CN103685483A - Sensor network data association method based on target signal relative position pairing - Google Patents

Abstract

The invention provides a sensor network data association method based on target signal relative position pairing. The relative positions of sensor measuring signals are compared to extract a relative position mode so as to obtain the corresponding target matching method of different sensor systems, and a modified matching algorithm applicable to sensor target signal association and a sensor deviation estimation method are provided. A sensor deviation model is built, the expression of the relative positions among different sensor target signals and different sensors is given, the corresponding target signal matching points can be found by searching the maximum matching pair of the relative positions among different sensors, a rough deviation estimation is obtained, the target matching pairs of the residual sensor signals can be found through the deviation estimation, data association of different target signals can be completed, and an accurate deviation estimation result can be obtained.

Description

A kind of sensor network data correlating method of based target signal relative position pairing

Technical field

The present invention relates to sensor network, signal is processed, and a plurality of technical fields of Multi-sensor Fusion and data correlation are specifically related to the sensor network data correlating method that a kind of based target signal relative position matches.

Background technology

Application background of the present invention is:

Multi-sensor data corresponding technology is widely used in multisensor network system, data correlation technology is a major issue in modern multisensor network system, by utilizing the redundant information of different sensors, multisensor network system has better performance (referring to document [1] D.L.Hall and J.Llinas.Handbook of Multisensor Data Fusion Boca Raton than traditional Method for Single Sensor System, FL:CRC Press, 2001.).The associated object of multi-sensor data is to determine whether some measuring-signals that different sensors observes derive from same target.When the different sensors in sensor network is observed same echo signal, the measuring-signal that same target is set up in different sensors must have certain similar feature because of its physics source, but meanwhile, unstable due to the interference of clutter and transducer self performance, may cause the feature of measuring-signal of transducer incomplete same (referring to document [2] J.Llinas, E.Waltz.Multisensor Data Fusion Boston, MA:Artech House, 1990).Multi-sensor data corresponding technology is the similar features that utilizes measuring-signal, judge the incomplete same measuring-signal of these features whether from same target and eliminate clutter and transducer self performance for the impact of association results.

Being analyzed as follows of relevant prior art:

Scheme one

Scenario Name: overall arest neighbors data correlation method (GNN) is (referring to document [3] S.Blackman and R.Popoli.Design and Analysis of Modern Tracking Systems.Boston, MA:Artech House, 1999., and referring to document [4] P.Konstantinova, A.Udvarev, and T.Semerdjiev.A Study of a Target Tracking Algorithm Using Global Nearest Neighbor Approach.in Proceedings of the International Conference on Computer Systems and Technologies (CompSysTech03), 2003.)

Algorithm idea: first overall arest neighbors data correlation method needs to estimate the deviation impact for association results to elimination deviation between different sensors, then form the incidence matrices of the cost function of based target statistical distance, by Different Optimization method, solve the optimal solution of this incidence matrices, thereby find the corresponding relation of different sensors measurement target signal.

Algorithm shortcomings: overall arest neighbors data correlation method need to be estimated the deviation between different sensors, and this is difficult to realize in practice.In addition,, under intensive echo signal or many clutter environment, its association results error rate is higher.

Scheme two

Scenario Name: the data correlation method (GNP) of the nearest pattern of the overall situation is (referring to document [5] M.Levedahl.An Explicit Pattern Matching Assignment Algorithm.in Proceedings of SPIE, vol.4728,2002.pp.461-469.)

Algorithm idea: the overall situation recently data correlation method of pattern is passed through the deviation of transducer and loss, the sensor performance parameters such as the alert rate of mistake are introduced the statistical distance cost function of echo signal, and the method for estimating by maximum likelihood function is calculated the deviation under each possibility relevance assumption.By eliminating after estimated bias impact, form cost incidence matrices, solve the optimal solution of this incidence matrices, thereby find the corresponding relation of different sensors measurement target signal.

Algorithm shortcomings: the algorithm complex of this method is very high, when the quantity of echo signal increases, the complexity of the method can have rising.

Scheme three

Scenario Name: the topological data correlation method of the fuzzy reference of based target signal is (referring to document [6] Y.Shi, Y.Wang, X.M.Shan.A Novel Fuzzy Pattern Recognition Data Association Method for Biased Sensor Datain Information Fusion, 2006.FUSION06.9th International Conference on.2006, pp.1-5., and referring to document [7] X.J.Du, Y.Wang, X.M.Shan.Track-to-Track Association using Reference Topology in the Presence of Sensor Bias.In Proc.2010International Conf.on Signal Processing.pp.2196-2201.)

Algorithm idea: the topological data correlation method of fuzzy reference of based target signal passes through to extract a kind of topological method of fuzzy reference as the feature of echo signal, and defined the incidence matrices that a kind of cost function that includes the parameter of sensor performance forms echo signal, by solving the optimal solution of this incidence matrices, thereby find the corresponding relation of different sensors measurement target signal.

Algorithm shortcomings: the topological data correlation method of fuzzy reference of based target signal is vulnerable to the undetected impact of transducer, and this method is easily subject to the impact that parameter arranges, and the complexity of this algorithm is higher.

Summary of the invention

The object of the invention is: 1) can eliminate sensor bias for the impact of association results.2), with respect to conventional method, on this method overall performance, promote to some extent.3) arithmetic speed of association algorithm meets requirement of real time.

