Centralized multi-sensor multi-target tracking with labeled random finite set

2016 ◽  
Vol 231 (4) ◽  
pp. 669-676 ◽  
Author(s):  
Hao Qiu ◽  
Gaoming Huang ◽  
Jun Gao
Keyword(s):  
Probability Hypothesis Density ◽  
Multiple Sensors ◽  
Parallel Track ◽  
Truncation Scheme ◽  
Moment Approximation ◽  
Multi Target Tracking ◽  
Finite Set ◽  
Density Filter ◽  
Single Sensor ◽  
First Moment

Tracking multiple objects with multiple sensors is widely recognized to be much more complex than the single-sensor scenario. This contribution proposes a computationally tractable multi-sensor multi-target tracker. Based on Bayes equation and multi-senor observation model, a new corrector for multi-senor is derived. To lower the complexity of update operation, a parallel track-to-measurement association strategy is applied to the corrector. Hypotheses truncation scheme along with first-moment approximation of multi-target density are also employed to improve the tracking efficiency. The tracker is applied to a couple-sensor scenario. Experiment results validate the advantages of proposed method compared to the standard single-sensor δ-generalized labeled multi-Bernoulli filter and the iterated-corrector probability hypothesis density filter.

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 Passive Tracking of Multiple Underwater Targets in Incomplete Detection and Clutter Environment

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1082
Author(s):  
Xiaohua Li ◽  
Bo Lu ◽  
Wasiq Ali ◽  
Haiyan Jin
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Data Association ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Probability Hypothesis Density ◽  
Moment Approximation ◽  
Passive Tracking ◽  
Underwater Targets ◽  
First Moment

A major advantage of the use of passive sonar in the tracking multiple underwater targets is that they can be kept covert, which reduces the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the complexity of data association between measurements and targets make the problem of underwater passive multiple target tracking challenging. To deal with these problems, the cardinalized probability hypothesis density (CPHD) recursion, which is based on Bayesian information theory, is developed to handle the data association uncertainty, and to acquire existing targets’ numbers and states (e.g., position and velocity). The key idea of the CPHD recursion is to simultaneously estimate the targets’ intensity and the probability distribution of the number of targets. The CPHD recursion is the first moment approximation of the Bayesian multiple targets filter, which avoids the data association procedure between the targets and measurements including clutter. The Bayesian-filter-based extended Kalman filter (EKF) is applied to deal with the nonlinear bearing and Doppler measurements. The experimental results show that the EKF-based CPHD recursion works well in the underwater passive multiple target tracking system in cluttered and noisy environments.

scholarly journals Improved Gaussian Mixture Probability Hypothesis Density for Tracking Closely Spaced Targets

2017 ◽  
Vol 63 (3) ◽  
pp. 247-254 ◽  
Author(s):  
Huanqing Zhang ◽  
Hongwei Ge ◽  
Jinlong Yang
Keyword(s):  
State Estimation ◽  
Analytic Solution ◽  
Gaussian Mixture ◽  
Multiple Target ◽  
Probability Hypothesis Density ◽  
Random Finite Set ◽  
Multi Target Tracking ◽  
Finite Set ◽  
Estimation Precision ◽  
Simulation Results

AbstractProbability hypothesis density (PHD) filter is a suboptimal Bayesian multi-target filter based on random finite set. The Gaussian mixture PHD filter is an analytic solution to the PHD filter for linear Gaussian multi-target models. However, when targets move near each other, the GM-PHD filter cannot correctly estimate the number of targets and their states. To solve the problem, a novel reweighting scheme for closely spaced targets is proposed under the framework of the GM-PHD filter, which can be able to correctly redistribute the weights of closely spaced targets, and effectively improve the multiple target state estimation precision. Simulation results demonstrate that the proposed algorithm can accurately estimate the number of targets and their states, and effectively improve the performance of multi-target tracking algorithm.

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