CN104155650B - A kind of method for tracking target based on entropy weight method point mark quality evaluation - Google Patents

A kind of method for tracking target based on entropy weight method point mark quality evaluation Download PDF

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CN104155650B
CN104155650B CN201410401795.7A CN201410401795A CN104155650B CN 104155650 B CN104155650 B CN 104155650B CN 201410401795 A CN201410401795 A CN 201410401795A CN 104155650 B CN104155650 B CN 104155650B
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target
value
mennei
delta
echo
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CN104155650A (en
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孔令讲
李雯雯
吴健
刘羽锐
易伟
崔国龙
李溯琪
杨建宇
杨晓波
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University of Electronic Science and Technology of China
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66Radar-tracking systems; Analogous systems
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/414Discriminating targets with respect to background clutter

Abstract

This invention relates to a kind of nearest-neighbor method for tracking target based on a mark quality evaluation, belongs to radar target tracking technical field, particularly to the method for tracking target of a kind of many evaluation indexes information fusion based on entropy weight.The present invention proposes and improves nearest-neighbor degree of association method according to the various features comprehensive assessment of some mark.When data association, the point mark falling into Bo Mennei is considered position, amplitude, Doppler, operating frequency four kinds measurement index, reference prediction value or prior information obtain the assessed value under each index, entropy weight method is used to determine weights, obtain comprehensive assessment value, utilize optimum measurement in quality evaluation to replace traditional method updates as flight path from the measurement that future position distance is minimum.Thus improve sensor information utilization rate, improve weak target tracking performance under complex environment.

Description

A kind of method for tracking target based on entropy weight method point mark quality evaluation
Technical field
The invention belongs to radar target tracking technical field, particularly to a kind of many evaluation indexes information based on entropy weight The method for tracking target merged.
Technical background
At present, the strong clutter that formed due to the mountain region in radar surveillance scene, city, ocean etc., strong noise background dry Disturbing, motor-driven, the stealthy impact of target itself, radar target tracking technology is faced with huge challenge.Under complex background, have A little clutters are all similar to target in time domain or frequency domain, and CFAR detection technology is difficult to effectively distinguish clutter and target, Substantial amounts of False Intersection Points mark brings huge challenge to rear end target following, data association.
The core of Target Tracking Problem is data association.Common data correlation method mainly has arest neighbors method (NN) and probability data correlation method (PDA) etc., they are all to utilize sensor to obtain and the dbjective state vector direct phase of calculating The information closed, such as document " Tracking in clutter with nearest neighbor filters:Analysis And performance.IEEE Trans.Aerospace Electron.Syst.32:995-1010 " propose one recently Adjacent data association algorithm, updates flight path with the point nearest from predictive value statistical distance, and it is little and to miscellaneous that this algorithm has amount of calculation The feature that ripple changes in distribution robustness is good, but in the environment that clutter density is higher, only consider that positional information makees hard decision, very may be used The situation missing with or losing target can be caused.It is true that sensor is not only the positional information of acquisition point mark, it is also possible to obtain More about the further feature data of target.During passive multiple target tracking, generally it is used directly for calculating target The metric data of state includes direction of arrival of signal (DOA), time of arrival (toa) (TOA) and Doppler frequency, amplitude etc..Nothing Source location system obtainable radiation source characteristic includes: polarization phases, operating frequency, pulsewidth, pulse amplitude, pulse repetition period Deng.Document " Wang Jiegui, Luo Jingqing. Passive Tracking [J] based on the association of multiple target multicharacteristic information fused data. electronics Journal, 2004,32 (6): 1013-1016.2004 (06) " in, make use of the frequency of target, pulsewidth, pulse to repeat when following the tracks of Three kinds of knowledge assistance features of cycle, and regard different evidence sources respectively as, utilize Dempste-Shafer evidence theory to carry out many Feature fusion, obtains each effectively observation and degree of association of real goal, but the method does not take into full account between feature Mutual relation, information synthesis weights distribution time " being determined on a case-by-case basis ", i.e. use experience weights, so cause in reality With certain subjectivity in application, even same operator, in different time and environment, same target is also tended to Go out inconsistent subjective judgment.This necessarily make information building-up process with getting sth into one's head property significantly so that information close The credibility becoming result declines, it is difficult to ensure tracking performance, lacks versatility.
