CN108363054A - Passive radar multi-object tracking method for Single Frequency Network and multipath propagation - Google Patents
Passive radar multi-object tracking method for Single Frequency Network and multipath propagation Download PDFInfo
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- CN108363054A CN108363054A CN201810133886.5A CN201810133886A CN108363054A CN 108363054 A CN108363054 A CN 108363054A CN 201810133886 A CN201810133886 A CN 201810133886A CN 108363054 A CN108363054 A CN 108363054A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO 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
- G01S13/00—Systems 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
- G01S13/66—Radar-tracking systems; Analogous systems
- G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
- G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
- G01S13/726—Multiple target tracking
Abstract
The invention belongs to passive radar target following technical fields, and in particular to a kind of passive radar multi-object tracking method for Single Frequency Network and multipath propagation.Tracking proposed by the present invention, in processing measurement and the related question between target, path and external sort algorithm, consider that the different propagation paths by different external sort algorithms reach multiple measurements of receiver and measured for possible target, and these measurements are measured function with known each bistatic pair of multipath respectively and are correctly associated with, to obtain the accumulation of target information, enhance target detection capabilities.Then target following is carried out by way of sliding window.
Description
Technical field
The invention belongs to passive radar target following technical fields, and in particular to one kind is passed for Single Frequency Network and multipath
The passive radar multi-object tracking method broadcast.
Background technology
Target following technology is widely used in each field, especially radar signal system.Passive radar PR is a kind of profit
The bistatic or more base system of target is tracked with external sort algorithm signal detection, e.g., radio, TV and communication node are basic
(NB), PR shows many advantages in the supervision field of city.Many PR systems all use Single Frequency Network (SFN) now,
Such as digital video broadcasting (DVB-T), digital audio broadcasting (DAB), city long term evolution environment (LTE) wireless communication system
System.All external sort algorithms in SFN all emit the signal with frequency simultaneously.Therefore, multiple measure may be derived from the same target
The different external sort algorithm signals of reflection, the related question being effectively treated between measurement, external radiation source and target become very necessary.
In addition, in actual urban applications scene, such as implement the tracking to unmanned plane (UAV), the receiver of PR is often not
It can arbitrarily be disposed.Therefore, the building of monitor area or barrier are probably added to external sort algorithm-target-receiver shape
At the bistatic signal propagation path to (such as Fig. 1) in.Miscellaneous by multipath is formed when if multi-path signal is more than detection threshold
Wave causes false target.
Current pertinent literature only discloses processing single frequency network multiple target tracking, multi-path environment multiple target tracking, not yet
The multi-object tracking method in the case of being measured there are multipath under single frequency network is handled simultaneously.
Invention content
A kind of purpose of the present invention, aiming at the above problem, it is proposed that while PR (SPR) scene of processing based on SFN
Two kinds of data correlation problems of lower multiple target tracking：It measures and is associated with uncertainty with target, path, measurement and target, outer spoke
The association for penetrating source is uncertain.
The present invention makes full use of more base multipath probability multiple hypotheis trackings of useful measurement information progress multiple target tracking
(MS-MP-PMHT) algorithm, is used in combination the performance of the simulating, verifying algorithm, and proves that useful measurement information is more, the tracking of target
Precision is better.
A kind of computation complexities of PMHT and the batch processing target tracking algorism for measuring number and number of targets linear correlation.It is used
" soft " decision of measurement and target association：Allow multiple measurements and target association, and measures mutual indepedent with being associated with for target.
The core that PMHT algorithms are realized is to obtain target based on maximum (EM) algorithm of expectation in the case where target is associated with unknown with measurement
The maximum a posteriori (MAP) of state is estimated.
The thinking of the present invention is, in processing measurement and the related question between target, path and external sort algorithm, to consider logical
The different propagation paths for crossing different external sort algorithms reach multiple measurements of receiver as the measurement of possible target, and these are measured
Function is measured with known each bistatic pair of multipath to be correctly associated with, to obtain the accumulation of target information, enhance target respectively
Detectability.Then target following is carried out by way of sliding window.