Technical solution of the present invention is: a kind of sensor network data correlating method of based target signal relative position pairing, the method is set up sensor bias model, and provided between sensor target signal and the expression formula of the relative position between different sensors, by finding the maximum of relative position between different sensors, mate right method, can find echo signal match point corresponding to a pair of different sensors, and obtain a comparatively coarse estimation of deviation, by this estimation of deviation, can find the object matching pair between remaining sensor signal, complete the data correlation of different target signal and obtain a result for estimation of deviation comparatively accurately, idiographic flow is as follows

1, sensor bias model

Suppose two different sensing system A and B, in whole observation area, observe N target, wherein sensing system A observes m echo signal, and sensing system B observes n echo signal, the impact that each sensing system can exist deviation and be subject to clutter noise, sensing system buggy model is set up as follows:

$A_i=X_i+G\left(P\right)+{\stackrel{\‾}{x}}_A;i=1...m$

$B_j=X_j+G\left(Q\right)+{\stackrel{\‾}{x}}_B;j=1...n$

A wherein _ifor i the echo signal that sensors A observes, B _jj the echo signal observing for transducer B,

for the physical location of target, the noise that G (v) is sensing system, its average is 0, and covariance matrix is v, and for sensing system A and B, its covariance matrix is respectively P and Q,

be respectively the deviation of sensing system A and B, the object of data correlation is to find the echo signal A that sensing system A and B observe _iand B _jbetween corresponding relation;

2, the relative position of echo signal

Suppose that sensing system A observes echo signal i and j, their state can be expressed as follows:

${\stackrel{\→}{X}}_{\mathrm{Ai}}={\stackrel{\→}{X}}_i+{\stackrel{\→}{B}}_A+{\stackrel{\→}{v}}_A$

${\stackrel{\→}{X}}_{\mathrm{Aj}}={\stackrel{\→}{X}}_j+{\stackrel{\→}{B}}_A+{\stackrel{\→}{v}}_A$

Wherein

be expressed as i the dbjective state that sensing system A observes, be expressed as j the dbjective state that sensing system A observes,

represent respectively the virtual condition of i target and j target,

the deviation that represents sensing system A,

the Gauss measurement noise that represents sensing system A, its average is 0, covariance matrix is $\left[\begin{array}{cc}{\mathrm{\σ}}_1^2& 0\\ 0& {\mathrm{\σ}}_1^2\end{array}\right],$ Wherein

represented noise variance;

I the dbjective state that sensing system A observes and the relative position of j dbjective state can be expressed as follows:

${\stackrel{\→}{X}}_{\mathrm{Aij}}={\stackrel{\→}{X}}_{\mathrm{Aj}}-{\stackrel{\→}{X}}_{\mathrm{Ai}}={\stackrel{\→}{X}}_j-{\stackrel{\→}{X}}_i+{\stackrel{\→}{v}}_A^{\′}={\stackrel{\→}{X}}_{\mathrm{ij}}+{\stackrel{\→}{v}}_A^{\′}$

It is relative position

can be expressed as the relative position of i target and j target reality

with noise

and, wherein

its average of Gauss measurement noise representing is 0, and covariance matrix is $\left[\begin{array}{cc}2{\mathrm{\σ}}_1^2& 0\\ 0& {2\mathrm{\σ}}_1^2\end{array}\right],$ Wherein

represented noise variance;

In like manner, sensing system B observes echo signal i and j, and their state can be expressed as follows:

${\stackrel{\→}{X}}_{\mathrm{Bi}}={\stackrel{\→}{X}}_i+{\stackrel{\→}{B}}_B+{\stackrel{\→}{v}}_B$

${\stackrel{\→}{X}}_{\mathrm{Bj}}={\stackrel{\→}{X}}_j+{\stackrel{\→}{B}}_B+{\stackrel{\→}{v}}_B$

Wherein

be expressed as i the dbjective state that sensing system B observes, be expressed as j the dbjective state that sensing system B observes,

represent respectively the virtual condition of i target and j target,

the deviation that represents sensing system B,

the Gauss measurement noise that represents sensing system B, its average is 0, covariance matrix is $\left[\begin{array}{cc}{\mathrm{\σ}}_2^2& 0\\ 0& {\mathrm{\σ}}_2^2\end{array}\right],$ Wherein represented noise

variance;

I the dbjective state that sensing system B observes and the relative position of j dbjective state can be expressed as follows:

${\stackrel{\→}{X}}_{\mathrm{Bij}}={\stackrel{\→}{X}}_{\mathrm{Bj}}-{\stackrel{\→}{X}}_{\mathrm{Bi}}={\stackrel{\→}{X}}_j-{\stackrel{\→}{X}}_i+{\stackrel{\→}{v}}_B^{\′}={\stackrel{\→}{X}}_{\mathrm{ij}}+{\stackrel{\→}{v}}_B^{\′}$

It is relative position

can be expressed as the relative position of i target and j target reality

with noise

and, wherein

its average of Gauss measurement noise representing is 0, and covariance matrix is $\left[\begin{array}{cc}{2\mathrm{\σ}}_2^2& 0\\ 0& 2{\mathrm{\σ}}_2^2\end{array}\right],$ Wherein represented noise

variance;

Relatively

with

can obtain:

${\stackrel{\→}{X}}_{\mathrm{Bij}}-{\stackrel{\→}{X}}_{\mathrm{Aij}}={\stackrel{\→}{v}}_B^{\′}-{\stackrel{\→}{v}}_A^{\′}={\stackrel{\→}{v}}^{\′\′}$

Above formula can obtain, and the difference of the relative position that corresponding target between different sensors system is right is just by Gaussian noise

determine, its average is 0, and covariance matrix is $\left[\begin{array}{cc}{2\mathrm{\σ}}_1^2+2{\mathrm{\σ}}_2^2& 0\\ 0& 2{\mathrm{\σ}}_1^2+2{\mathrm{\σ}}_2^2\end{array}\right],$ Wherein

represented noise

variance;

3, the relative position of different sensors system

Suppose that the target i that sensors A observes corresponds to the target a that transducer B observes, its state can be expressed as:

${\stackrel{\→}{X}}_{\mathrm{Ai}}={\stackrel{\→}{X}}_i+{\stackrel{\→}{B}}_A+{\stackrel{\→}{v}}_A$

${\stackrel{\→}{X}}_{\mathrm{Ba}}={\stackrel{\→}{X}}_a+{\stackrel{\→}{B}}_B+{\stackrel{\→}{v}}_B$

Wherein representative sensor A observed object signal i position,

represent target i physical location,

representative sensor A deviation, representative sensor A noise,

representative sensor B observed object signal a position, represent target a physical location,

representative sensor B deviation,

representative sensor B noise.

