Track-Oriented Multiple Hypothesis Tracking Based on Tabu Search and Gibbs Sampling

被引：22

作者：

He, Shaoming ^{[1
]}

Shin, Hyo-Sang ^{[1
]}

Tsourdos, Antonios ^{[1
]}

机构：

[1] Cranfield Univ, Sch Aerosp Transport & Mfg, Cranfield MK43 0AL, Beds, England

来源：

IEEE SENSORS JOURNAL | 2018年 / 18卷 / 01期

关键词：

Multiple target tracking; multiple hypothesis tracking; best global hypothesis generation; Tabu search; Gibbs sampling; DATA ASSOCIATION; TARGET TRACKING; ALGORITHM; RADAR; SONAR; IMPLEMENTATION; RELAXATION; FILTER; JPDA;

D O I：

10.1109/JSEN.2017.2758846

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In order to circumvent the curse of dimensionality in multiple hypothesis tracking data association, this paper proposes two efficient implementation algorithms using Tabu search and Gibbs sampling. As the first step, we formulate the problem of generating the best global hypothesis in multiple hypothesis tracking as the problem of finding a maximum weighted independent set of a weighted undirected graph. Then, the metaheuristic Tabu search with two basic movements is designed to find the global optimal solution of the problem formulated. To improve the computational efficiency, this paper also develops a sampling based algorithm based on Gibbs sampling. The problem formulated for the Tabu search-based algorithm is reformulated as a maximum product problem to enable the implementation of Gibbs sampling. The detailed algorithm is then designed and the convergence is also theoretically analyzed. The performance of the two algorithms proposed are verified through numerical simulations and compared with that of a mainstream multiple dimensional assignment implementation algorithm. The simulation results confirm that the proposed algorithms significantly improve the computational efficiency while maintaining or even enhancing the tracking performance.

引用

页码：328 / 339

页数：12

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