CN106131955B - A kind of wireless sensor network node locating method based on mobile robot auxiliary - Google Patents

A kind of wireless sensor network node locating method based on mobile robot auxiliary Download PDF

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CN106131955B
CN106131955B CN201610545672.XA CN201610545672A CN106131955B CN 106131955 B CN106131955 B CN 106131955B CN 201610545672 A CN201610545672 A CN 201610545672A CN 106131955 B CN106131955 B CN 106131955B
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CN106131955A (en
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陈孟元
陈晓飞
凌有铸
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Anhui Polytechnic University
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    • 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

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The embodiment of the invention discloses a kind of wireless sensor network node locating method based on mobile robot auxiliary, belong to wireless sensor network node positioning field.Mobile robot is combined with wireless sensor network, the positioning method coordinated using robot node, node cooperation, 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, realize the dynamic positioning 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 algorithms of use adversely affect caused by can effectively overcoming high non-linearity and anomalous differences, reduce error caused by being dissipated due to system filter, improve node locating precision.

Description

A kind of wireless sensor network node locating method based on mobile robot auxiliary
Technical field
The present invention relates to wireless sensor network node positioning field, more particularly to it is a kind of based on mobile robot auxiliary Wireless sensor network node locating method.
Background technology
Wireless sensor network (Wireless Sensor Networks, WSNs) is examined as a radio communication and sensing The emerging technology that survey technology mutually blends, have become the fields such as national defense and military, biologic medical, production and living, traffic administration not The strength that can or lack.But in many utilizations, only node location state is, it is known that each node could be played more effectively Monitoring function.Environment is uncertain and unknown situation under, it is how more stable, accurately realize that node locating has become WSNs's One of basis and key technical problem.
The sensor node of a large amount of random scatters is usually contained in WSNs, the positioning method or profit artificially demarcated can be used Realized with the self-contained global positioning system of sensor (Global Positioning System, GPS).With WSNs The increasingly scale arranged net, the difficulty and cost manually demarcated also are 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 the three side positioning modes based on multiple anchor nodes, DV-HOP Method, Monte Carlo method etc., but the realization of these localization methods is realized based on multiple fixed anchor nodes mostly, wants to realize height The dynamic positioning of precision, deployment and quantity to anchor node have higher requirement, and the increase of quantity can also cause calculated load Increase, influence the reliability of positioning.
The content of the invention
It is an object of the invention to provide a kind of wireless sensor network node positioning side based on mobile robot auxiliary Method, to solve caused above-mentioned multinomial defect in the prior art.
The embodiment of the present invention adopts the following technical scheme that：
A kind of wireless sensor network node locating method based on mobile robot auxiliary, it is characterised in that the side Method comprises the following steps：
Step 1) node is in communication with each other positioning with anchor node known to part, obtains relative reference location information；
Step 2) mobile robot periodically sends effective sight between positional information and foundation and node in moving process Survey, establish observed range set and position coordinates set；
Step 3) robot cooperates auxiliary positioning with node, establishes multiple constraint inequality group, asks for estimated location；
Step 4) is using Gaussian Mixture volume Kalman filtering algorithm to positioning further refinement.
Optionally, in the step 1), it is in communication with each other between part of nodes, obtains relative distance information.Node Mi And MjThe counterpart node range information of acquisition is di,j, the measurement model of node and node is represented by：
Wherein zi,jThe positional information between node is represented,Gaussian noise caused by ranging between node, (xi,yi), (xj,yj) it is node i and j position coordinates.
Optionally, in the step 2), described robot reaches each state XkPlace can establish with each node The measurement of relative efficiency, the relative distance with node can be obtained after measurementAnd relative angleSurvey of the robot to node Measuring model is：
Wherein qr(Xk,Mj) it is measurement equation of the robot to node, (xk,yk) be k moment robots coordinate, (xj, yj) be node j position coordinates,Represent the error that radio communication is brought, the observation Gaussian noise between robot and node.