?

${\stackrel{\→}{X}}_{\mathrm{ia}}={\stackrel{\→}{X}}_{\mathrm{Ba}}-{\stackrel{\→}{X}}_{\mathrm{Ai}}={\stackrel{\→}{B}}_B-{\stackrel{\→}{B}}_A+{\stackrel{\→}{v}}_B-{\stackrel{\→}{v}}_A={\stackrel{\→}{R}}_b+v^{\′\′\′}$

Be the poor of the target i that matches of sensing system A and B and a

relative deviation for sensing system A and B

determine with Gaussian noise v''', the average of sensing system Gaussian noise v''' is 0, and covariance matrix is $\left[\begin{array}{cc}{\mathrm{\σ}}_1^2+{\mathrm{\σ}}_2^2& 0\\ 0& {\mathrm{\σ}}_1^2+{\mathrm{\σ}}_2^2\end{array}\right],$ Wherein represented the variance of noise v''';

4, the pairing algorithm of based target signal relative position

4.1, finding twin target signal match point and sensor bias estimates

Suppose sensing system A to observe m echo signal and sensing system B observes n target, from the echo signal of sensing system A observation, choose echo signal i, and the echo signal observing from sensing system B, choose echo signal a, the remaining m-1 of a calculating sensor system A echo signal j with the relative position of echo signal i and the remaining n-1 of a sensing system B echo signal b with the relative position of echo signal a and compare, when the comparison value of relative position || A _ij-B _ab||≤Δ ₁, A wherein _ijrepresent the relative distance between echo signal i and j, B _abrepresent the relative distance between echo signal a and b, Δ ₁represent threshold value 1, can think that j and b are the echo signal of pairing, and define a new Matrix C ntNum _iacalculate the echo signal j that may match and the number of b, by remaining n-1 the echo signal a of the B that once circulates, can obtain when different corresponding match point i-> a, may match the maximum number MaxCntNum of echo signal point _iait is Matchingpair that definition can be mated right number, because the quantity Matchingpair of the point that may match is necessarily less than or equal to the minimum value of the target number n that target number m that sensing system A observes and sensing system B observe, if the maximum number MaxCntNum that may match echo signal point finding _iaequal m-1, can think that the echo signal a that echo signal i that now corresponding sensing system A observes and sensing system A observe is match point, and the number that can mate right point is Matchingpair, can enter next step 4.2, otherwise the number of the explanation point that can match is less than m-1;

Start to choose echo signal i in the echo signal of circulatory system A observation, and repeat step above, suppose when being recycled to the echo signal i=c that sensors A observes, if may match the maximum number MaxCntNum of echo signal point _iabe less than m-c, now illustrate that match point number Matchingpair is necessarily less than m-c, reduce the number of the point that may match, and continue circulation; If the number of the point that can match equals m-c, the number Matchingpair of the explanation point that can match has found and has equaled m-c, storage match point i-> a now, enter next step 4.2;

4.2, find remaining echo signal match point

By 4.1 steps, found the point of first pair of pairing, can to match point, calculate a comparatively rough estimation of deviation by this, the echo signal now sensing system A being observed is by adding acquired estimation of deviation, can be mapped in the observation area of sensing system B, if be mapped to the some map (A in B _j) the echo signal B that observes with sensing system B _bmeet || map (A _j)-B _b||≤Δ ₂, map (A wherein _j) represent echo signal A _jbe mapped to the corresponding points in transducer B region, B _bthe echo signal that representative sensor B observes, Δ ₂represent threshold value 2, and B now _bunique, can think that j-> b is now match point, the match point j-> b that meets uniqueness by finding these, can calculate a more accurate estimation of deviation value, echo signal for remaining sensors A observation, by adding this more accurate estimation of deviation value, the echo signal of sensing system A observation is mapped in the observation area of sensing system B, search out the echo signal of the nearest B of same mapping point, can complete the pairing of left point, thereby complete whole association process;

5, thresholding determines

In above-mentioned steps, need to use threshold value Δ ₁, Δ ₂find match point, being therefore necessary to calculate suitable threshold value realizes whole association process;

In the process of above-mentioned steps 4, need to pass through threshold value Δ ₁carry out comparison relative distance A _ijand B _ij, by the analysis of step 2:

${\stackrel{\→}{X}}_{\mathrm{Bij}}-{\stackrel{\→}{X}}_{\mathrm{Aij}}={\stackrel{\→}{v}}_B^{\′}-{\stackrel{\→}{v}}_A^{\′}={\stackrel{\→}{v}}^{\′\′}$

Wherein represented transducer B observed object i and j relative position vector,

represented sensors A observed object i and j relative position vector,

represented the noise that B and A transducer relative position comprise.The difference of relative position of echo signal of the logical transducer B observation of relative position that is the echo signal of sensors A observation is a noise that meets Gaussian Profile, and its average is 0, and covariance matrix is $\left[\begin{array}{cc}{2\mathrm{\σ}}_1^2+2{\mathrm{\σ}}_2^2& 0\\ 0& 2{\mathrm{\σ}}_1^2+2{\mathrm{\σ}}_2^2\end{array}\right],$ Wherein

represented noise

variance, utilize the 3-σ principle of Gaussian Profile, select threshold value Δ ₁for

In the process of above-mentioned steps 4, need to pass through threshold value Δ ₂come the observed object signal of comparison sensors A with the mapping corresponding relation of transducer B observed object signal, by the analysis of step 2:

${\stackrel{\→}{X}}_{\mathrm{ia}}={\stackrel{\→}{X}}_{\mathrm{Ba}}-{\stackrel{\→}{X}}_{\mathrm{Ai}}={\stackrel{\→}{B}}_B-{\stackrel{\→}{B}}_A+{\stackrel{\→}{v}}_B-{\stackrel{\→}{v}}_A={\stackrel{\→}{R}}_b+{\stackrel{\→}{v}}^{\′\′\′}$

