CN110673132A - Real-time filtering method for trace point sequence for multi-frame joint detection and tracking - Google Patents

Real-time filtering method for trace point sequence for multi-frame joint detection and tracking Download PDF

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CN110673132A
CN110673132A CN201910961671.7A CN201910961671A CN110673132A CN 110673132 A CN110673132 A CN 110673132A CN 201910961671 A CN201910961671 A CN 201910961671A CN 110673132 A CN110673132 A CN 110673132A
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杨晓波
刘克柱
杨琪
张鹏辉
汤窈颖
李武军
易伟
孔令讲
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Abstract

The invention discloses a real-time filtering method for outputting a trace point sequence by a multi-frame joint detection and tracking algorithm, which is applied to the technical field of radar target detection and tracking and aims to solve the problems that the existing processing method is lack of real-time property for improving the target tracking precision, and the correlation between the trace point sequences output by the tracking algorithm before detection is difficult to calculate, the invention firstly obtains the trace point sets belonging to the same target at the same time according to the trace point sequences output by the tracking algorithm before batch processing, then, respectively performing Kalman filtering on the traces in the trace set to obtain corresponding updated state estimation and posterior covariance estimation, solving the minimum value of the updated posterior covariance estimation based on a determinant minimization criterion to obtain corresponding weight values, and finally obtaining the state estimation and the posterior covariance estimation after weighted summation by using the obtained weight values to complete filtering on a trace sequence; the method of the invention obviously improves the tracking precision of the tracking algorithm before detection.

Description

Real-time filtering method for trace point sequence for multi-frame joint detection and tracking
Technical Field
The invention belongs to the technical field of radar target detection and tracking, and particularly relates to a filtering technology for a trace point sequence output by a tracking algorithm before batch detection.
Background
For the data plane obtained by radar scanning, in order to track a target, a traditional tracking algorithm can perform constant false alarm detection on a single-frame data plane, the target is easily lost in the constant false alarm detection process, the detection and tracking performance of the radar on the weak target is seriously reduced, and the power push-away range of a radar system is weakened.
The pre-detection tracking technology is a technology capable of detecting and tracking a weak target signal. The biggest difference from the traditional detection method is that the tracking technology before detection does not perform threshold decision detection on data in a single frame, but performs joint accumulation on multi-frame data in a target echo. By increasing the dimension of time, multi-frame data are compared in a combined mode, real target echoes are separated by utilizing the difference between targets and clutter and noise, target loss caused by limited processing information of single-frame echo data detection is effectively avoided, and therefore the method can be used for detecting weak target signals. Common weak target pre-detection tracking algorithms include a dynamic programming-based pre-detection tracking algorithm, a maximum likelihood probability data fusion algorithm, a Hough transform pre-detection tracking algorithm, a particle filter-based pre-detection tracking algorithm, a random set theory-based pre-detection tracking algorithm and the like. However, the output of the pre-detection tracking algorithm is a plurality of trace point sequences with the same length, and the estimation of directly taking the trace point sequences as the target track leads to low target tracking precision. In the literature, "A novel dynamic programming algorithm for track-before-detect in radar systems, IEEE Transactions on Signal Processing, vol.61, No.10, pp.2608-2619,2013" proposes to smooth the trace point sequence output by the tracking algorithm before detection so as to improve the target tracking precision; but the details of the smoothing of the trace point sequence are not described. The document "particle filtering for target tracking using spot-sequences of multi-frame tracking before detect," in 2015IEEE Radar Conference (RadarCon), IEEE 2015, pp.0495-0500 ", and its corresponding patent" a particle filtering method for a multi-frame pre-detection tracking trace sequence, publication CN104237853A "give a filtering method for a trace sequence, but this method focuses on the iteration problem between adjacent batches, while the present method focuses more on the fusion problem within the trace set. The document "a tracking adaptive tracking for low adaptive target using spot-sequences of multi-frame detection, in 201619th International Conference on Information Fusion (Fusion), IEEE,2016, pp.1427-1433" provides a smoothing method for a trace sequence output by a tracking algorithm before detection, but the method lacks rationality verification for an assumption of a measured noise model, and a patent corresponding to the document "a smoothing filtering method for a trace sequence of multi-frame joint detection, the method proposed by publication No. CN 106226750A" is a smoothing algorithm, and only can improve target tracking accuracy before the current time, and lacks an effective method for improving target tracking accuracy at the current time; compared with a smoothing algorithm, the method has a real-time filtering tracking effect and can improve the target tracking precision at the current moment.
