Gaussian mixtures in multi-target tracking: a look at Gaussian mixture probability hypothesis density and integrated track splitting

2012 ◽  
Vol 6 (5) ◽  
pp. 359 ◽  
Author(s):  
T.L. Song ◽  
D. Mušicki ◽  
D.S. Kim ◽  
Z. Radosavljević
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Gaussian Mixtures ◽  
Probability Hypothesis Density ◽  
Multi 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 Multisensor Multi-Target Tracking Based on GM-PHD Using Out-Of-Sequence Measurements

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4315 ◽  
Author(s):  
Meiqin Liu ◽  
Tianyi Huai ◽  
Ronghao Zheng ◽  
Senlin Zhang
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Gaussian Mixture ◽  
Tracking Performance ◽  
Probability Hypothesis Density ◽  
Single Target ◽  
Multi Target Tracking ◽  
Finite Set ◽  
Single Target Tracking ◽  
Better Than

In this paper, we study the issue of out-of-sequence measurement (OOSM) in a multi-target scenario to improve tracking performance. The OOSM is very common in tracking systems, and it would result in performance degradation if we used it inappropriately. Thus, OOSM should be fully utilized as far as possible. To improve the performance of the tracking system and use OOSM sufficiently, firstly, the problem of OOSM is formulated. Then the classical B1 algorithm for OOSM problem of single target tracking is given. Next, the random finite set (RFS)-based Gaussian mixture probability hypothesis density (GM-PHD) is introduced. Consequently, we derived the equation for re-updating of posterior intensity with OOSM. Implementation of GM-PHD using OOSM is also given. Finally, several simulations are given, and results show that tracking performance of GM-PHD using OOSM is better than GM-PHD using in-sequence measurement (ISM), which can strongly demonstrate the effectiveness of our proposed algorithm.

scholarly journals Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking

Sensors ◽  
2016 ◽  
Vol 16 (10) ◽  
pp. 1666 ◽  
Author(s):  
Feng Yang ◽  
Yongqi Wang ◽  
Hao Chen ◽  
Pengyan Zhang ◽  
Yan Liang
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Probability Hypothesis Density ◽  
Probability Hypothesis Density Filter ◽  
Multi Target Tracking ◽  
Density Filter

scholarly journals Active Sonar Target Tracking Based on the GM-CPHD Filter Algorithm

2018 ◽  
Vol 36 (4) ◽  
pp. 656-663
Author(s):  
Xiao Chen ◽  
Yaan Li ◽  
Yuxing Li ◽  
Jing Yu
Keyword(s):  
Target Tracking ◽  
Detection Probability ◽  
Gaussian Mixture ◽  
Time Varying ◽  
Adaptive Detection ◽  
Probability Hypothesis Density ◽  
Active Sonar ◽  
Multi Target Tracking ◽  
Cphd Filter ◽  
The Relationship

The estimation of underwater multi-target state has always been the difficult problem of active sonar target tracking.In order to get the variable number of target and their state, the random finite set theory is applied to multi-target tracking system.This theory not only effectively avoids the problem of multi-target tracking data association, and also realizes the estimation of time-varying number of targets and their states.Due to Probability Hypothesis Density(PHD) recursion propagates cardnality distribution with only a single parameter, a new generalization of the PHD recursion called Cardinalized Probability Hypothesis Density(CPHD) recursion, which jointly propagates the intensity function and the cardnality distribution, while have a big computation than PHD.Also there did not have closed-form solution for PHD recursion and CPHD recursion, so for linear Gaussian multi-target tracking system, the Gaussian Mixture Probability Hypothesis Density and Gaussian Mixture Cardinalized Probability Hypothesis Density(GM-CPHD) filter algorithm is put forward.GM-CPHD is more accurate than GM-PHD in estimation of the time-varying number of targets.In this paper, we use the ellipse gate tracking strategy to reduce computation in GM-CPHD filtering algorithm.At the same time, according to the characteristics of underwater target tracking, using active sonar equation, we get the relationship between detection probability, distance and false alarm, when fixed false alarm, analytic formula of the relationship between adaptive detection probability and distance is obtained, we puts forward the adaptive detection probability GM-CPHD filtering algorithm.Simulation shows that the combination of ellipse tracking gate strategy and adaptive detection probability GM-CPHD filtering algorithm can realize the estimation of the time-varying number of targets and their state more accuracy in dense clutter environment.

scholarly journals An Unbalanced Weighted Sequential Fusing Multi-Sensor GM-PHD Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 366
Author(s):  
Han Shen-Tu ◽  
Hanming Qian ◽  
Dongliang Peng ◽  
Yunfei Guo ◽  
Ji-An Luo
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Tracking Problem ◽  
Finite Sets ◽  
Probability Hypothesis Density ◽  
Multiple Sensors ◽  
Multi Target Tracking ◽  
Random Finite Sets

In this paper, we study the multi-sensor multi-target tracking problem in the formulation of random finite sets. The Gaussian Mixture probability hypothesis density (GM-PHD) method is employed to formulate the sequential fusing multi-sensor GM-PHD (SFMGM-PHD) algorithm. First, the GM-PHD is applied to multiple sensors to get the posterior GM estimations in a parallel way. Second, we propose the SFMGM-PHD algorithm to fuse the multi-sensor GM estimations in a sequential way. Third, the unbalanced weighted fusing and adaptive sequence ordering methods are further proposed for two improved SFMGM-PHD algorithms. At last, we analyze the proposed algorithms in four different multi-sensor multi-target tracking scenes, and the results demonstrate the efficiency.

scholarly journals Adaptive probability hypothesis density filter for multi-target tracking with unknown measurement noise statistics

2021 ◽  
Vol 54 (3-4) ◽  
pp. 279-291
Author(s):  
Weijun Xu
Keyword(s):  
Target Tracking ◽  
Random Noise ◽  
Measurement Model ◽  
Gaussian Mixture ◽  
Measurement Noise ◽  
Probability Hypothesis Density ◽  
Practical Applications ◽  
Multi Target Tracking ◽  
New Formulation ◽  
Noise Statistics

Under the Gaussian noise assumption, the probability hypothesis density (PHD) filter represents a promising tool for tracking a group of moving targets with a time-varying number. However, inaccurate prior statistics of the random noise will degrade the performance of the PHD filter in many practical applications. This paper presents an adaptive Gaussian mixture PHD (AGM-PHD) filter for the multi-target tracking (MTT) problem in the scenario where both the mean and covariance of measurement noise sequences are unknown. The conventional PHD filters are extended to jointly estimate both the multi-target state and the aforementioned measurement noise statistics. In particular, the Normal-inverse-Wishart and Gaussian distributions are first integrated to represent the joint posterior intensity by transforming the measurement model into a new formulation. Then, the updating rule for the hyperparameters of the model is derived in closed form based on variational Bayesian (VB) approximation and Bayesian conjugate prior heuristics. Finally, the dynamic system state and the noise statistics are updated sequentially in an iterative manner. Simulations results with both constant velocity and constant turn model demonstrate that the AGM-PHD filter achieves comparable performance as the ideal PHD filter with true measurement noise statistics.

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