Wherein

represented the deviation between sensors A and transducer B, wherein

representative sensor A observed object signal i position,

representative sensor A deviation,

representative sensor A noise,

representative sensor B observed object signal a position,

representative sensor B deviation,

representative sensor B noise, differs a deviation between the echo signal that sensors A and transducer B observe

with remaining noise

remaining noise wherein

meeting Gaussian Profile and its average is 0, and covariance matrix is $\left[\begin{array}{cc}{\mathrm{\σ}}_1^2+{\mathrm{\σ}}_2^2& 0\\ 0& {\mathrm{\σ}}_1^2+{\mathrm{\σ}}_2^2\end{array}\right],$ Wherein

represented noise variance, utilize the 3-σ principle of Gaussian Profile, select threshold value Δ ₂for

6, relative deviation is estimated

After searching out a match point, by relative deviation, estimate to decide remaining coupling right, by the analysis of step 2, can see that the measured deviation of a match point i-> a can be expressed as follows:

${\stackrel{\→}{X}}_{\mathrm{ia}}={\stackrel{\→}{X}}_{\mathrm{Ba}}-{\stackrel{\→}{X}}_{\mathrm{Ai}}={\stackrel{\→}{R}}_b+{\stackrel{\→}{v}}^{\′\′\′}$

Wherein

represented the relative deviation between sensors A and transducer B observation signal,

represented the position of transducer B observed object signal a, represented the position of sensors A observed object signal i,

for the relative deviation between sensing system A and B,

for noise.

Suppose to have n coupling right, measured deviation is:

${\stackrel{\→}{X}}_{\mathrm{AB}}=H\×{\stackrel{\→}{R}}_b+{\stackrel{\→}{v}}_{(2n\×1)}$

Wherein ${\stackrel{\→}{X}}_{\mathrm{AB}}={[x_{(B1-A1)},y_{(B1-A1)},x_{(B2-A2)},y_{(B2-A2)}...x_{(\mathrm{Bn}-\mathrm{An})},y_{(\mathrm{Bn}-\mathrm{An})}]}^T(2n\×1)$ Represented n coupling between relative distance vector, $H={\left[\begin{array}{c}\mathrm{1,0,1,0},...\mathrm{1,0}\\ \mathrm{0,1,0,1},...\mathrm{0,1}\end{array}\right]}_{(2\×2n)}^T$ For observed differential matrix,

represent noise vector,

represented the relative deviation between different sensors that needs estimate;

Least-squares estimation is to minimize:

Wherein

represented error sum of squares,

represented the estimation to relative deviation, T represents transpose of a matrix.

By getting differential and making it, be 0, can obtain:

Wherein

represented the value of the relative deviation of least-squares estimation.

Transducer relative deviation

estimated value is:

${\hat{R}}_{\mathrm{bls}}={\left(H^{T}H\right)}^{-1}{H}^T{\stackrel{\→}{X}}_{\mathrm{AB}}.$

The advantage of technical solution of the present invention and good effect are:

1), by adopting the correlating method of based target relative position to reduce the impact of sensor bias for association results, by adopting the correlating method of based target relative position, utilize the intrinsic characteristic of echo signal relative position, effectively reduced the impact of sensor bias for association results, than conventional method, from performance, there is larger lifting.

2), be applicable to the pairing algorithm of the echo signal of multi-sensor data association, in the pairing algorithm of the echo signal point of multi-sensor data association, first by the corresponding echo signal point that circulates, find the logarithm of matched echo signal maximum between different sensors system, thereby search out a pair of reliable echo signal point, by this pair of echo signal, can estimate the relative deviation between sensing system, utilize the deviation of this estimation can obtain remaining match point, and calculate a relative deviation between comparatively accurate different sensors system.

3), provided the method for estimation of comparatively accurate transducer relative deviation, in the present invention, first by finding first pair of reliable match point, can obtain a comparatively coarse transducer relative deviation, recycle after this transducer relative deviation, can obtain remaining target pairing signal, final, can obtain by the method for least-squares estimation a comparatively accurate transducer relative deviation.

Accompanying drawing explanation

Fig. 1 is the sensor network data correlating method flow chart of a kind of based target signal of the present invention relative position pairing.

Embodiment

Below in conjunction with accompanying drawing and embodiment, further illustrate the present invention.

The present invention proposes a kind of method that compares the relative position between sensor measurement signal and extract relative position pattern by employing and obtain the right method of object matching corresponding to different sensors system, and provided a kind of improved matching algorithm of sensor target signal association and method of estimation of sensor bias of being applicable to.The method is set up sensor bias model, and provided between sensor target signal and the expression formula of the relative position between different sensors, by finding the maximum of relative position between different sensors, mate right method, can find echo signal match point corresponding to a pair of different sensors, and obtain a comparatively coarse estimation of deviation, by this estimation of deviation, can find the object matching pair between remaining sensor signal, complete the data correlation of different target signal and obtain a result for estimation of deviation comparatively accurately.Idiographic flow is as follows:

1, sensor bias model

Suppose two different sensing system A and B, in whole observation area, observe N target, wherein sensing system A observes m echo signal, and sensing system B observes n echo signal, the impact that each sensing system can exist deviation and be subject to clutter noise, sensing system buggy model is set up as follows:

$A_i=X_i+G\left(P\right)+{\stackrel{\‾}{x}}_A;i=1...m$

$B_j=X_j+G\left(Q\right)+{\stackrel{\‾}{x}}_B;j=1...n$

A wherein _ifor i the echo signal that sensors A observes, B _jj the echo signal observing for transducer B,

for the physical location of target, the noise that G (v) is sensing system, its average is 0, and covariance matrix is v, and for sensing system A and B, its covariance matrix is respectively P and Q,

be respectively the deviation of sensing system A and B.The object of data correlation is to find the echo signal A that sensing system A and B observe _iand B _jbetween corresponding relation.