The technical solution adopted in the present invention is：
Passive radar multi-object tracking method for Single Frequency Network and multipath propagation, which is characterized in that including following
Step：
A, passive radar observation information is obtained：
A1, initialization observed parameter, including：
Number of targets N, the original state of target, covariance, reaching time-difference (TDOA) variance, Doppler variance, detection are general
Rate, clutter density λ, sampling interval, monitoring space V, external sort algorithm number S and position ps=(xs,ys)T, s ∈ [1, S], reception
Station location prec=(xrec,yrec)T, reflect number point L-1 and positioni∈[1,L-1]；
Set it is each pair of it is bistatic have a L paths, wherein L-1 paths are reflected into receiver by L-1 pip respectively, 1
Paths are the directapaths that receiver directly receives target echo；(as shown in Figure 1)
A2, observation information is obtained：T frame data are shared, have T in each sliding windowbFrame data, metric data in the sliding window
Collection is combined into, t frame amount measured data collection is combined into Z (t), t ∈ [1, T in sliding windowb]；
B, using more base multipath probability multiple hypotheis tracking (MS-MP-PMHT) algorithms, Single Frequency Network and multipath are constructed
Under the passive radar scene of propagation, the correlation model between target, path and external sort algorithm so that each target is each pair of biradical
Only there are one comprehensive measurements and comprehensive covariance for each path on ground, including：
B1, the posterior probability calculation formula for constructing t frames：
It sets any measurement at most to be generated by a kind of a pair of bistatic propagation path by a target, a target energy
Any amount of measurement is generated by a kind of a pair of bistatic propagation path, and is measured and being associated with of target, measurement and path
Association, measurement with being associated with for external sort algorithm be statistical iteration；
Then unknown association is expressed as：
Wherein, mtIt is the measurement number of t moment, kj(t, s, l)=n indicates to measure zj(t) target xn(t) it is derived from and belongs to biradical
Ground is expressed as π to the path l of s, prior probabilityn(t, s, l)=p (kj(t, s, l)=n), calculation formula is：
Wherein, n=0 represents false target, Pd n(s, l) is target xnThe inspection measured is generated by the bistatic path l to s
Survey probability；
B2, construction likelihood calculation formula：
Assuming that clutter is distributed for space uniform, then：
Wherein,Indicating Gaussian probability-density function, the mean value of gaussian variable χ is μ, covariance Σ, andIndicate the measurement model corresponding to the bistatic l kinds path to s, Rn(t,
S, l) for it the covariance matrix of measurement model is corresponded to, the measurement model of different target is identical；
Prolonging new probability formula after b3, construction is：
Wherein,Indicate that moment t measures zj(t) target x is derived to the paths s l by bistaticn(t) after
Test probability.Thus known to formula as L=1, MS-MP-PMHT is degenerated to more base PMHT (MS-PMHT)；When S=1, MS-MP-
PMHT is degenerated to multipath PMHT (MP-PMHT)；
The comprehensive measurement of b4, construction and comprehensive covariance formula are：
It is comprehensive to measureWith comprehensive covarianceFormula be respectively：
C, according to the correlation model of the step a observation data obtained and step b constructions, target is obtained by way of iteration
The accumulation of information, specifically, setting maximum iteration, executes：
T in c1, initialization sliding windowbFrame data and metric data setThe i-th=1 iteration since t=1；
C2, after the calculating of the correlation model of step b construction, judge t=TbIt is whether true, if set up, enter
Step d；Otherwise t=t+1 repeats step c2；
D, target following is carried out, specially：
Measurement matrix, comprehensive measurement and the comprehensive covariance that step c2 is obtained are stacked using stacking method, then transported
With spreading kalman smoothing algorithm realization state with new estimation；
Jacobian matrix is sought to measuring function, as measurement matrix：
Respectively measurement matrix, comprehensive measurement and comprehensive covariance are stacked to obtain：
Wherein, diag () indicates diagonalizable matrix；
Finally, to target xn(t) it executes spreading kalman smoothing algorithm and obtains state estimation
E, judge whether iterations meet the loop iteration condition of convergence, i.e. whether i is equal to maximum iteration.Such as it is equal to
Then enter step f；Otherwise return to step c2, i-th=i+1 times iteration since t=1；
F, judge whether sliding window includes the last T of T frame data collectionbFrame data, if not provided, sliding window forward slip TsIt is a
When
It carves, forms T in new windowbFrame data and metric data setReturn to step c1；Otherwise terminate.
Beneficial effects of the present invention are：
First, the measurement information in different bistatic pair of different paths is utilized in the present invention under SFN environment, and these
Measurement information measures function with known each bistatic pair of multipath respectively and is correctly associated with, to obtain the accumulation of target information,
Enhance the detectability of target；
Second, the present invention solves measurement and target, path while effective under SFN environment, measures and target, outer spoke
The association penetrated between source is uncertain, and avoids the exponential calculation amount of conventional target track algorithm data correlation, MS-MP-
The computation complexity of PMHT algorithms is linearly related with measurement number, external sort algorithm number and number of targets.
Third, the present invention can be applied not only to passive radar.Being also applied for other, there are same take place frequently of multistation to penetrate signal detection
The target acquisition for having the multistatic radar under the conditions of multipath clutter off the net.