Summary of the invention
It is an object of the invention to the weak point Curve guide impeller for background technology a kind of based on entropy weight method point mark quality The method for tracking target of assessment, the method, on the basis of nearest-neighbor, initially sets up assessment models, utilizes the entropy weight in operational research Value method carries out comprehensive assessment to the many indexes of Bo Mennei point, updates flight path by quality evaluation optimum point, thus improves sensing Device information utilization, improves weak target tracking performance under complex environment.
A kind of method for tracking target based on entropy weight method point mark quality evaluation of the present invention, the method includes:
Step 1, initialized target state X (k) and target covariance matrix P (k), k=0;
The state transition equation of target is:
X (k+1)=FX (k)+ω (k)
Wherein, F is state-transition matrix, and ω (k) is the Gaussian process noise of zero-mean, and its covariance matrix is Q, measures Model is:
Z (k)=HX (k)+v (k)
Z (k) is that target measures, and H is observing matrix, and v (k) is the Gauss observation noise of zero-mean, and its covariance matrix is R;
Step 2, according to state X (k-1) of k-1 moment target t and covariance matrix P (k-1) thereof, obtain k moment target t Status predication value:
X (k | k-1)=FX (k-1)
Measurement predictor:
Z (k | k-1)=HX (k | k-1)
Covariance one-step prediction value P (k | k-1):
P (k | k-1)=FP (k-1) FT+Q;
Step 3, reading kth frame position measurement set from radar receiverziK () is the k moment I-th measures, and meanwhile, and obtains kth frame measurement amplitude setDoppler ensembles Operating frequency setAnd corresponding three knowledge assistances of each position quantity measured value measure index a (k), d (k), f (k),
Whether amount of calculation measured value z (k) meets following formula, if meeting, as candidate's echo, and adds up the echo meeting requirement Number,
[z(k)-Z(k|k-1)]TS-1(k)×[z(k)-Z(k|k-1)]≤γ
Wherein S (k) is new breath covariance S (k)=HP (k | the k-1) H in k momentT+ R, γ are that ripple door set in advance is big Little;
Step 4, according to step 3 statistics obtain falling into the candidate target number of Bo Mennei, update targetpath,
If candidate's echo number is zero, does not the most measure and fall into relevant Bo Mennei, update flight path with the predictive value of step 2, Proceed to step 8.1;
If falling into the measuring value only one of which of relevant Bo Mennei, then this measuring value can be directly used for flight path renewal, proceeds to step Rapid 8.2;
If there being more than one echo to fall into the relevant Bo Mennei of tracked target, then Bo Mennei point mark is carried out quality and comment Estimate, choose optimum point, proceed to step 5;
Step 5, calculate the locus assessed value of each candidate's echo, amplitude assessed value, Doppler evaluation value, operating frequency Assessed value, obtains the assessment vector under each evaluation index, then assessment Vector Groups is combined into an evaluating matrix A;
Step 6, calculate the entropy assessment weights of every evaluation index, form weight vector;
Step 6.1 sets xijFor the element in a mark mass matrix A, i=1,2,3 ..., mk, mkFor falling into having of Bo Mennei Effect measures total number, j=1, and 2,3,4 represent four respectively measures index, then carries out data translation, i.e. uses xij+ 1 as translation After xij, because utilizing entropy to calculate with logarithm when seeking flexible strategy, carry out when data translation can be avoided taking the logarithm meaningless;
Step 6.2 calculates the proportion that under jth item index, the value of the i-th factor is shared in this index
Step 6.3 calculates the entropy of jth item index
Step 6.4 calculates the coefficient of variation of jth item index:
Step 6.5 calculates weightsObtain four evaluation index weight vectors;
The weight vector that step 7, evaluating matrix step 5 obtained obtain with step 6 is multiplied, and obtains final mass assessment Value V=Aw, finds the position i that maximum in assessed value V is correspondingop, obtain Bo Mennei optimum and measure zop(k);
Step 8, calculate filtering gain matrix K (k)=P (k | the k-1) H (k) S (k) of target t-1, obtain state and update expression Formula x (k | k), carry out flight path renewal;
If step 8.1 candidate's echo number is zero, does not the most measure and fall into relevant Bo Mennei, utilize future position to update boat Mark;
X (k | k)=x (k | k-1)
P (k | k)=P (k | k-1)
If step 8.2 falls into the measuring value only one of which of relevant Bo Mennei, then this measuring value can be directly used for flight path renewal;
X (k | k)=x (k | k-1)+K (k) [z (k)-HX (k | k-1)]
P (k | k)=P (k | k-1)-K (k) HP (k | k-1)
Step 8.3 Ruo Bomennei has multiple point, then choose and update flight path by optimum point after a mark quality evaluation
X (k | k)=x (k | k-1)+K (k) [zop(k)-Z(k|k-1)]
P (k | k)=P (k | k-1)-K (k) HP (k | k-1).
Concretely comprising the following steps of described step 5:
Step 5.1 calculates locus assessed value:
M within k moment, ripple doorkIndividual effective status measures as zi(k), i=1,2,3 ..., mk, the filtering of i-th state measurement Residual vector is vi(k)=zi(k)-Z (k | k-1), it with the statistical distance of predicted position isI-th The locus assessed value of individual effective measurement is:
m s ( i ) = { m i n ( D 1 , D 2 , ... , D m k ) D i , D i ≠ 0 1 D i = 0 , i = 1 , 2 , 3 , ... , m k ;
Step 5.2 calculates amplitude assessed value:
M within k moment, ripple doorkIndividual effective observation amplitude information is ai(k), i=1,2,3 ..., mk, definition amplitude assessment Value ma:
m a ( a i ( k ) ) = p 1 T ( a i ( k ) ) p 1 T ( a i ( k ) ) + p 0 T ( a i ( k ) )
Wherein,p0When () echo-signal comes solely from noise or clutter a The probability density function of amplitude, p1A () is echo-signal probability density function when comprising echo signal, T is detection threshold, pfa、pdIt is respectively false-alarm probability, detection probability;
Step 5.3 calculates Doppler evaluation value:
M within k moment, ripple doorkThe doppler information of individual effective observation is di(k), i=1,2,3 ..., mkIf, kth-1 During secondary sampling, corresponding to this tracking gate, the Doppler frequency observation of target is d (k-1), note observation Doppler difference Δ di(k) =| di(k)-d (k-1) |, the assessed value of Doppler frequency is designated as md:
m d ( i ) = m i n ( Δd 1 , Δd 2 , ... , Δd m k ) Δd i , Δd i ≠ 0 1 , Δd i = 0 ;
The assessed value of step 5.4 operating frequency:
M within k moment, ripple doorkIndividual effective observation working frequency information is fi(k), i=1,2,3 ..., mkIf, kth-1 During secondary sampling, corresponding to this tracking gate, the operating frequency observation of target is f (k-1), remembers Δ fi(k)=| fi(k)-f(k-1) |, for observation frequency difference, the assessed value of operating frequency is designated as mf, only considering the target of fixed frequency FIX type, i-th is effectively observed MfFor:
m f ( i ) = 1 , Δf i ≤ ϵ f ( Δf i - 2 ϵ f ) 2 ϵf 2 , ϵ f ≤ Δf i ≤ 2 ϵ f 0 , Δf i ≥ 2 ϵ f
Wherein ε f is by frequency measurement tolerance determined by system noise and measurement noise;
Step 5.5 is by step 5.1,5.2,5.3,5.4 each evaluation index composition point the mark quality evaluation matrix A, i.e. A obtained =[ms;ma;md;mf]T
A kind of method for tracking target based on entropy weight method point mark quality evaluation of the present invention, utilizes information of forecasting from each Characteristic angle is analyzed this mark and is derived from the probability of target, and uses the comprehensive estimation method entropy assessment in operational research to determine The weights of multi objective assessment, and then a mark quality is had objective comprehensive assessment, sequence, utilize optimal amount in some mark quality evaluation Measure and update flight path for nearest-neighbor method measures recently from future position, thus the Evaluation Model on Quality with foundation is applicable to Multiple different knowledge assistance information fusion, solution procedure is simple, improves sensor information utilization rate, improve weak signal target with The effect of track performance.
Accompanying drawing explanation
Fig. 1 is the flow chart of the present invention;
Fig. 2 is the present invention and the tradition nearest-neighbor track algorithm schematic diagram when data association;
Fig. 3 is at PfaAfter improving when=0.01, nearest-neighbor is followed the tracks of and is followed the tracks of Monte Carlo 200 times all with tradition nearest-neighbor Side's curve of error correlation curve figure.