2, the relative position of echo signal

Suppose that sensing system A observes echo signal i and j, their state can be expressed as follows:

${\stackrel{\→}{X}}_{\mathrm{Ai}}={\stackrel{\→}{X}}_i+{\stackrel{\→}{B}}_A+{\stackrel{\→}{v}}_A$

${\stackrel{\→}{X}}_{\mathrm{Aj}}={\stackrel{\→}{X}}_j+{\stackrel{\→}{B}}_A+{\stackrel{\→}{v}}_A$

Wherein

be expressed as i the dbjective state that sensing system A observes,

be expressed as j the dbjective state that sensing system A observes,

represent respectively the virtual condition of i target and j target,

the deviation that represents sensing system A,

the Gauss measurement noise that represents sensing system A, its average is 0, covariance matrix is $\left[\begin{array}{cc}{\mathrm{\σ}}_1^2& 0\\ 0& {\mathrm{\σ}}_1^2\end{array}\right],$ Wherein

represented noise

variance;

I the dbjective state that sensing system A observes and the relative position of j dbjective state can be expressed as follows:

${\stackrel{\→}{X}}_{\mathrm{Aij}}={\stackrel{\→}{X}}_{\mathrm{Aj}}-{\stackrel{\→}{X}}_{\mathrm{Ai}}={\stackrel{\→}{X}}_j-{\stackrel{\→}{X}}_i+{\stackrel{\→}{v}}_A^{\′}={\stackrel{\→}{X}}_{\mathrm{ij}}+{\stackrel{\→}{v}}_A^{\′}$

It is relative position can be expressed as the relative position of i target and j target reality

with noise

and, wherein

its average of Gauss measurement noise representing is 0, and covariance matrix is $\left[\begin{array}{cc}2{\mathrm{\σ}}_1^2& 0\\ 0& {2\mathrm{\σ}}_1^2\end{array}\right],$ Wherein

represented noise

variance;

In like manner, sensing system B observes echo signal i and j, and their state can be expressed as follows:

${\stackrel{\→}{X}}_{\mathrm{Bi}}={\stackrel{\→}{X}}_i+{\stackrel{\→}{B}}_B+{\stackrel{\→}{v}}_B$

${\stackrel{\→}{X}}_{\mathrm{Bj}}={\stackrel{\→}{X}}_j+{\stackrel{\→}{B}}_B+{\stackrel{\→}{v}}_B$

Wherein

be expressed as i the dbjective state that sensing system B observes,

be expressed as j the dbjective state that sensing system B observes,

represent respectively the virtual condition of i target and j target,

the deviation that represents sensing system B,

the Gauss measurement noise that represents sensing system B, its average is 0, covariance matrix is $\left[\begin{array}{cc}{\mathrm{\σ}}_2^2& 0\\ 0& {\mathrm{\σ}}_2^2\end{array}\right],$ Wherein

represented noise

variance;

I the dbjective state that sensing system B observes and the relative position of j dbjective state can be expressed as follows:

${\stackrel{\→}{X}}_{\mathrm{Bij}}={\stackrel{\→}{X}}_{\mathrm{Bj}}-{\stackrel{\→}{X}}_{\mathrm{Bi}}={\stackrel{\→}{X}}_j-{\stackrel{\→}{X}}_i+{\stackrel{\→}{v}}_B^{\′}={\stackrel{\→}{X}}_{\mathrm{ij}}+{\stackrel{\→}{v}}_B^{\′}$

It is relative position

can be expressed as the relative position of i target and j target reality with noise

and, wherein

its average of Gauss measurement noise representing is 0, and covariance matrix is $\left[\begin{array}{cc}{2\mathrm{\σ}}_2^2& 0\\ 0& {2\mathrm{\σ}}_2^2\end{array}\right]$ . wherein

represented noise

variance;

Relatively

with

can obtain:

${\stackrel{\→}{X}}_{\mathrm{Bij}}-{\stackrel{\→}{X}}_{\mathrm{Aij}}={\stackrel{\→}{v}}_B^{\′}-{\stackrel{\→}{v}}_A^{\′}={\stackrel{\→}{v}}^{\′\′}$

Above formula can obtain, and the difference of the relative position that corresponding target between different sensors system is right just determines by Gaussian noise v'', and its average is 0, and covariance matrix is $\left[\begin{array}{cc}{2\mathrm{\σ}}_1^2+2{\mathrm{\σ}}_2^2& 0\\ 0& 2{\mathrm{\σ}}_1^2+2{\mathrm{\σ}}_2^2\end{array}\right],$ Wherein

represented noise variance; .

3, the relative position of different sensors system

Suppose that the target i that sensors A observes corresponds to the target a that transducer B observes, its state can be expressed as:

${\stackrel{\→}{X}}_{\mathrm{Ai}}={\stackrel{\→}{X}}_i+{\stackrel{\→}{B}}_A+{\stackrel{\→}{v}}_A$

${\stackrel{\→}{X}}_{\mathrm{Ba}}={\stackrel{\→}{X}}_a+{\stackrel{\→}{B}}_B+{\stackrel{\→}{v}}_B$

Wherein

representative sensor A observed object signal i position,

represent target i physical location,

representative sensor A deviation, representative sensor A noise,

representative sensor B observed object signal a position,

represent target a physical location,

representative sensor B deviation,

representative sensor B noise.

?