Optionally, in the step 3), the data that robotic end is sent to monitoring computer in the auxiliary positioning that cooperates include Time k, robot current location Xk, the location information with neighbors foundationMeasurement to neighborsPass through moving machine For device people in the observation of diverse location, each node can obtain a series of inequality constraints on self-position：It is possible thereby to produce the inequality group of multiple constraint, pass through minimumIt must be approached to optimum position.
Optionally, in the step 3), state space equation corresponding to the auxiliary positioning that cooperates is：
Wherein XkRepresent k moment robots current location, ZkRepresent the k moment to node j observation, εkFor sensitive zones Position detection noise caused by internal cause environment,Represent Gaussian noise caused by less radio-frequency observation.
Optionally, in the step 4), after positional information is estimated in acquisition, Gaussian Mixture volume Kalman filtering algorithm is utilized State fusion estimation is carried out to location information.
Optionally, in the step 4), Gaussian Mixture volume Kalman filtering algorithm is divided into three parts, Gauss segmentation, door Limit differentiates, forecast updating.
The wireless sensor network node locating method of mobile robot auxiliary based on above-mentioned technical proposal, using machine The positioning method that people-node, NODE-NODE cooperation coordinate, make full use of the mobility and wireless sensor node of robot Computability, Gaussian Mixture volume Kalman filtering is incorporated, realize the dynamic positioning to node, the co-positioned side proposed Method can realize the location estimation to node, and the Gaussian Mixture volume Kalman filtering algorithm of use can effectively overcome height non-thread Property and anomalous differences caused by adversely affect, reduce due to system filter dissipate caused by error, improve node locating precision.
It should be appreciated that the general description and following detailed description of the above are only exemplary and explanatory, not The disclosure can be limited.
Brief description of the drawings
Accompanying drawing herein is merged in specification and forms the part of this specification, shows the implementation for meeting the present invention Example, and for explaining principle of the invention together with specification.
Fig. 1 is a kind of wireless sensor network node locating method system model based on mobile robot auxiliary of the present invention Schematic diagram；
Fig. 2 is a kind of wireless sensor network node locating method flow chart based on mobile robot auxiliary of the present invention；
Fig. 3 is a kind of wireless sensor network node locating method algorithm flow based on mobile robot auxiliary of the present invention Figure.
Embodiment
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 Site preparation describes, it is clear that described embodiment is part of the embodiment of the present invention, rather than whole embodiments.Based on this hair Embodiment in bright, the every other implementation that those of ordinary skill in the art are obtained under the premise of creative work is not made Example, belongs to the scope of protection of the invention.
According to one embodiment of the invention, as shown in figure 1, a kind of wireless sensor network based on mobile robot auxiliary Node positioning method, it the described method comprises the following steps：
Step 1) node is in communication with each other positioning with anchor node known to part, obtains relative reference location information；
Step 2) mobile robot periodically sends effective sight between positional information and foundation and node in moving process Survey, establish observed range set and position coordinates set；
Step 3) robot cooperates auxiliary positioning with node, establishes multiple constraint inequality group, asks for estimated location；
Step 4) is using Gaussian Mixture volume Kalman filtering algorithm to positioning further refinement.
Whole co-positioned system by n WSNs node of mobile robot and extensive random scatter M1, M2 ... Mn } composition.Part of nodes location status, it is known that between each node can with adjacent node carry out mutually measurement, Communication.Mobile robot (Move Robot, MR) is unique removable module in whole co-positioned system, on the move not only Displacement information can be obtained by the sensor that itself is loaded, the neighbors location status of its process can also be observed.
In step 1), it is in communication with each other between part of nodes and known anchor node, node mutually calculates processing and obtains phase Information of adjusting the distance simultaneously is sent to monitoring computer.Node MiAnd MjThe counterpart node range information of acquisition is di,j, node and node Measurement model be represented by：
Wherein zi,jThe positional information between node is represented,Gaussian noise caused by ranging between node, (xi,yi), (xj,yj) it is node i and j position coordinates.
In step 2), described robot reaches each state XkPlace can establish the survey of relative efficiency with each node Amount, the relative distance that can be obtained with node is handled by robot calculating after measurementAnd relative angleRobot is to node Measurement model be：
Wherein qr(Xk,Mj) it is measurement equation of the robot to node, (xk,yk) be k moment robots coordinate, (xj, yj) be node j position coordinates,Represent the error that radio communication is brought, the observation Gaussian noise between robot and node.
In step 3), the robot data that robotic end in auxiliary positioning is sent to monitoring computer that cooperated with node include Time k, robot current location Xk, the location information with neighbors foundationMeasurement to neighborsPass through moving machine For device people in the observation of diverse location, each node can obtain a series of inequality constraints on self-position：It is possible thereby to produce the inequality group of multiple constraint, pass through minimumIt must be approached to optimum position, now corresponding state space equation is：
Wherein XkRepresent k moment robots current location, ZkRepresent the k moment to node j observation, εkFor sensitive zones Position detection noise caused by internal cause environment,Represent Gaussian noise caused by less radio-frequency observation.
In step 4), after positional information is estimated in acquisition, using Gaussian Mixture volume Kalman filtering algorithm to location information Carry out State fusion estimation.
In addition, as shown in Fig. 2 mobile robot moves according to certain mobile route in WSNs, due to mobile machine People periodically issues own location information, thus can continuous online updating positional information, because observation can only provide one The information of dimension, so enough constraints can not be obtained from certain measurement once, unknown node receives robot cycle After multiple information such as positional information, node observation information, observed range set and position coordinates set are established, and utilize multiple constraint Inequality group asks for estimated location.After receiving robot observation, Gaussian Mixture volume Kalman filtering algorithm (GM- is utilized CKF) filtering algorithm is to positioning further refinement, so as to improve node locating precision.
As shown in figure 3, the state renewal step and measuring process of volume Kalman filtering algorithm are as follows：
The first step：K-1 moment estimate variances are decomposed
Second step：Cubature points calculate
3rd step：Cubature points are propagated
4th step：Try to achieve predicted state and prediction covariance
5th step：Prediction covariance matrix is decomposed to obtainCalculate Cubature points
6th step：Estimate is measured to calculate
7th step：Calculate the error in measurement variance after renewal
8th step：Calculate covariance
9th step：Calculate Kalman filtering gain
Therefore, state vector and corresponding estimate covariance are：
As shown in figure 4, the Gaussian Mixture volume Kalman filtering algorithm is divided into three parts, Gauss segmentation, threshold discrimination, Forecast updating.The amount of calculation of robotary estimation can increase with the time in series, therefore all be accorded with node and robot observation When closing approximate Gaussian distribution, observability estimate section [a, the b] equal proportion for filtering initial time is divided into common ratio isN Individual Gaussian component, each component can be used 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 n-th of component weight of k moment is：
Wherein：p(zk|xk, i) and the likelihood function corresponding to n-th component, it is represented by：
Wherein σiWithCovariance and premeasuring for i-th Gaussian component.Calculate estimation output and be represented by Gauss The parameter weighting of component and, i.e.,：
The sharp likelihood function containing Gaussian component again, refinement is carried out to the weight of subfilter.By setting authority γwCan be with Authority is 0 or removed close to 0 subfilter.Again because Robotic Dynamic is stronger, have in each moment linearity Institute is different, in being the introduction of global nonlinear degree differentiation amount：
WhereinForWithCross covariance,ForVariance.Again The higher metric-threshold γ of nonlinear degree is setnIfMore than γn, it is considered as the non-linear journey of this subfilter of moment Degree it is higher, by this prediction be divided into n gaussian density with：
In formula,For carry out Gauss selection segmentation after n-th of component prediction average,Represent association side corresponding to it Difference., whereas if nonlinear degree is not less than γn, then do not split.Such priority assignation causes the operand of the algorithm more Few, validity and reliability is also improved.The state of each wave filter and covariance estimation can be filtered by volume Kalman The state renewal step and measuring process of ripple algorithm are updated.