Disclosure of Invention
Aiming at the problems that the existing processing method lacks real-time performance for improving target tracking precision and correlation between trace point sequences output by a tracking algorithm before detection is difficult to calculate, the invention provides a real-time filtering method for outputting trace point sequences by a multi-frame joint detection and tracking algorithm, and the target tracking precision of the tracking algorithm before detection is improved.
The technical scheme adopted by the invention is as follows: for the convenience of describing the contents of the present invention, the following terms are first explained:
the term 1: trace point sequence
And (3) carrying out joint processing on the N frame data planes by a tracking algorithm before detection, and outputting a plurality of short track sections with the lengths of N.
The term 2: set of traces of dots
For a continuous batch pre-detection tracking algorithm, state estimates of the same target at the same time exist in a trace point sequence output by the continuous batch pre-detection tracking algorithm, and the repeated state estimates are collected in the same set and are called a trace point set.
The term 3: correlation between trace point sequences
The state estimates within the trace set are estimates of the same target state and thus there is a correlation between them.
The invention provides a real-time filtering algorithm for a multi-frame joint detection and tracking algorithm output trace point sequence, which comprises the following specific steps:
step 1, initializing system parameters
Setting the frame number of a total observation data plane as L, the dimension of the observation data plane as A, and the combined processing frame number of a tracking algorithm before detection as N; the detection probabilities of the N frames are respectively
Figure BDA0002229127580000021
Each frame data plane sampling interval is TsA target state transition matrix F, an observation matrix H, a process noise covariance Q, an elliptic wave gate threshold gamma, and the current time k being N;
step 2, calculating the observation noise covariance of the tracking algorithm before detection
Respectively calculating the observation noise covariance matrix corresponding to each frame in the N frame data of the combined processing
Figure BDA0002229127580000022
N, wherein
Figure BDA0002229127580000023
Is the product of Kronecker, IΑRepresenting an identity matrix.
Step 3, point track set and track initialization
Reading the 1 st, 2 th, N frame echo data from the radar receiver at the k-N moment, and outputting a trace sequence by a pre-detection tracking algorithm
Figure BDA0002229127580000024
Thus obtaining the trace point set from all frames with the frame number of 1 being less than or equal to N being less than or equal to N at the moment of N
Figure BDA0002229127580000025
Wherein
Figure BDA0002229127580000031
The trace point sets are all in batch processing at the moment N and come from the nth frame of the same target; and is initialized thereby
Figure BDA0002229127580000032
And Pn|n(1≤n≤k)。
Step 4, updating the trace point set
At the moment of k +1, the echo data of the (k-N + 2) ·, k +1 frame is read from the radar receiver, and a track point sequence is output by a tracking algorithm before detection
Figure BDA0002229127580000033
Thereby obtaining an updated trace point set
Figure BDA0002229127580000034
k-N +2 is not less than N is not less than k +1, and
Figure BDA0002229127580000035
step 5, for N ═ k-N +2
Figure BDA0002229127580000036
And (3) carrying out filtering treatment:
step 5.1, firstly, a trace point set of N-k-N +2 frames
Figure BDA0002229127580000037
Inner part
Figure BDA0002229127580000038
Trace of points thetan tPerforming Kalman filtering to obtain corresponding updated state estimation
Figure BDA0002229127580000039
Sum covariance estimation
Figure BDA00022291275800000310
Step 5.1.1, trace point theta is pointedn tPerforming Kalman filtering to obtain corresponding updated state estimation
Figure BDA00022291275800000311
Sum covariance estimation
Figure BDA00022291275800000312
Including the following calculations:
intermediate variables
Figure BDA00022291275800000314
Intermediate variables
Figure BDA00022291275800000315
Figure BDA00022291275800000316
Wherein (·)TFor matrix transposition
Intermediate variables
Figure BDA00022291275800000317
Figure BDA00022291275800000318
Intermediate variables
Figure BDA00022291275800000319
Figure BDA00022291275800000320
Updated covariance estimation
Figure BDA00022291275800000321
Figure BDA00022291275800000322
Step 5.1.2, judging whether the requirement is met
Figure BDA00022291275800000323
If so, then there is an updated state estimate
Figure BDA00022291275800000324
Otherwise, there is
Figure BDA00022291275800000325
Step 5.1.3, judging whether the requirement is metIf yes, go to step 5.2; otherwise let t ═ t +1 and go to step 5.1.1.