${\stackrel{\→}{X}}_{\mathrm{ia}}={\stackrel{\→}{X}}_{\mathrm{Ba}}-{\stackrel{\→}{X}}_{\mathrm{Ai}}={\stackrel{\→}{B}}_B-{\stackrel{\→}{B}}_A+{\stackrel{\→}{v}}_B-{\stackrel{\→}{v}}_A={\stackrel{\→}{R}}_b+v^{\′\′\′}$

Be the poor of the target i that matches of sensing system A and B and a

relative deviation for sensing system A and B

determine with Gaussian noise v''', the average of sensing system Gaussian noise v''' is 0, and covariance matrix is $\left[\begin{array}{cc}{\mathrm{\σ}}_1^2+{\mathrm{\σ}}_2^2& 0\\ 0& {\mathrm{\σ}}_1^2+{\mathrm{\σ}}_2^2\end{array}\right],$ Wherein

represented noise

variance;

4, the pairing algorithm of based target signal relative position

4.1, finding twin target signal match point and sensor bias estimates

Suppose sensing system A to observe m echo signal and sensing system B observes n target.From the echo signal of sensing system A observation, choose echo signal i, and the echo signal observing from sensing system B, choose echo signal a, the remaining m-1 of a calculating sensor system A echo signal j with the relative position of echo signal i and the remaining n-1 of a sensing system B echo signal b with the relative position of echo signal a and compare, when the comparison value of relative position || A _ij-B _ab||≤Δ ₁(A wherein _ijrepresent the relative distance between echo signal i and j, B _abrepresent the relative distance between echo signal a and b, Δ ₁represent threshold value 1), can think that j and b are the echo signal of pairing, and define a new Matrix C ntNum _iacalculate the echo signal j that may match and the number of b.By remaining n-1 the echo signal a of the B that once circulates, can obtain when different corresponding match point i-> a, may match the maximum number MaxCntNum of echo signal point _ia.It is Matchingpair that definition can be mated right number, because the quantity Matchingpair of the point that may match is necessarily less than or equal to the minimum value of the target number n that target number m that sensing system A observes and sensing system B observe, if the maximum number MaxCntNum that may match echo signal point finding _iaequal m-1, can think that the echo signal a that echo signal i that now corresponding sensing system A observes and sensing system A observe is match point, and the number that can mate right point is Matchingpair, can enter next step 4.2, otherwise the number of the explanation point that can match is less than m-1.

Start to choose echo signal i in the echo signal of circulatory system A observation, and repeat step above, suppose when being recycled to the echo signal i=c that sensors A observes, if may match the maximum number MaxCntNum of echo signal point _iabe less than m-c, now illustrate that match point number Matchingpair is necessarily less than m-c, reduce the number of the point that may match, and continue circulation; If the number of the point that can match equals m-c, the number Matchingpair of the explanation point that can match has found and has equaled m-c, storage match point i-> a now, enter next step 4.2.

4.2, find remaining echo signal match point

By 4.1 steps, found the point of first pair of pairing, can to match point, calculate a comparatively rough estimation of deviation by this, the echo signal now sensing system A being observed is by adding acquired estimation of deviation, can be mapped in the observation area of sensing system B, if be mapped to the some map (A in B _j) the echo signal B that observes with sensing system B _bmeet || map (A _j)-B _b||≤Δ ₂(map (A wherein _j) represent echo signal A _jbe mapped to the corresponding points in transducer B region, B _bthe echo signal that representative sensor B observes, Δ ₂represent threshold value 2), and B now _bunique, can think that j-> b is now match point.The match point j-> b that meets uniqueness by finding these, can calculate a more accurate estimation of deviation value.Echo signal for remaining sensors A observation, by adding this more accurate estimation of deviation value, the echo signal of sensing system A observation is mapped in the observation area of sensing system B, search out the echo signal of the nearest B of same mapping point, can complete the pairing of left point, thereby complete whole association process.

5, thresholding determines

In above-mentioned steps, need to use threshold value Δ ₁, Δ ₂find match point, being therefore necessary to calculate suitable threshold value realizes whole association process.

In the process of above-mentioned steps 4, need to pass through threshold value Δ ₁carry out comparison relative distance A _ijand B _ij, by the analysis of step 2:

${\stackrel{\→}{X}}_{\mathrm{Bij}}-{\stackrel{\→}{X}}_{\mathrm{Aij}}={\stackrel{\→}{v}}_B^{\′}-{\stackrel{\→}{v}}_A^{\′}={\stackrel{\→}{v}}^{\′\′}$

Wherein

represented transducer B observed object i and j relative position vector,

represented sensors A observed object i and j relative position vector, represented the noise that B and A transducer relative position comprise.The difference of relative position of echo signal of the logical transducer B observation of relative position that is the echo signal of sensors A observation is a noise that meets Gaussian Profile, and its average is 0, and covariance matrix is $\left[\begin{array}{cc}{2\mathrm{\σ}}_1^2+2{\mathrm{\σ}}_2^2& 0\\ 0& 2{\mathrm{\σ}}_1^2+2{\mathrm{\σ}}_2^2\end{array}\right],$ Wherein

represented noise

variance.Utilize the 3-σ principle of Gaussian Profile, select threshold value Δ ₁for

In the process of above-mentioned steps 4, need to pass through threshold value Δ ₂come the observed object signal of comparison sensors A with the mapping corresponding relation of transducer B observed object signal, by the analysis of step 2:

${\stackrel{\→}{X}}_{\mathrm{ia}}={\stackrel{\→}{X}}_{\mathrm{Ba}}-{\stackrel{\→}{X}}_{\mathrm{Ai}}={\stackrel{\→}{B}}_B-{\stackrel{\→}{B}}_A+{\stackrel{\→}{v}}_B-{\stackrel{\→}{v}}_A={\stackrel{\→}{R}}_b+{\stackrel{\→}{v}}^{\′\′\′}$

Wherein

represented the deviation between sensors A and transducer B, wherein

representative sensor A observed object signal i position,

represent target i physical location,

representative sensor A deviation,

representative sensor A noise,

representative sensor B observed object signal a position, represent target a physical location,

representative sensor B deviation,

representative sensor B noise, differs a deviation between the echo signal that sensors A and transducer B observe

with remaining noise

remaining noise wherein

meeting Gaussian Profile and its average is 0, and covariance matrix is $\left[\begin{array}{cc}{\mathrm{\σ}}_1^2+{\mathrm{\σ}}_2^2& 0\\ 0& {\mathrm{\σ}}_1^2+{\mathrm{\σ}}_2^2\end{array}\right],$ Wherein represented noise

variance.Utilize the 3-σ principle of Gaussian Profile, select threshold value Δ ₂for

6, relative deviation is estimated

It is a basic problem that relative deviation is estimated in whole data correlation process.After searching out a match point, by relative deviation, estimate to decide remaining coupling right.By the analysis of step 2, can see that the measured deviation of a match point i-> a can be expressed as follows:

${\stackrel{\→}{X}}_{\mathrm{ia}}={\stackrel{\→}{X}}_{\mathrm{Ba}}-{\stackrel{\→}{X}}_{\mathrm{Ai}}={\stackrel{\→}{R}}_b+{\stackrel{\→}{v}}^{\′\′\′}$

Wherein represented the relative deviation between sensors A and transducer B observation signal,

represented the position of transducer B observed object signal a,

represented the position of sensors A observed object signal i,

for the relative deviation between sensing system A and B,

for noise.