It is described above various embodiments of the present invention, described above is exemplary, and non-exclusive, and It is not limited to disclosed each embodiment.In the case of without departing from the scope and spirit of illustrated each embodiment, for this skill Many modifications and changes will be apparent from for the those of ordinary skill in art field.The selection of term used herein, purport The principle of each embodiment, practical application or improvement to the technology in market are best being explained, or is making the art Other those of ordinary skill are understood that each embodiment disclosed herein.
Those skilled in the art will readily occur to the disclosure its after considering specification and putting into practice disclosure disclosed herein Its embodiment.The application is intended to any modification, purposes or the adaptations of the disclosure, these modifications, purposes or Person's adaptations follow the general principle of the disclosure and including the undocumented common knowledges in the art of the disclosure Or conventional techniques.

Claims (6)

  1. A kind of 1. wireless sensor network node locating method based on mobile robot auxiliary, it is characterised in that methods described Comprise the following steps：
    Step 1) node is in communication with each other positioning with anchor node known to part, obtains relative reference location information；
    Step 2) mobile robot periodically sends effective observation between positional information and foundation and node in moving process, builds Vertical observed range set and position coordinates set；
    Step 3) robot cooperates auxiliary positioning with node, establishes multiple constraint inequality group, asks for estimated location, wherein, cooperation Robotic end includes time k, robot current location X to the data that monitoring computer is sent in auxiliary positioningk, built with neighbors Vertical location informationMeasurement to neighborsBy mobile robot in the observation of diverse location, each node can be with Obtain a series of inequality constraints on self-position：It is possible thereby to produce multiple constraint not Equation set, pass through minimumIt must be approached to optimum position；
    Step 4) is using Gaussian Mixture volume Kalman filtering algorithm to positioning further refinement.
  2. 2. a kind of wireless sensor network node locating method based on mobile robot auxiliary according to claim 1, Characterized in that, in the step 1), it is in communication with each other between part of nodes, obtains relative distance information；Node MiAnd MjObtain The counterpart node range information obtained is di,j, the measurement model of node and node is represented by：
    <mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <msup> <mi>z</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msup> <mo>=</mo> <msup> <mi>d</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msup> <mo>+</mo> <msubsup> <mi>&amp;delta;</mi> <mi>k</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msubsup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>=</mo> <msqrt> <mrow> <msup> <mrow> <mo>(</mo> <msup> <mi>x</mi> <mi>i</mi> </msup> <mo>-</mo> <msup> <mi>x</mi> <mi>j</mi> </msup> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msup> <mi>y</mi> <mi>i</mi> </msup> <mo>-</mo> <msup> <mi>y</mi> <mi>j</mi> </msup> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> <mo>+</mo> <msubsup> <mi>&amp;delta;</mi> <mi>k</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msubsup> </mrow> </mtd> </mtr> </mtable> </mfenced>
    Wherein zi,jThe positional information between node is represented,Gaussian noise caused by ranging between node, (xi,yi), (xj, yj) it is node i and j position coordinates.