Step 5.2, estimating all update states at the moment when N is k-N +2 according to determinant minimum covariance criterion
Figure BDA00022291275800000327
Sum covariance estimation
Figure BDA00022291275800000328
Solving the optimal combination:
Figure BDA0002229127580000042
obtain a correspondenceWeight vector
Figure BDA0002229127580000043
And solving to obtain the fused updated state estimation
Figure BDA0002229127580000044
Sum covariance estimation Pn|n：
Figure BDA0002229127580000046
Step 5.3, judging whether n is equal to k +1 or not, if yes, turning to step 6; otherwise, let n be n +1 and go to step 5.1.
Step 6, judging whether k +1 is equal to L or not, and if so, finishing updating all target states in the total observation time L frame; otherwise, let k be k +1, go to step 4.
The invention has the beneficial effects that: firstly, acquiring a trace point set belonging to the same target at the same moment according to a trace point sequence output by a tracking algorithm before batch processing detection, then respectively performing Kalman filtering on trace points in the trace point set to obtain corresponding updated state estimation and posterior covariance estimation, solving the minimum value of the updated posterior covariance estimation based on a determinant minimization criterion to obtain a corresponding weight, and finally obtaining the state estimation and the posterior covariance estimation after weighted summation by using the obtained weight; the method effectively solves the problem of complex correlation between trace sequences, realizes filtering of the trace sequences by utilizing the correlation between the trace sequences, and obviously improves the tracking precision of a tracking algorithm before detection; and has the following advantages:
1. by using a covariance cross algorithm, the direct calculation of the correlation between trace point sequences is ingeniously avoided;
2. filtering is carried out by combining a plurality of trace point sequences with overlapped trace points, and the correlation information among all trace point sequences output by a tracking algorithm before detection is fully utilized, so that the target tracking precision is improved in real time;
3. the solving process is simple, and the calculation complexity of the tracking algorithm before detection is reduced.
Drawings
FIG. 1 is a general flow diagram of the present invention.
FIG. 2 is a schematic diagram of an algorithm.
FIG. 3 is a graph comparing the effect of the solution of the present invention and the conventional method;
wherein, fig. 3(a) is a comparison graph of the real target trajectory and the estimated trajectory of the algorithm in a single scene (SNR ═ 5 dB); FIG. 3(b) is a graph comparing the RMES curves for KF-CI-DP-TBD (the algorithm of the present invention) and conventional DP-TBD (the conventional processing method) with an SNR of 6 dB; FIG. 3(c) is a graph comparing the RMES curves for KF-CI-MFDT and MFDT at SNR of 10 dB.
Detailed Description
In order to facilitate the understanding of the technical contents of the present invention by those skilled in the art, the following further explains the technical contents of the present invention with reference to fig. 1 to 3.
As shown in fig. 1, which is a general flow chart of the present invention, the implementation process includes steps 1-6, and all the steps and conclusions are verified to be correct on Matlab2017 b.
Step 1: the initialization of the system parameters is carried out,
specific simulation parameters are given in the embodiment: the number of frames of the total observation data plane is set to be L to 20, the dimension of the observation data plane is set to be A to 2, and the dimension is set to be mx×ny70 × 70, in cells; the joint processing frame number of the tracking algorithm before detection is N-6; the detection probabilities of 6 frames are respectively PD＝[0.8058,0.8922,0.9024,0.8901,0.8832,0.8287]Each frame data plane sampling interval is Ts1s, 16 elliptic wave gate threshold gamma, 6 current time k, a target state transition matrix F, an observation matrix H and a process noise covariance Q; the number of monte carlo trials was 500.