Suppose to have n coupling right, measured deviation is:

${\stackrel{\→}{X}}_{\mathrm{AB}}=H\×{\stackrel{\→}{R}}_b+{\stackrel{\→}{v}}_{(2n\×1)}$

Wherein ${\stackrel{\→}{X}}_{\mathrm{AB}}={[x_{(B1-A1)},y_{(B1-A1)},x_{(B2-A2)},y_{(B2-A2)}...x_{(\mathrm{Bn}-\mathrm{An})},y_{(\mathrm{Bn}-\mathrm{An})}]}^T(2n\×1)$ Represented n coupling between relative distance vector, $H={\left[\begin{array}{c}\mathrm{1,0,1,0},...\mathrm{1,0}\\ \mathrm{0,1,0,1},...\mathrm{0,1}\end{array}\right]}_{(2\×2n)}^T$ For observed differential matrix,

represent noise vector,

represented the relative deviation between different sensors that needs estimate.

Least-squares estimation (referring to document [8] Kay, S.M.Fundamentals of Statistical Signal Processing:Estimation Theory.Upper Saddle River, NJ:Prentice-Hall, 1998.) is to minimize:

Wherein

represented error sum of squares,

represented the estimation to relative deviation, T represents transpose of a matrix.

By getting differential and making it, be 0, can obtain:

Wherein

represented the value of the relative deviation of least-squares estimation.

Transducer relative deviation

estimated value is:

${\hat{R}}_{\mathrm{bls}}={\left(H^{T}H\right)}^{-1}{H}^T{\stackrel{\→}{X}}_{\mathrm{AB}}$

7, algorithm flow of the present invention

Algorithm flow of the present invention is as follows:

STEP1): obtain and storage sensor A and B observed object signal condition;

STEP2): from sensors A, choose echo signal Ai, from transducer B, choose echo signal Ba;

STEP3): by traversal transducer B echo signal Ba, calculate relative position and find corresponding match point by comparing the relative position of different sensors system, the maximum pairing number MaxCntNum that calculating may be matched _ia;

STEP4): if possible maximum pairing number MaxCntNum _ia< m-i, needs to return Step2), and choose new sensing system A echo signal Ai and repeat Step3) step, otherwise find match point i->a, and enter Step5) step;

STEP5): find match point i->a, calculating sensor relative deviation;

STEP6): by mapping, find remaining match point;

STEP7): find all match points, calculating sensor relative deviation, completes data correlation process.

Claims (1)

1. the sensor network data correlating method of based target signal relative position pairing, it is characterized in that, the method is set up sensor bias model, and provided between sensor target signal and the expression formula of the relative position between different sensors, by finding the maximum of relative position between different sensors, mate right method, can find echo signal match point corresponding to a pair of different sensors, and obtain a comparatively coarse estimation of deviation, by this estimation of deviation, can find the object matching pair between remaining sensor signal, complete the data correlation of different target signal and obtain a result for estimation of deviation comparatively accurately, idiographic flow is as follows:

(1), sensor bias model

Suppose two different sensing system A and B, in whole observation area, observe N target, wherein sensing system A observes m echo signal, and sensing system B observes n echo signal, the impact that each sensing system can exist deviation and be subject to clutter noise, sensing system buggy model is set up as follows:

A wherein _ifor i the echo signal that sensors A observes, B _jfor j the echo signal that transducer B observes, X _i,jfor the physical location of target, the noise that G (v) is sensing system, its average is 0, and covariance matrix is v, and for sensing system A and B, its covariance matrix is respectively P and Q,

be respectively the deviation of sensing system A and B, the object of data correlation is to find the echo signal A that sensing system A and B observe _iand B _jbetween corresponding relation;

(2), the relative position of echo signal

Suppose that sensing system A observes echo signal i and j, their state can be expressed as follows:

Wherein

be expressed as i the dbjective state that sensing system A observes,

be expressed as j the dbjective state that sensing system A observes,

represent respectively the virtual condition of i target and j target,

the deviation that represents sensing system A,

the Gauss measurement noise that represents sensing system A, its average is 0, covariance matrix is

wherein represented noise

variance;

I the dbjective state that sensing system A observes and the relative position of j dbjective state can be expressed as follows:

It is relative position can be expressed as the relative position of i target and j target reality

with noise

and, wherein its average of Gauss measurement noise representing is 0, and covariance matrix is

wherein

represented noise

variance;

In like manner, sensing system B observes echo signal i and j, and their state can be expressed as follows:

Wherein

be expressed as i the dbjective state that sensing system B observes,

be expressed as j the dbjective state that sensing system B observes,

represent respectively the virtual condition of i target and j target,

the deviation that represents sensing system B, the Gauss measurement noise that represents sensing system B, its average is 0, covariance matrix is

wherein

represented noise

variance;

I the dbjective state that sensing system B observes and the relative position of j dbjective state can be expressed as follows:

It is relative position can be expressed as the relative position of i target and j target reality with noise

and, wherein

its average of Gauss measurement noise representing is 0, and covariance matrix is wherein

represented noise

variance;

Relatively

with can obtain:

Above formula can obtain, and the difference of the relative position that corresponding target between different sensors system is right just determines by Gaussian noise v'', and its average is 0, and covariance matrix is

wherein

represented noise

variance;

(3), the relative position of different sensors system

Suppose that the target i that sensors A observes corresponds to the target a that transducer B observes, its state can be expressed as:

Wherein

representative sensor A observed object signal i position,

represent target i physical location, representative sensor A deviation,

representative sensor A noise,

representative sensor B observed object signal a position,

represent target a physical location,

representative sensor B deviation,

representative sensor B noise;

?

