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 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>

Maneuvering Target Tracking Algorithm Based on Particle PHD Filtering

2014 ◽  
Vol 989-994 ◽  
pp. 2212-2215
Author(s):  
Song Gao ◽  
Chao Bo Chen ◽  
Qian Gong
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Tracking Accuracy ◽  
Maneuvering Target Tracking ◽  
Probability Hypothesis Density ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Maneuvering Targets ◽  
The Status ◽  
Simulation Results

As for the problem of maneuvering target tracking in the clutter environment, this paper combines IMM with PHD and realizes it through approach of particle filter. This algorithm avoids the troublesome problem of data association, and takes advantage of probability hypothesis density (PHD) filter in tracking maneuvering targets and interacting multi-model (IMM) algorithm in the field of model switching effectively, in the clutter environment, the status of the targets can be estimated precisely and steadily. This paper compares the proposed filtering algorithm with the classical IMM algorithm in performance, and the simulation results show that, the improved filtering algorithm has good tracking performance and tracking accuracy.

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>

Novel Approach for Nonlinear Maneuvering Target Tracking Based on Input Estimation

2011 ◽  
Vol 110-116 ◽  
pp. 4415-4423 ◽  
Author(s):  
Hodjat Rahmati ◽  
Hamid Khaloozadeh ◽  
Moosa Ayati
Keyword(s):  
Target Tracking ◽  
Maneuvering Target Tracking ◽  
Input Estimation ◽  
Maneuvering Target ◽  
Detection Delay ◽  
Maneuvering Targets ◽  
Novel Approach ◽  
Nonlinear Input ◽  
Highly Nonlinear ◽  
Concurrent Estimation

In this paper, a new method for maneuvering target tracking (MTT) based on nonlinear input estimation (IE) is innovated and is employed for tracking of maneuvering targets. Proposed method augments the states and unknown inputs (maneuvers) in a higher order state space realization and estimates both of them simultaneously. The concurrent estimation of states and inputs eliminates the maneuver detection delay which is popular in the conventional IE methods. The proposed method has excellent performance for both maneuvering and non-maneuvering stages. Also, a model with highly nonlinear dynamics for maneuvering targets is given and is used in the numerical simulations to analyze the performance of the proposed maneuvering target tracking method. Furthermore the proposed method is compared with a conventional IE method and the simulation results show the effectiveness of the proposed method.

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