  3. 3. a kind of wireless sensor network node locating method based on mobile robot auxiliary according to claim 1, Characterized in that, in the step 2), described robot reaches each state XkPlace can establish relative with each node Effective measurement, can obtain the relative distance with node after measurementAnd relative angleMeasurement mould of the robot to node Type is：
    <mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <msubsup> <mi>z</mi> <mi>k</mi> <mrow> <mi>r</mi> <mo>,</mo> <mi>j</mi> </mrow> </msubsup> <mo>=</mo> <msup> <mi>q</mi> <mi>r</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>k</mi> </msub> <mo>,</mo> <msup> <mi>M</mi> <mi>j</mi> </msup> <mo>)</mo> </mrow> <mo>+</mo> <msubsup> <mi>&amp;delta;</mi> <mi>k</mi> <mi>r</mi> </msubsup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msubsup> <mi>d</mi> <mi>k</mi> <mrow> <mi>r</mi> <mo>,</mo> <mi>j</mi> </mrow> </msubsup> <mo>,</mo> <msubsup> <mi>&amp;theta;</mi> <mi>k</mi> <mrow> <mi>r</mi> <mo>,</mo> <mi>j</mi> </mrow> </msubsup> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mo>+</mo> <msubsup> <mi>&amp;delta;</mi> <mi>k</mi> <mi>r</mi> </msubsup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>=</mo> <mfenced open = "{" close = "}"> <mtable> <mtr> <mtd> <msqrt> <mrow> <msup> <mrow> <mo>(</mo> <msup> <mi>x</mi> <mi>j</mi> </msup> <mo>-</mo> <msub> <mi>x</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msup> <mi>y</mi> <mi>j</mi> </msup> <mo>-</mo> <msub> <mi>y</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>arctan</mi> <mfrac> <mrow> <msup> <mi>y</mi> <mi>j</mi> </msup> <mo>-</mo> <msub> <mi>y</mi> <mi>k</mi> </msub> </mrow> <mrow> <msup> <mi>x</mi> <mi>j</mi> </msup> <mo>-</mo> <msub> <mi>x</mi> <mi>k</mi> </msub> </mrow> </mfrac> <mo>-</mo> <msub> <mi>&amp;theta;</mi> <mi>k</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>+</mo> <msubsup> <mi>&amp;delta;</mi> <mi>k</mi> <mi>r</mi> </msubsup> </mrow> </mtd> </mtr> </mtable> </mfenced>
    Wherein qr(Xk,Mj) it is measurement equation of the robot to node, (xk,yk) be k moment robots coordinate, (xj,yj) it is section Point j position coordinates,Represent the error that radio communication is brought, the observation Gaussian noise between robot and node.
  4. 4. a kind of wireless sensor network node locating method based on mobile robot auxiliary according to claim 1, Characterized in that, in the step 3), state space equation corresponding to the auxiliary positioning that cooperates is：
    <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>X</mi> <mi>k</mi> </msub> <mo>=</mo> <mi>g</mi> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>u</mi> <mi>k</mi> </msub> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>&amp;epsiv;</mi> <mi>k</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>Z</mi> <mi>k</mi> </msub> <mo>=</mo> <msup> <mi>q</mi> <mi>r</mi> </msup> <mrow> <mo>(</mo> <msub> <mi>X</mi> <mi>k</mi> </msub> <mo>,</mo> <msubsup> <mi>M</mi> <mi>k</mi> <mi>j</mi> </msubsup> <mo>)</mo> </mrow> <mo>+</mo> <msubsup> <mi>&amp;delta;</mi> <mi>k</mi> <mi>r</mi> </msubsup> </mrow> </mtd> </mtr> </mtable> </mfenced>
    Wherein XkRepresent k moment robots current location, ZkRepresent the k moment to node j observation, εkFor sensitive zones internal cause Position detection noise caused by environment,Represent Gaussian noise caused by less radio-frequency observation.
  5. 5. a kind of wireless sensor network node locating method based on mobile robot auxiliary according to claim 1, Characterized in that, in the step 4), after positional information is estimated in acquisition, using Gaussian Mixture volume Kalman filtering algorithm to fixed Position information carries out State fusion estimation.
  6. 6. a kind of wireless sensor network node locating method based on mobile robot auxiliary according to claim 5, Characterized in that, in the step 4), Gaussian Mixture volume Kalman filtering algorithm is divided into three parts, and Gauss segmentation, thresholding are sentenced Not, forecast updating.
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