Detailed description of the invention
The present invention mainly uses the method for Computer Simulation to verify, institute is in steps, conclusion is all at MATLAB-R2010b Upper checking is correct.It is embodied as step as follows:
Step 1, input flight path, and calculate the predictive value in k moment:
Consider a linear uniform motion target in rectangular coordinate system, if the original state of target be X (0)=[80m, 6m/s, 100m, 0m/s], flight path front cross frame is the most initial.Design parameter is arranged such as table 1, table 2.In the k moment, obtain the state of target Predictive value X (k | k-1): X (k | k-1)=FX (k-1), and covariance one-step prediction value P (k | k-1): P (k | k-1)=FP (k-1) FT+Q。
Wherein
Step 2, calculating fall into candidate's echo of Bo Mennei, and add up its number:
The set of kth frame adjustment location is read from radar receiverziK () is the i-th amount in k moment Survey, meanwhile, also obtain kth frame and measure amplitude setDoppler ensemblesWork Frequency sets
When aim parameter measured value z (k) meets formula [z (k)-Z (k | k-1)]TS-1During (k) × [z (k)-Z (k | k-1)]≤γ, then As candidate's echo, the number of statistics candidate's echo;
If 2.1 candidate's echo numbers are zero, the most do not measure and fall into relevant Bo Mennei, update flight path with future position, proceed to Step 6.1;
If the 2.2 measuring value only one of which falling into relevant Bo Mennei, then this measuring value can be directly used for flight path renewal, proceeds to Step 6.2;
If 2.3 have more than one echo to fall into the relevant Bo Mennei of tracked target, then Bo Mennei point mark is carried out matter Amount assessment, chooses optimum point, proceeds to step 3
Step 3, the foundation of assessment models, the assessment of feature:
The assessed value of 3.1 locus
In the k moment, the effective observer state of Bo Mennei measures as zi(k), i=1,2,3 ..., mk, the state of i-th observation measures Filtering residuals vector be vi(k)=zi(k)-xi(k | k-1), itself and the statistical distance of predicted position The quantity of state test and appraisal valuation that definition i-th is effectively observed is:
m s ( i ) = { m i n ( D 1 , D 2 , ... , D m k ) D i , D i ≠ 0 1 , D i = 0 , i = 1 , 2 , 3 , ... , m k .
The assessed value of 3.2 amplitudes
M within k moment, ripple doorkIndividual effective observation amplitude information is ai(k), i=1,2,3 ..., mk.Definition amplitude index Under assessed value ma,Wherein, WhereinWherein σ2The power spectrum of multiple Gaussian noise Density,For the power spectral density of multiple gaussian signal, and signal to noise ratio snr=10lg (σs 22)。
The assessed value of 3.3 Doppler
In the k moment, the doppler information that Bo Mennei effectively observes is di(k), i=1,2,3 ..., mkIf kth is adopted for-1 time During sample, the Doppler frequency observation of target corresponding to this tracking gate is d (k-1), note observation Doppler difference Δ di(k)=| di (k)-d (k-1) |, the assessed value of definition Doppler frequency is designated as md:
m d ( i ) = m i n ( Δd 1 , Δd 2 , ... , Δd m k ) Δd i , Δd i ≠ 0 1 , Δd i = 0
The assessed value of 3.4 operating frequencies
In the k moment, it is f that Bo Mennei effectively observes working frequency informationi(k), i=1,2,3 ..., mk.If kth is adopted for-1 time During sample, the operating frequency observation of target corresponding to this tracking gate is f (k-1), remembers Δ fi(k)=| fi(k)-f (k-1) |, for seeing Frequency measurement is poor.The assessed value of definition operating frequency is designated as mf, only considering the target of fixed frequency (FIX) type, i-th is effectively observed MfIt is defined as:
m f ( i ) = 1 , Δf i ≤ ϵ f ( Δf i - 2 ϵ f ) 2 ϵf 2 , ϵf i ≤ Δ f ≤ 2 ϵ f 0 , Δf i ≥ 2 ϵ f
Wherein ε f is by frequency measurement tolerance determined by system noise and measurement noise.
3.5 by step 3.1,3.2,3.3,3.4 each evaluation index composition point the mark quality evaluation matrix A, i.e. A=obtained [ms;ma;md;mf]T
Step 4, the calculating of entropy assessment weights:
4.1 set xijFor element in a mark mass matrix A, i=1,2,3 ..., mk, j=1,2,3,4, because utilizing entropy to ask Calculate with logarithm during flexible strategy, carry out data translation xij=xij+ 1, meaningless when can avoid taking the logarithm;
4.2 calculate the value of the i-th factor under jth item index herein means proportion shared in mark
4.3 entropy calculating jth item index
4.4 coefficients of variation calculating jth item index.For jth item index, desired value xijDifference the biggest, entropy is more Little.The definition of difference value coefficient:
4.5 seek weightsObtain weight vector.