Description of the drawings
Fig. 1 is the position and measurement model geometric graph of target and sensor under SPR scenes；
Fig. 2 is two UAVs targetpaths and the geometry of SPR scenes；
Fig. 3 is the track of two targets in single emulation；
Fig. 4 is that the TDOA in single emulation measures figure；
Fig. 5 is that the Doppler frequency shift in single emulation measures figure；
Fig. 6 is the location estimation RMSE of 100 Monte Carlos of algorithm；
Fig. 7 is the velocity estimation RMSE of 100 Monte Carlos of algorithm.
Specific implementation mode
With reference to the accompanying drawings and detailed description, the present invention is described in further detail：
Emulation carries out in the SPR scenes of base LTE, tracks the UAVs of two adjacent linear uniform motion, as shown in Fig. 2,
MS-MP-PMHT is compared with standard PMHT, MS-PMHT, MP-PMHT.
(1) initial background parameter.
1a.2 UAVs movement original state be respectively：
x1(1)=[300m, 10m/s, 850m, -10m/s], x2(1)=[300m, 12m/s, 800m, -8m/s].Two targets
Initial covariance be ([200,1,200,1]) diagonal matrix diag.
1b. pip numbers are 1, position pref=(- 200m, 100m)T, 2 external sort algorithm positions are p1=(1000m,
0m)T, p2=(- 500m, 1000m)T.Receiver location is prec=(0m, 0m)TTDOA measurement ranges are 1.2~5.2us, how general
It is -180~-30Hz and 50~200Hz to strangle frequency displacement measurement range.Clutter is uniformly distributed in region, and quantity obeys Poisson distribution,
Mean clutter number per the moment is 40.The detection probability of target is
Measurement noise isσD=1.5Hz.Emulation total duration is 40s, sampling interval 1s, each batch processing
Duration TbFor 3 moment, sliding length TsFor 2 moment.It uses fixed cycles iterations for 5 times in per batch processing, filters
Original state is set as
After 1c.MS-MP-PMHT algorithm environment parameters determine, observation model is also predefined.From two-dimensional position state parameter
CoordinateTo the mapping of sensor observation coordinate [r dop], i.e., s is to bistatic l paths
Observation model can be obtained by the geometrical model of Fig. 1：
R=(r1+r2+r3-dis)/(3e^2)
The wherein transposition of subscript T representing matrixes has 1 pip in simulating scenes, therefore number of path L is 2, path Zhong Bao
Directapath containing 1 without pip, therefore, as l=2, pref T=[0；0].
(2) T is initializedbData in=3s sliding windows and metric data setSince t=1 the i-th=1 time repeatedly
Generation；
(3) the posterior probability calculation formula of MS-MP-PMHT t frames is constructed：
(4) comprehensive measurement and comprehensive covariance are calculated：
(5) judge whether t=3s is true, if set up, execute next step；Otherwise t=t+1 is returned to step
(3)；
(6) spreading kalman is smooth：
By T in sliding windowb-Ts+ 1=2s arrives TbThe stacking measurement matrix of=3sStack comprehensive amount
It surveysWith the comprehensive covariance of stackingIt is passed to spreading kalman smoothing algorithm as input；
(7) judge to be whether number of iterations i is equal to 5, as executed next step equal to if；Otherwise return to step (3), are opened from t=1
Begin i-th=i+1 times iteration；
(8) judge sliding window whether comprising the last T of emulation total duration 40sbThe data of=3s, if not provided, sliding window to
Front slide Ts=2s forms data and metric data set in new 3s sliding windowsIt returns to step (2)；Otherwise side
Method terminates.
In this example implementation, the RMSE of multiple target tracking 200 times shows the tracking accuracy of each algorithm in Fig. 6 and Fig. 7.
The result shows that the useful information that MS-MP-PMHT is used is most, RMSE is minimum, and Track In Track is most accurate.Because of direct signal
It is higher than multipath signal detection probability, so the MP-PMHT tracking of MS-PMHT ratios is accurate.The PMHT tracking of MP-PMHT ratios is more acurrate, because
It makes use of multi-path informations.