Be the poor of the target i that matches of sensing system A and B and a

relative deviation for sensing system A and B

determine with Gaussian noise v''', the average of sensing system Gaussian noise v''' is 0, and covariance matrix is

wherein

represented the variance of noise v''';

(4), the pairing algorithm of based target signal relative position

4.1, finding twin target signal match point and sensor bias estimates

Suppose sensing system A to observe m echo signal and sensing system B observes n target, from the echo signal of sensing system A observation, choose echo signal i, and the echo signal observing from sensing system B, choose echo signal a, the remaining m-1 of a calculating sensor system A echo signal j with the relative position of echo signal i and the remaining n-1 of a sensing system B echo signal b with the relative position of echo signal a and compare, when the comparison value of relative position || A _ij-B _ab||≤Δ ₁, A wherein _ijrepresent the relative distance between echo signal i and j, B _abrepresent the relative distance between echo signal a and b, Δ ₁represent threshold value 1, can think that j and b are the echo signal of pairing, and define a new Matrix C ntNum _iacalculate the echo signal j that may match and the number of b, by remaining n-1 the echo signal a of the B that once circulates, can obtain when different corresponding match point i-> a, may match the maximum number MaxCntNum of echo signal point _iait is Matchingpair that definition can be mated right number, because the quantity Matchingpair of the point that may match is necessarily less than or equal to the minimum value of the target number n that target number m that sensing system A observes and sensing system B observe, if the maximum number MaxCntNum that may match echo signal point finding _iaequal m-1, can think that the echo signal a that echo signal i that now corresponding sensing system A observes and sensing system A observe is match point, and the number that can mate right point is Matchingpair, can enter next step 4.2, otherwise the number of the explanation point that can match is less than m-1;

Start to choose echo signal i in the echo signal of circulatory system A observation, and repeat step above, suppose when being recycled to the echo signal i=c that sensors A observes, if may match the maximum number MaxCntNum of echo signal point _iabe less than m-c, now illustrate that match point number Matchingpair is necessarily less than m-c, reduce the number of the point that may match, and continue circulation; If the number of the point that can match equals m-c, the number Matchingpair of the explanation point that can match has found and has equaled m-c, storage match point i-> a now, enter next step 4.2;

4.2, find remaining echo signal match point

By 4.1 steps, found the point of first pair of pairing, can to match point, calculate a comparatively rough estimation of deviation by this, the echo signal now sensing system A being observed is by adding acquired estimation of deviation, can be mapped in the observation area of sensing system B, if be mapped to the some map (A in B _j) the echo signal Bb that observes with sensing system B meet || map (A _j)-B _b||≤Δ ₂(map (A wherein _j) represent echo signal A _jbe mapped to the corresponding points in transducer B region, B _bthe echo signal that representative sensor B observes, Δ ₂represent threshold value 2, and B now _bunique, can think that j-> b is now match point, the match point j-> b that meets uniqueness by finding these, can calculate a more accurate estimation of deviation value, echo signal for remaining sensors A observation, by adding this more accurate estimation of deviation value, the echo signal of sensing system A observation is mapped in the observation area of sensing system B, search out the echo signal of the nearest B of same mapping point, can complete the pairing of left point, thereby complete whole association process;

(5), thresholding determines

In above-mentioned steps, need to use threshold value Δ ₁, Δ ₂find match point, being therefore necessary to calculate suitable threshold value realizes whole association process;

In the process of above-mentioned steps 4, need to pass through threshold value Δ ₁carry out comparison relative distance A _ijand B _ij, by the analysis of step 2:

Wherein

represented transducer B observed object i and j relative position vector,

represented sensors A observed object i and j relative position vector, represented the noise that B and A transducer relative position comprise, i.e. the difference of the relative position of the echo signal of the logical transducer B observation of relative position of the echo signal of sensors A observation is a noise that meets Gaussian Profile

its average is 0, and covariance matrix is

wherein

represented noise variance, utilize the 3-σ principle of Gaussian Profile, select threshold value Δ ₁for

In the process of above-mentioned steps 4, need to pass through threshold value Δ ₂come the observed object signal of comparison sensors A with the mapping corresponding relation of transducer B observed object signal, by the analysis of step 2:

Wherein

represented the deviation between sensors A and transducer B, wherein

representative sensor A observed object signal i position, representative sensor A deviation,

representative sensor A noise, representative sensor B observed object signal a position,

representative sensor B deviation,

representative sensor B noise, differs a deviation between the echo signal that sensors A and transducer B observe

with remaining noise

remaining noise wherein meeting Gaussian Profile and its average is 0, and covariance matrix is

wherein represented noise

variance, utilize the 3-σ principle of Gaussian Profile, select threshold value Δ ₂for

(6), relative deviation is estimated

After searching out a match point, by relative deviation, estimate to decide remaining coupling right, by the analysis of step 2, can see that the measured deviation of a match point i-> a can be expressed as follows:

Wherein represented the relative deviation between sensors A and transducer B observation signal,

represented the position of transducer B observed object signal a,

represented the position of sensors A observed object signal i,

for the relative deviation between sensing system A and B,

for noise;

Suppose to have n coupling right, measured deviation is:

Wherein represented n coupling between relative distance vector,

for observed differential matrix,

represent noise vector,

represented the relative deviation between different sensors that needs estimate;

Least-squares estimation is to minimize:

Wherein

represented error sum of squares,

represented the estimation to relative deviation, T represents transpose of a matrix;

By getting differential and making it, be 0, can obtain:

Wherein

represented the value of the relative deviation of least-squares estimation;

Transducer relative deviation

estimated value is:

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