Figure BDA0002229127580000051
Step 2, calculating the observation noise covariance of the tracking algorithm before detection
Respectively calculating the observation noise covariance matrix corresponding to each frame in the N frame data of the combined processing
Figure BDA0002229127580000052
N, whereinIs the product of Kronecker, I2＝[1,0；0,1]。
Figure BDA0002229127580000054
Representing the target detection probability of the mth frame in the N frame data processed in a combined mode;
step 3, point track set and track initialization
Reading the 1 st, 2 th, N frame echo data from the radar receiver at the k-N moment, and outputting a trace sequence by a pre-detection tracking algorithm
Figure BDA0002229127580000055
Thus obtaining the trace point set from all frames with the frame number of 1 being less than or equal to N being less than or equal to N at the moment of N
Figure BDA0002229127580000056
WhereinThe trace point sets are all in batch processing at the moment N and come from the nth frame of the same target; and is initialized thereby
Figure BDA0002229127580000058
And Pn|n(1≤n≤N)。
Wherein the content of the first and second substances,
Figure BDA0002229127580000061
indicating the updated state estimate, P, at the nth framennRepresents the updated covariance estimate at the nth frame, and t represents the trace of points
Figure BDA0002229127580000062
The number of echoes of the frameAccording to the serial number in the echo data of the frame number L frame of the whole total observation data plane;
the processing procedure of steps 4-5 is shown in fig. 2:
step 4, updating the trace point set
At the moment of k +1, the echo data of the (k-N + 2) ·, k +1 frame is read from the radar receiver, and a track point sequence is output by a tracking algorithm before detection
Figure BDA0002229127580000063
Thereby obtaining an updated trace point set
Figure BDA0002229127580000064
k-N +2 is not less than N is not less than k +1, and
Figure BDA0002229127580000065
step 5, for N ═ k-N +2
Figure BDA0002229127580000066
And (3) carrying out filtering treatment:
step 5.1, firstly, a time point set with N being equal to k-N +2
Figure BDA0002229127580000067
Inner part
Figure BDA0002229127580000068
Trace of points thetan tPerforming Kalman filtering to obtain corresponding updated state estimationSum covariance estimation
Step 5.1.1, trace point theta is pointedn tPerforming Kalman filtering to obtain corresponding updated state estimationSum covariance estimation
Figure BDA00022291275800000612
Including the following calculations:
intermediate variables
Figure BDA00022291275800000613
Intermediate variables
Figure BDA00022291275800000615
Figure BDA00022291275800000616
Wherein (·)TFor matrix transposition
Intermediate variables
Figure BDA00022291275800000617
Figure BDA00022291275800000618
Intermediate variables
Figure BDA00022291275800000619
Figure BDA00022291275800000620
Updated covariance estimation
Figure BDA00022291275800000621
Figure BDA00022291275800000622
Step 5.1.2, judging whether the requirement is met
Figure BDA00022291275800000623
If so, then there is an updated state estimate
Figure BDA00022291275800000624
Otherwise, there is
Figure BDA00022291275800000625
Step 5.1.3, judging whether the requirement is met
Figure BDA00022291275800000626
If yes, go to step 5.2; otherwise let t ═ t +1 and go to step 5.1.1.
Step 5.2, estimating all update states at the moment when N is k-N +2 according to determinant minimum covariance criterionSum covariance estimation
Figure BDA00022291275800000628
Solving the optimal combination:
Figure BDA0002229127580000071
obtaining corresponding weight vector
Figure BDA0002229127580000073
And solving to obtain the fused updated state estimation
Figure BDA0002229127580000074
Sum covariance estimation Pn|n：
Figure BDA0002229127580000075
Figure BDA0002229127580000076
Wherein the content of the first and second substances,
Figure BDA0002229127580000077
representing covariance estimates
Figure BDA0002229127580000078
The weight of (2);
step 5.3, judging whether n is equal to k +1 or not, if yes, turning to step 6; otherwise, let n be n +1 and go to step 5.1.
Step 6, judging whether k +1 is equal to L or not, and if so, finishing updating all target states in the total observation time L frame; otherwise, k is made to be k +1, and the step is switched to.
