
CN104363649A (en) UKF (unscented Kalman filter)-based WSN (wireless sensor network) node location method with constraint conditions
Aernouts et al. Combining TDoA and AoA with a particle filter in an outdoor LoRaWAN network
CN106772516A (en) A kind of compound new location method based on fuzzy theory
CN110346821A (en) A kind of SINS/GPS integrated attitude determination localization method solving the problems, such as GPS long-time losing lock and system
Si et al. An adaptive weighted Wi-Fi FTM-based positioning method in an NLOS environment
Ma et al. Subway station real-time indoor positioning system for cell phones
Zhao et al. Factor graph based multi-source data fusion for wireless localization
Xiong et al. Hybrid WSN-RFID cooperative positioning based on extended Kalman filter
Song et al. RFID/in-vehicle sensors-integrated vehicle positioning strategy utilising LSSVM and federated UKF in a tunnel

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
CB03 Change of inventor or designer information

Inventor after: Chen Mengyuan

Inventor after: Chen Xiaofei

Inventor after: Ling Youzhu

Inventor before: Chen Mengyuan

COR Change of bibliographic data
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant