scholarly journals Tracking multiple maneuvering targets hidden in the DBZ based on the MM-PHD filter

2020 ◽  
Author(s):  
Weihua Wu
Keyword(s):  
Label Propagation ◽  
Tracking Performance ◽  
Moving Target ◽  
Multiple Model ◽  
Probability Hypothesis Density ◽  
Maneuvering Target ◽  
Maneuvering Targets ◽  
Model Probability ◽  
Track Initiation ◽  
Ground Moving Target Indication

<p><a></a><a></a><a>For a ground moving target indication (GMTI) radar, the presence of </a><a></a><a></a><a></a><a>Doppler blind zone (DBZ)</a> results in many short tracks with frequent label switching, which seriously deteriorates the tracking performance. When the DBZ masking is coupled with targets maneuvering, tracking multiple maneuvering targets hidden in the DBZ becomes very challenging, which is reflected in the fact that there is no public research on this issue. To overcome this complicated problem, we propose a practical and fully functional GMTI multi-maneuvering-target tracker based on the multiple model probability hypothesis density (MM-PHD) filter. Unlike the standard MM-PHD filter, the proposed tracker utilizes the Doppler information and incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Furthermore, to cope with the problems of the fixed initiation and no label output of the standard MM-PHD filter, the resulting MM-PHD filter with the Doppler and MDV information is augmented with measurement-driven adaptive track initiation and track label propagation, which are necessary for a practical tracker and also required for evaluating the overall GMTI tracking performance. Finally, numerical examples show that the proposed tracker outperforms significantly the existing ones, thus verifying its effectiveness.</p> <p> </p>

scholarly journals Tracking multiple maneuvering targets hidden in the DBZ based on the MM-PHD filter

2020 ◽  
Author(s):  
Weihua Wu
Keyword(s):  
Label Propagation ◽  
Tracking Performance ◽  
Moving Target ◽  
Multiple Model ◽  
Probability Hypothesis Density ◽  
Maneuvering Target ◽  
Maneuvering Targets ◽  
Model Probability ◽  
Track Initiation ◽  
Ground Moving Target Indication

<p><a></a><a></a><a>For a ground moving target indication (GMTI) radar, the presence of </a><a></a><a></a><a></a><a>Doppler blind zone (DBZ)</a> results in many short tracks with frequent label switching, which seriously deteriorates the tracking performance. When the DBZ masking is coupled with targets maneuvering, tracking multiple maneuvering targets hidden in the DBZ becomes very challenging, which is reflected in the fact that there is no public research on this issue. To overcome this complicated problem, we propose a practical and fully functional GMTI multi-maneuvering-target tracker based on the multiple model probability hypothesis density (MM-PHD) filter. Unlike the standard MM-PHD filter, the proposed tracker utilizes the Doppler information and incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Furthermore, to cope with the problems of the fixed initiation and no label output of the standard MM-PHD filter, the resulting MM-PHD filter with the Doppler and MDV information is augmented with measurement-driven adaptive track initiation and track label propagation, which are necessary for a practical tracker and also required for evaluating the overall GMTI tracking performance. Finally, numerical examples show that the proposed tracker outperforms significantly the existing ones, thus verifying its effectiveness.</p> <p> </p>

scholarly journals MM-PHD filter-based sensor control for tracking multiple maneuvering targets hidden in the DBZ

2020 ◽  
Author(s):  
Weihua Wu
Keyword(s):  
Control Technique ◽  
Maneuvering Target Tracking ◽  
Probability Hypothesis Density ◽  
Maneuvering Target ◽  
Maneuvering Targets ◽  
Control Scheme ◽  
Sensor Control ◽  
Markov Decision ◽  
Model Probability ◽  
Partially Observable