FIG. 3 is a graph comparing the effect of the solution of the present invention and the conventional method; perfect Filtering in FIG. 3 refers to directly Filtering the real position of the target; the conventional DP-TBD is a Traditional DP-TBD algorithm (namely, if the detected track segment sequence is within a certain error range with the target track, the last frame point track of the track segment sequence is used for updating the target track); KF-CI-DP-TBD is the method provided by the invention, and Traditional DP-TBD is the Traditional processing method, and as can be seen from FIG. 3(a), the estimated track of the KF-CI-DP-TBD is closer to the real track than the Traditional DP-TBD in a single scene; as can be seen from FIG. 3(b), the RMSE (Root Mean square error) performance of the KF-CI-DP-TBD of the invention is better than that of the conventional DP-TBD; as can be seen by combining FIG. 3(b) and FIG. 3(c), as the SNR of the target increases, the RMSE of the KF-CI-DP-TBD gradually approaches the RMSE obtained by directly filtering the real position of the target, which shows the robustness of the KF-CI-DP-TBD algorithm, and shows that the method of the present invention has high and stable estimation accuracy.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims (4)

1. A real-time filtering method for output trace sequences of a multi-frame joint detection and tracking algorithm is characterized by comprising the following steps:
s1, obtaining a trace point set belonging to the same target at the same time according to a trace point sequence output by a tracking algorithm before batch processing detection;
s2, respectively performing Kalman filtering on the point traces in the point trace set of the same target at the same time obtained in the step S1, and obtaining the updated state estimation and the posterior covariance estimation of all the point traces in the point trace set of the same target at the same time;
s3, solving the minimum value of the updated state estimation and the posterior covariance estimation of all the point traces in the point trace set at the same time of the same target based on the determinant minimization criterion to obtain the weight corresponding to the updated posterior covariance estimation of all the point traces in the point trace set at the same time of the same target;
and S4, obtaining the final state estimation and the posterior covariance estimation through weighted summation according to the weight values corresponding to all the updated posterior covariance estimations obtained in the step S3 at each moment.
2. The method as claimed in claim 1, wherein the step S2 comprises the following steps:
if it isThen adopt
Figure FDA0002229127570000012
Updating the state estimation of the trace point; otherwise adoptUpdating the state estimation of the trace point;
wherein, (.)TPerforming matrix transposition operation; (.)-1Evaluating matricesPerforming inverse operation; thetan tRepresenting a trace of points; t represents a trace of dots
Figure FDA00022291275700000114
The sequence number of the echo data of the frame is in the echo data of the frame number of the total observation data plane; h represents an observation matrix; gamma represents the elliptic wave gate threshold; f represents a target state transition matrix; q represents process noise covariance;
Figure FDA0002229127570000014
is an intermediate variable;
the above-mentioned
Figure FDA0002229127570000015
Is updated by the expression of
Figure FDA0002229127570000016
Figure FDA0002229127570000017
Represents the updated state estimate at frame n-1;
the above-mentioned
Figure FDA0002229127570000018
Is updated by the expression of
Figure FDA0002229127570000019
Figure FDA00022291275700000110
Is an intermediate variable, and the update expression is:Rt-n+1represents the point trace measurement thetan tA corresponding measured noise covariance; pn-1，n-1Represents the updated covariance estimate at frame n-1;
step S2 is the update assistantThe difference estimation specifically includes:
Figure FDA00022291275700000112
Figure FDA00022291275700000113
representing an updated covariance estimate.
3. The method as claimed in claim 1, wherein the step S1 is specifically as follows: and at the current moment, reading continuous N frames of echo data cut off to the current moment from the radar receiver, and outputting a trace point sequence by a track-before-detect algorithm so as to obtain a trace point set of the same target at the same moment.