Step 5, comprehensive quality are assessed
Assessed value matrix is multiplied with weight vector, obtains final mass assessed value V=Aw.Find in assessed value V maximum The position i that element is correspondingop, obtain Bo Mennei optimum and measure zop(k)。
Step 6, flight path update
Filtering gain matrix K (k) of calculating target t, and K (k)=P (k | k-1) H (k) S (k)-1, obtain state and update expression Formula x (k | k):
If 6.1 candidate's echo numbers are zero, the most do not measure and fall into relevant Bo Mennei, utilize future position to update flight path
X (k | k)=x (k | k-1)
P (k | k)=P (k | k-1)
If the 6.2 measuring value only one of which falling into relevant Bo Mennei, then this measuring value can be directly used for flight path renewal
X (k | k)=x (k | k-1)+K (z) [z (k)-HX (k | k-1)]
P (k | k)=P (k | k-1)-K (k) HP (k | k-1)
6.3 Ruo Bomennei have multiple point, then choose and update flight path by optimum point after a mark quality evaluation
X (k | k)=x (k | k-1)+K (z) [zop(k)-Hx(k|k-1)]
P (k | k)=P (k | k-1)-K (k) HP (k | k-1)
6.4 repeat to return step 1, terminate until following the tracks of.
Table 1 basic parameter table
Table 2 knowledge assistance parameter list

Claims (2)

1. a method for tracking target based on entropy weight method point mark quality evaluation, the method includes:
Step 1, initialized target state X (k) and target covariance matrix P (k), k=0;
The state transition equation of target is:
X (k+1)=FX (k)+ω (k)
Wherein, F is state-transition matrix, and ω (k) is the Gaussian process noise of zero-mean, and its covariance matrix is Q, measurement model For:
Z (k)=HX (k)+v (k)
Z (k) is that target measures, and H is observing matrix, and v (k) is the Gauss observation noise of zero-mean, and its covariance matrix is R;
Step 2, according to state X (k-1) of k-1 moment target t and covariance matrix P (k-1) thereof, obtain the shape of k moment target t State predictive value:
X (k | k-1)=FX (k-1)
Measurement predictor:
Z (k | k-1)=HX (k | k-1)
Covariance one-step prediction value P (k | k-1):
P (k | k-1)=FP (k-1) FT+Q;
Step 3, reading kth frame position measurement set from radar receiverziK () is the i-th in k moment Measure, meanwhile, and obtain kth frame measurement amplitude setDoppler ensemblesWork Frequency setsAnd corresponding three knowledge assistances of each position quantity measured value measure index a (k), d (k), f (k),
Whether amount of calculation measured value z (k) meets following formula, if meeting, as candidate's echo, and adds up the echo number meeting requirement,
[z(k)-Z(k|k-1)]TS-1(k)×[z(k)-Z(k|k-1)]≤γ
Wherein S (k) is new breath covariance S (k)=HP (k | the k-1) H in k momentT+ R, γ are ripple door size set in advance;
Step 4, according to step 3 statistics obtain falling into the candidate target number of Bo Mennei, update targetpath,
If candidate's echo number is zero, does not the most measure and fall into relevant Bo Mennei, update flight path with the predictive value of step 2, proceed to Step 8.1;
If falling into the measuring value only one of which of relevant Bo Mennei, then this measuring value can be directly used for flight path renewal, proceeds to step 8.2;
If there being more than one echo to fall into the relevant Bo Mennei of tracked target, then Bo Mennei point mark is carried out quality evaluation, Choose optimum point, proceed to step 5;
Step 5, calculate the locus assessed value of each candidate's echo, amplitude assessed value, Doppler evaluation value, operating frequency assessment Value, obtains the assessment vector under each evaluation index, then assessment Vector Groups is combined into an evaluating matrix A;
Step 6, calculate the entropy assessment weights of every evaluation index, form weight vector;
Step 6.1 sets xijFor the element in a mark mass matrix A, i=1,2,3 ..., mk, mkFor falling into the effective dose of Bo Mennei Surveying total number, j=1,2,3,4 represent four respectively measures index, then carries out data translation, i.e. uses xijAfter+1 as translation xij, because utilizing entropy to calculate with logarithm when seeking flexible strategy, carry out when data translation can be avoided taking the logarithm meaningless;
