scholarly journals Maintaining Track Continuity for Extended Targets Using Gaussian-Mixture Probability Hypothesis Density Filter

2015 ◽  
Vol 2015 ◽  
pp. 1-16
Author(s):  
Yulan Han ◽  
Hongyan Zhu ◽  
ChongZhao Han
Keyword(s):  
Gaussian Mixture ◽  
Intensity Function ◽  
Probability Hypothesis Density ◽  
Management Scheme ◽  
Extended Target ◽  
Close Proximity ◽  
Density Filter ◽  
Track Management ◽  
Simulation Results ◽  
Track Initiation

A multiextended-target tracker based on the extended target Gaussian-mixture probability hypothesis density (ET-GMPHD) filter, which can provide the tracks of the extended targets, is proposed to maintain the track continuity for the extended targets. To identify the extended targets, each individual Gaussian term of the mixture representing the posterior intensity function will be assigned a label, which is evolved through time. Then a track management scheme, including track initiation, track confirmation, track propagation, and termination, is developed to form the tracks for the extended targets. Furthermore, to improve the performance of the extended target tracker we also propose a mixture partitioning algorithm for resolving the identities of the extended targets in close proximity. The simulation results show that our proposed tracker achieves the less error of the position estimates and decreases the probability of incorrect label assignments from 0.6 to 0.25.

scholarly journals Adaptive Target Birth Intensity for ET-PHD Filter

2018 ◽  
Vol 176 ◽  
pp. 03010
Author(s):  
Lu Miao ◽  
Xin-xi Feng ◽  
Luo-jia Chi
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Tracking Algorithm ◽  
Tracking Problem ◽  
Probability Hypothesis Density ◽  
Probability Hypothesis Density Filter ◽  
Target Probability ◽  
Extended Target ◽  
Density Filter ◽  
Simulation Results

An adaptive tracking algorithm based on Extended target Probability Hypothesis Density (ETPHD) filter is proposed for extended target tracking problem with priori unknown target birth intensity.The algorithm is implemented by gaussian mixture, where the target birth intensity is generated by measurement-driven, and the persistent and the newborn targets intensity are respectively predicted and updated. The simulation results show that the proposed algorithm improves the performance of the probability hypothesis density filter in the extended target tracking.

scholarly journals Time-Matching Random Finite Set-Based Filter for Radar Multi-Target Tracking

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4416 ◽  
Author(s):  
Defu Jiang ◽  
Ming Liu ◽  
Yiyue Gao ◽  
Yang Gao ◽  
Wei Fu ◽  
...  
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Bayesian Filtering ◽  
Probability Hypothesis Density ◽  
Random Finite Set ◽  
Bayesian Formulation ◽  
Multi Target Tracking ◽  
Finite Set ◽  
Density Filter ◽  
Bernoulli Filter

The random finite set (RFS) approach provides an elegant Bayesian formulation of the multi-target tracking (MTT) problem without the requirement of explicit data association. In order to improve the performance of the RFS-based filter in radar MTT applications, this paper proposes a time-matching Bayesian filtering framework to deal with the problem caused by the diversity of target sampling times. Based on this framework, we develop a time-matching joint generalized labeled multi-Bernoulli filter and a time-matching probability hypothesis density filter. Simulations are performed by their Gaussian mixture implementations. The results show that the proposed approach can improve the accuracy of target state estimation, as well as the robustness.

scholarly journals Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2665 ◽  
Author(s):  
Yulan Han ◽  
Chongzhao Han
Keyword(s):  
Mahalanobis Distance ◽  
Nearest Neighbors ◽  
Set Partitioning ◽  
Measurement Information ◽  
Probability Hypothesis Density ◽  
Dbscan Algorithm ◽  
Target Probability ◽  
Extended Target ◽  
Density Filter ◽  
Partitioning Algorithms

The extended target probability hypothesis density (ET-PHD) filter cannot work well if the density of measurements varies from target to target, which is based on the measurement set partitioning algorithms employing the Mahalanobis distance between measurements. To tackle the problem, two measurement set partitioning approaches, the shared nearest neighbors similarity partitioning (SNNSP) and SNN density partitioning (SNNDP), are proposed in this paper. In SNNSP, the shared nearest neighbors (SNN) similarity, which incorporates the neighboring measurement information, is introduced to DP instead of the Mahalanobis distance between measurements. Furthermore, the SNNDP is developed by combining the DBSCAN algorithm with the SNN similarity together to enhance the reliability of partitions. Simulation results show that the ET-PHD filters based on the two proposed partitioning algorithms can achieve better tracking performance with less computation than the compared algorithms.

scholarly journals Convergence Results for the Gaussian Mixture Implementation of the Extended-Target PHD Filter and Its Extended Kalman Filtering Approximation

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Feng Lian ◽  
Chongzhao Han ◽  
Jing Liu ◽  
Hui Chen
Keyword(s):  
Uniform Convergence ◽  
Kalman Filtering ◽  
Gaussian Mixture ◽  
Extended Kalman Filtering ◽  
Probability Hypothesis Density ◽  
Target Probability ◽  
Convergence Results ◽  
Extended Target ◽  
Nonlinear Condition

The convergence of the Gaussian mixture extended-target probability hypothesis density (GM-EPHD) filter and its extended Kalman (EK) filtering approximation in mildly nonlinear condition, namely, the EK-GM-EPHD filter, is studied here. This paper proves that both the GM-EPHD filter and the EK-GM-EPHD filter converge uniformly to the true EPHD filter. The significance of this paper is in theory to present the convergence results of the GM-EPHD and EK-GM-EPHD filters and the conditions under which the two filters satisfy uniform convergence.

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