4. The method as claimed in claim 3, wherein the step S1 is preceded by initializing the state estimate and the a posteriori covariance estimate, specifically: at the Nth moment, the 1 st, 2 nd, 9th, N frames of echo data are read from the radar receiver, and a track point sequence is output by a track-before-detection algorithm
Figure FDA0002229127570000021
Thus obtaining the trace point set from all frames with the frame number of 1 being less than or equal to N being less than or equal to N at the Nth momentInitializing state estimation and posterior covariance estimation according to the state estimation and the posterior covariance estimation;
wherein the content of the first and second substances,
Figure FDA0002229127570000023
set of traces, θ, for all batches at time N and from the nth frame of the same targetn tRepresenting the trace point measurement.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111413693A (en) * 2020-04-10 2020-07-14 中国人民解放军海军航空大学 TBD (tunnel boring device) and conventional tracking combination method based on double-threshold shunt processing in MIMO (multiple input multiple output) radar

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100156698A1 (en) * 2008-12-16 2010-06-24 Thales Multiple-Sensor Tracking Processing Method with Reduced Latency Time
JP2011185719A (en) * 2010-03-08 2011-09-22 Mitsubishi Electric Corp Tracking radar device
CN102608568A (en) * 2012-02-28 2012-07-25 杭州电子科技大学 OSPA (Optimal Subpattern Assignment) distance track correlation method with fixed sliding window
CN103885057A (en) * 2014-03-20 2014-06-25 西安电子科技大学 Self-adaptation variable-sliding-window multi-target tracking method
CN104237853A (en) * 2014-09-16 2014-12-24 电子科技大学 Particle filtering method for multi-frame track-before-detect trace point sequence
CN105487061A (en) * 2015-12-01 2016-04-13 中国人民解放军海军航空工程学院 Multi-characteristic information fusion method for target data correlation
CN106226750A (en) * 2016-07-01 2016-12-14 电子科技大学 A kind of some mark sequence smooth filtering method for multi-frame joint detection
CN107656265A (en) * 2017-09-19 2018-02-02 电子科技大学 Particle filter fusion method for tracking short flight path before multi frame detection
CN108333571A (en) * 2018-02-07 2018-07-27 电子科技大学 A kind of multisensor multi-frame joint detecting and tracking method merged based on mark sequence
CN109633628A (en) * 2019-01-07 2019-04-16 西安电子科技大学 The method of anti-RGPO interference based on distributed networking Radar Data Fusion

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100156698A1 (en) * 2008-12-16 2010-06-24 Thales Multiple-Sensor Tracking Processing Method with Reduced Latency Time
JP2011185719A (en) * 2010-03-08 2011-09-22 Mitsubishi Electric Corp Tracking radar device
CN102608568A (en) * 2012-02-28 2012-07-25 杭州电子科技大学 OSPA (Optimal Subpattern Assignment) distance track correlation method with fixed sliding window
CN103885057A (en) * 2014-03-20 2014-06-25 西安电子科技大学 Self-adaptation variable-sliding-window multi-target tracking method
CN104237853A (en) * 2014-09-16 2014-12-24 电子科技大学 Particle filtering method for multi-frame track-before-detect trace point sequence
CN105487061A (en) * 2015-12-01 2016-04-13 中国人民解放军海军航空工程学院 Multi-characteristic information fusion method for target data correlation
CN106226750A (en) * 2016-07-01 2016-12-14 电子科技大学 A kind of some mark sequence smooth filtering method for multi-frame joint detection
CN107656265A (en) * 2017-09-19 2018-02-02 电子科技大学 Particle filter fusion method for tracking short flight path before multi frame detection
CN108333571A (en) * 2018-02-07 2018-07-27 电子科技大学 A kind of multisensor multi-frame joint detecting and tracking method merged based on mark sequence
CN109633628A (en) * 2019-01-07 2019-04-16 西安电子科技大学 The method of anti-RGPO interference based on distributed networking Radar Data Fusion

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
JINGHE WANG等: "An Efficient Recursive Multiframe Track-Before-Detect Algorithm", 《IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS》 *
李洋漾等: "基于DP-TBD的分布式异步迭代滤波融合算法研究", 《雷达学报》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111413693A (en) * 2020-04-10 2020-07-14 中国人民解放军海军航空大学 TBD (tunnel boring device) and conventional tracking combination method based on double-threshold shunt processing in MIMO (multiple input multiple output) radar
CN111413693B (en) * 2020-04-10 2022-02-11 中国人民解放军海军航空大学 TBD (tunnel boring device) and conventional tracking combination method based on double-threshold shunt processing in MIMO (multiple input multiple output) radar

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