Step 6.2 calculates the proportion that under jth item index, the value of the i-th factor is shared in this index
Step 6.3 calculates the entropy of jth item index
Step 6.4 calculates the coefficient of variation of jth item index:
Step 6.5 calculates weightsObtain four evaluation index weight vectors;
The weight vector that step 7, evaluating matrix step 5 obtained obtain with step 6 is multiplied, and obtains final mass assessed value V =Aw, finds the position i that maximum in assessed value V is correspondingop, obtain Bo Mennei optimum and measure zop(k);
Step 8, calculate filtering gain matrix K (k)=P (k | the k-1) H (k) S (k) of target t-1, obtain state more new-standard cement x (k | k), carry out flight path renewal;
If step 8.1 candidate's echo number is zero, does not the most measure and fall into relevant Bo Mennei, utilize future position to update flight path;
X (k | k)=x (k | k-1)
P (k | k)=P (k | k-1)
If step 8.2 falls into the measuring value only one of which of relevant Bo Mennei, then this measuring value can be directly used for flight path renewal;
X (k | k)=x (k | k-1)+K (k) [z (k)-HX (k | k-1)]
P (k | k)=P (k | k-1)-K (k) HP (k | k-1)
Step 8.3 Ruo Bomennei has multiple point, then choose and update flight path by optimum point after a mark quality evaluation
X (k | k)=x (k | k-1)+K (k) [zop(k)-Z(k|k-1)]
P (k | k)=P (k | k-1)-K (k) HP (k | k-1).
A kind of method for tracking target based on entropy weight method point mark quality evaluation, it is characterised in that Concretely comprising the following steps of step 5:
Step 5.1 calculates locus assessed value:
M within k moment, ripple doorkIndividual effective status measures as zi(k), i=1,2,3 ..., mk, the filtering of i-th state measurement Residual vector is vi(k)=zi(k)-Z (k | k-1), it with the statistical distance of predicted position is The locus assessed value that i-th effectively measures is:
m s ( i ) = m i n ( D 1 , D 2 , ... , D m k ) D i , D i ≠ 0 , 1 , D i = 0 , i = 1 , 2 , 3 , ... , m k ;
Step 5.2 calculates amplitude assessed value:
M within k moment, ripple doorkIndividual effective observation amplitude information is ai(k), i=1,2,3 ..., mk, define amplitude assessed value ma:
m a ( a i ( k ) ) = p 1 T ( a i ( k ) ) p 1 T ( a i ( k ) ) + p 0 T ( a i ( k ) )
Wherein,p0Amplitude when () echo-signal comes solely from noise or clutter a Probability density function, p1A () is echo-signal probability density function when comprising echo signal, T is detection threshold, pfa、pd It is respectively false-alarm probability, detection probability;
Step 5.3 calculates Doppler evaluation value:
M within k moment, ripple doorkThe doppler information of individual effective observation is di(k), i=1,2,3 ..., mkIf kth is adopted for-1 time During sample, the Doppler frequency observation of target corresponding to this tracking gate is d (k-1), note observation Doppler difference Δ di(k)=| di (k)-d (k-1) |, the assessed value of Doppler frequency is designated as md:
m d ( i ) = m i n ( Δd 1 , Δd 2 , ... , Δd m k ) Δd i , Δd i ≠ 0 1 , Δd i = 0 ;
The assessed value of step 5.4 operating frequency:
M within k moment, ripple doorkIndividual effective observation working frequency information is fi(k), i=1,2,3 ..., mkIf kth is adopted for-1 time During sample, the operating frequency observation of target corresponding to this tracking gate is f (k-1), remembers Δ fi(k)=| fi(k)-f (k-1) |, for Observation frequency difference, the assessed value of operating frequency is designated as mf, only consider the target of fixed frequency FIX type, the m that i-th is effectively observedf For:
m f ( i ) = 1 , Δf i ≤ ϵ f ( Δf i - 2 ϵ f ) 2 ϵf 2 , ϵ f ≤ Δf i ≤ 2 ϵ f 0 , Δf i ≥ 2 ϵ f
Wherein ε f is by frequency measurement tolerance determined by system noise and measurement noise;
Step 5.5 is by step 5.1,5.2,5.3,5.4 each evaluation index composition point the mark quality evaluation matrix A, i.e. A=obtained [ms;ma;md;mf]T
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