An improved labeled multi-bernoulli filter for bearings-only multi-target tracking

2017 ◽  
Author(s):  
Yifan Xie ◽  
Taek Lyul Song
Keyword(s):  
Target Tracking ◽  
Multi Target Tracking ◽  
Bernoulli 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 Multi-Target Tracking Based on Multi-Bernoulli Filter with Amplitude for Unknown Clutter Rate

Sensors ◽  
2015 ◽  
Vol 15 (12) ◽  
pp. 30385-30402 ◽  
Author(s):  
Changshun Yuan ◽  
Jun Wang ◽  
Peng Lei ◽  
Yanxian Bi ◽  
Zhongsheng Sun
Keyword(s):  
Target Tracking ◽  
Multi Target Tracking ◽  
Bernoulli Filter

scholarly journals Image-Based Multi-Target Tracking Through Multi-Bernoulli Filtering with Interactive Likelihoods

2017 ◽  
Author(s):  
Anthony Hoak ◽  
Henry Medeiros ◽  
Richard J. Povinelli
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Deep Neural Network ◽  
Sequential Monte Carlo ◽  
Pedestrian Detection ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Tracking Accuracy ◽  
Multi Target Tracking ◽  
Bernoulli Filter

We develop an interactive likelihood (ILH) for sequential Monte-Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, AFL, and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (OSPA and CLEAR MOT). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

scholarly journals Adaptive Target Birth Intensity Multi-Bernoulli Filter with Noise-Based Threshold

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1120 ◽  
Author(s):  
Xiaolong Hu ◽  
Hongbing Ji ◽  
Long Liu
Keyword(s):  
Target Tracking ◽  
Sequential Monte Carlo ◽  
Measurement Noise ◽  
Tracking Accuracy ◽  
Operation Efficiency ◽  
Processing Step ◽  
Multi Target Tracking ◽  
Filtering Efficiency ◽  
Bernoulli Filter ◽  
Simulation Results

Adaptively modeling the target birth intensity while maintaining the filtering efficiency is a challenging issue in multi-target tracking (MTT). Generally, the target birth probability is predefined as a constant and only the target birth density is considered in existing adaptive birth models, resulting in deteriorated target tracking accuracy, especially in the target appearing cases. In addition, the existing adaptive birth models also give rise to a decline in operation efficiency on account of the extra birth modeling calculations. To properly adapt the real variation of the number of newborn targets and improve the multi-target tracking performance, a novel fast sequential Monte Carlo (SMC) adaptive target birth intensity cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter is proposed in this paper. Through adaptively conducting the target birth probability in a pre-processing step, which incorporates the information of current measurements to correct the pre-setting of the target birth probability, the proposed filter can truly adapt target birth cases and achieve better tracking accuracy. Moreover, the implementation efficiency can be improved significantly by employing a measurement noise-based threshold in the likelihood calculations of the multi-target updating. Simulation results verify the effectiveness of the proposed filter.

scholarly journals Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 980
Author(s):  
Yun Zhu ◽  
Jun Wang ◽  
Shuang Liang
Keyword(s):  
Target Tracking ◽  
Sensor Selection ◽  
Management Problem ◽  
Multi Objective Optimization ◽  
Posterior Density ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Multi Target Tracking ◽  
Bernoulli Filter ◽  
Target States

This paper presents a novel multi-objective optimization based sensor selection method for multi-target tracking in sensor networks. The multi-target states are modelled as multi-Bernoulli random finite sets and the multi-Bernoulli filter is used to propagate the multi-target posterior density. The proposed method is designed to select the sensor that provides the most reliable cardinality estimate, since more accurate cardinality estimate indicates more accurate target states. In the multi-Bernoulli filter, the updated multi-target density is a multi-Bernoulli random finite set formed by a union of legacy tracks and measurement-updated tracks. The legacy track and the measurement-updated track have different theoretical and physical meanings, and hence these two kinds of tracks are considered separately in the sensor management problem. Specifically, two objectives are considered: (1) maximizing the mean cardinality of the measurement-updated tracks, (2) minimizing the cardinality variance of the legacy tracks. Considering the conflicting objectives simultaneously is a multi-objective optimization problem. Tradeoff solutions between two conflicting objectives will be derived. Theoretical analysis and examples show that the proposed approach is effective and direct. The performance of the proposed method is demonstrated using two scenarios with different levels of observability of targets in the passive sensor network.

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