<a></a><a></a><a>To improve the performance of tracking multiple maneuvering targets hidden in the Doppler blind zone (DBZ), we put forward the idea of using sensor control technique to suppress the DBZ masking problem for the first time, by utilizing the principle that the absolute Doppler of a target with respect to a sensor is affected by the target-to-sensor relative geometry and extending multi model probability hypothesis density (MM-PHD) filter for DBZ masking to the partially observable Markov decision process (POMDP) framework. First, the process flow of sensor control is systematically constructed based on our existing work. Second, in the core sensor controller module, we devise three objective functions (including a new safety indicator ensuring sensor safety, a novel reward rule for the DBZ avoidance, and the Cauchy-Schwarz divergence (CSD) compatible with the multi-maneuvering-target tracking) and a decision-making logic for the selection of control commands. Finally, the feasibility and effectiveness of the proposed control scheme are verified through numerical examples, and it is demonstrated that it is obviously superior to the random control strategy and the earlier work without using the control technology.</a>

scholarly journals MM-PHD filter-based sensor control for tracking multiple maneuvering targets hidden in the DBZ

2020 ◽  
Author(s):  
Weihua Wu
Keyword(s):  
Control Technique ◽  
Maneuvering Target Tracking ◽  
Probability Hypothesis Density ◽  
Maneuvering Target ◽  
Maneuvering Targets ◽  
Control Scheme ◽  
Sensor Control ◽  
Markov Decision ◽  
Model Probability ◽  
Partially Observable

<a></a><a></a><a>To improve the performance of tracking multiple maneuvering targets hidden in the Doppler blind zone (DBZ), we put forward the idea of using sensor control technique to suppress the DBZ masking problem for the first time, by utilizing the principle that the absolute Doppler of a target with respect to a sensor is affected by the target-to-sensor relative geometry and extending multi model probability hypothesis density (MM-PHD) filter for DBZ masking to the partially observable Markov decision process (POMDP) framework. First, the process flow of sensor control is systematically constructed based on our existing work. Second, in the core sensor controller module, we devise three objective functions (including a new safety indicator ensuring sensor safety, a novel reward rule for the DBZ avoidance, and the Cauchy-Schwarz divergence (CSD) compatible with the multi-maneuvering-target tracking) and a decision-making logic for the selection of control commands. Finally, the feasibility and effectiveness of the proposed control scheme are verified through numerical examples, and it is demonstrated that it is obviously superior to the random control strategy and the earlier work without using the control technology.</a>

scholarly journals IMM Fifth-Degree Spherical Simplex-Radial Cubature Filter for Maneuvering Target Tracking

2017 ◽  
Author(s):  
Hua Liu ◽  
Wen Wu
Keyword(s):  
Target Tracking ◽  
Tracking Performance ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Scenario Simulation ◽  
Tracking Accuracy ◽  
Maneuvering Target Tracking ◽  
Switching Speed ◽  
Maneuvering Target ◽  
Spherical Simplex

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named interacting multiple model fifth-degree spherical simplex-radial cubature filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and fifth-degree spherical simplex-radial cubature filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with IMMUKF, IMMCKF and IMM5thCKF.

Multiple-model Probability Hypothesis Density Smoother

2010 ◽  
Vol 36 (7) ◽  
pp. 939-950 ◽  
Author(s):  
Feng LIAN ◽  
Chong-Zhao HAN ◽  
Wei-Feng LIU ◽  
Xiang-Hui YUAN
Keyword(s):  
Multiple Model ◽  
Probability Hypothesis Density ◽  
Model Probability

Multiple Model CPHD Filter for Tracking Maneuvering Targets

2014 ◽  
Vol 556-562 ◽  
pp. 3238-3241 ◽  
Author(s):  
Jie Sun ◽  
Dong Li
Keyword(s):  
Estimation Accuracy ◽  
Multiple Model ◽  
Single Model ◽  
Target Number ◽  
Probability Hypothesis Density ◽  
Maneuvering Targets ◽  
Target States ◽  
Augmented State ◽  
Cphd Filter ◽  
Motion Mode

A multiple model cardinalized probability hypothesis density (CPHD) filter is proposed for tracking multiple maneuvering targets. The augmented state is established by combining the target motion mode with the kinematic state. Both the posterior cardinality distribution of the targets and the posterior probability hypothesis density (PHD) of the augmented state are jointly propagated by using CPHD recursion. Simulation results show that the proposed filter improves the estimation accuracy of target number and target states over the multiple model PHD filter and single model CPHD filter respectively.

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