Claims (1)
1. the passive radar multi-object tracking method for Single Frequency Network and multipath propagation, which is characterized in that including following step
Suddenly：
A, passive radar observation information is obtained：
A1, initialization observed parameter, including：
Number of targets N, the original state of target, covariance, reaching time-difference variance, Doppler variance, detection probability, clutter density
λ, sampling interval, monitoring space V, external sort algorithm number S and position ps=(xs,ys)T, s ∈ [1, S], reception station location prec=
(xrec,yrec)T, reflect number point L-1 and position
Setting each pair of bistatic has L paths, wherein L-1 paths to be reflected into receiver, 1 road by L-1 pip respectively
Diameter is the directapath that receiver directly receives target echo；
A2, observation information is obtained：T frame data are shared, have T in each sliding windowbFrame data, metric data set in the sliding window
ForT frame amount measured data collection is combined into Z (t), t ∈ [1, T in sliding windowb]；
B, using more base multipath probability multiple hypotheis tracking algorithms, in the passive radar scene of Single Frequency Network and multipath propagation
Under, establish the correlation model between target, path and external sort algorithm so that each pair of bistatic each path of each target is only
There are one comprehensive measurements and comprehensive covariance, including：
B1, the posterior probability calculation formula for constructing t frames：
It sets any measurement at most to be generated by a kind of a pair of bistatic propagation path by a target, a target can pass through
A kind of a pair of bistatic propagation path generates any amount of measurement, and measures and being associated with of target, the pass of measurement and path
Connection, measurement are statistical iterations with being associated with for external sort algorithm；
Then unknown association is expressed as：
Wherein, mtIt is the measurement number of t moment, kj(t, s, l)=n indicates to measure zj(t) target xn(t) it is derived from and belongs to bistatic to s
Path l, prior probability is expressed as πn(t, s, l)=p (kj(t, s, l)=n), calculation formula is：
Wherein, n=0 represents false target, Pd n(s, l) is target xnIt is general that the detection measured is generated by the bistatic path l to s
Rate；
B2, construction likelihood calculation formula：
Assuming that clutter is distributed for space uniform, then：
Wherein,Indicating Gaussian probability-density function, the mean value of gaussian variable χ is μ, covariance Σ, and Indicate the measurement model corresponding to the bistatic l kinds path to s, Rn(t,s,
L) covariance matrix of measurement model is corresponded to for it, the measurement model of different target is identical；
Prolonging new probability formula after b3, construction is：
Wherein,Indicate that moment t measures zj(t) target x is derived to the paths s l by bistaticn(t) posteriority is general
Rate.Thus known to formula as L=1, MS-MP-PMHT is degenerated to more base PMHT (MS-PMHT)；When S=1, MS-MP-PMHT
It is degenerated to multipath PMHT (MP-PMHT)；
The comprehensive measurement of b4, construction and comprehensive covariance formula are：
It is comprehensive to measureWith comprehensive covarianceFormula be respectively：
C, according to the correlation model of the step a observation data obtained and step b constructions, target information is obtained by way of iteration
Accumulation, specifically, setting maximum iteration, execute：
T in c1, initialization sliding windowbFrame data and metric data setThe i-th=1 iteration since t=1；
C2, after the calculating of the correlation model of step b construction, judge t=TbIt is whether true, if set up, enter step d；
Otherwise t=t+1 repeats step c2；
D, target following is carried out, specially：
Measurement matrix, comprehensive measurement and the comprehensive covariance that step c2 is obtained are stacked using stacking method, then with expansion
Open up Kalman smoothing algorithm realize state with new estimation；
Jacobian matrix is sought to measuring function, as measurement matrix：
Respectively measurement matrix, comprehensive measurement and comprehensive covariance are stacked to obtain：
Wherein, diag () indicates diagonalizable matrix；
Finally, to target xn(t) it executes spreading kalman smoothing algorithm and obtains Target state estimator
E, judge whether iterations meet the loop iteration condition of convergence, i.e. whether i is equal to maximum iteration, if being equal into
Enter step f；Otherwise return to step c2, i-th=i+1 times iteration since t=1；
F, judge whether sliding window includes the last T of T frame data collectionbFrame data, if not provided, sliding window forward slip TsA moment,
Form T in new windowbFrame data and metric data setReturn to step c1；Otherwise terminate.
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| CN110726989A (en) * | 2019-06-28 | 2020-01-24 | 武汉大学 | Time-division multi-frequency external radiation source radar multi-target tracking method |
| CN113359099A (en) * | 2021-06-09 | 2021-09-07 | 电子科技大学 | Attribute data association method for multi-target radar radiation source tracking |
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| CN109509207B (en) * | 2018-11-09 | 2021-07-06 | 电子科技大学 | Method for seamless tracking of point target and extended target |
| CN110161492A (en) * | 2019-01-24 | 2019-08-23 | 北京机电工程研究所 | Naval vessel coursespeed extracting method |
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| CN110376574A (en) * | 2019-07-04 | 2019-10-25 | 西北工业大学 | A kind of method for tracking target based on more base passive sonars observation data |
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| CN115453489A (en) * | 2022-10-28 | 2022-12-09 | 长沙莫之比智能科技有限公司 | Indoor multipath discrimination method for millimeter wave radar |
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