Recent advances in multisensor multitarget tracking using random finite set

2021 ◽  
Vol 22 (1) ◽  
pp. 5-24
Author(s):  
Kai Da ◽  
Tiancheng Li ◽  
Yongfeng Zhu ◽  
Hongqi Fan ◽  
Qiang Fu
Keyword(s):  
Multitarget Tracking ◽  
Random Finite Set ◽  
Recent Advances ◽  
Finite Set

scholarly journals Finite Set Statistics Based Multitarget Tracking.

2017 ◽  
Author(s):  
Keith LeGrand ◽  
Raymond H. Byrne ◽  
Pavan Datta ◽  
David K. Melgaard ◽  
Johnathan Mulcahy-Stanislawczyk
Keyword(s):  
Multitarget Tracking ◽  
Finite Set

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 “Statistics 103” for Multitarget Tracking

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 202 ◽  
Author(s):  
Ronald Mahler
Keyword(s):  
Point Process ◽  
Information Fusion ◽  
Theoretical Foundation ◽  
Measure Theory ◽  
Multitarget Tracking ◽  
Mathematical Abstraction ◽  
Finite Set ◽  
Theoretic Point

The finite-set statistics (FISST) foundational approach to multitarget tracking and information fusion was introduced in the mid-1990s and extended in 2001. FISST was devised to be as “engineering-friendly” as possible by avoiding avoidable mathematical abstraction and complexity—and, especially, by avoiding measure theory and measure-theoretic point process (p.p.) theory. Recently, however, an allegedly more general theoretical foundation for multitarget tracking has been proposed. In it, the constituent components of FISST have been systematically replaced by mathematically more complicated concepts—and, especially, by the very measure theory and measure-theoretic p.p.’s that FISST eschews. It is shown that this proposed alternative is actually a mathematical paraphrase of part of FISST that does not correctly address the technical idiosyncrasies of the multitarget tracking application.

scholarly journals Random Finite Set Based Bayesian Filtering with OpenCL in a Heterogeneous Platform

Sensors ◽  
2017 ◽  
Vol 17 (4) ◽  
pp. 843 ◽  
Author(s):  
Biao Hu ◽  
Uzair Sharif ◽  
Rajat Koner ◽  
Guang Chen ◽  
Kai Huang ◽  
...  
Keyword(s):  
Bayesian Filtering ◽  
Heterogeneous Platform ◽  
Random Finite Set ◽  
Finite Set

scholarly journals Relating Random Vector and Random Finite Set Estimation in Navigation, Mapping, and Tracking

2017 ◽  
Vol 65 (17) ◽  
pp. 4609-4623 ◽  
Author(s):  
Keith Y. K. Leung ◽  
Felipe Inostroza ◽  
Martin Adams
Keyword(s):  
Random Vector ◽  
Random Finite Set ◽  
Set Estimation ◽  
Finite Set
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