Sensor control method for star-convex shape multiple extended target tracking

被引：0
|
作者：
Chen H. [1 ]
Li G.-C. [1 ]
Han C.-Z. [2 ]
Du J.-R. [1 ]
机构：
[1] School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou
[2] School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2020年 / 37卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Multi-Bernoulli filter; Multiple extended target tracking; Random hypersurface model; Sensor control;
D O I：
10.7641/CTA.2020.91030
中图分类号：
TN911 [通信理论];
学科分类号：
081002 ;
摘要：
Aiming at the sensor control in multiple extended target tracking, this paper proposes the effective sensor control strategies based on the finite set statistics (FISST) theory and random hypersurface model (RHM) by using multi- Bernoulli (MBer) filter. First, this paper presents the solution ideas of sensor control for joint target shape estimation optimization and target motion state estimation optimization based on the information theory in multiple extended target tracking. Subsequently, this paper gives the detailed implementation of the RHM cubature Kalman Gaussian mixture (GM) cardinality balanced multiple extended target multi-Bernoulli filter. Then, a sensor control decision is proposed through the Cauchy-Schwarz divergence between the GM densities. In addition, this paper derives the GM implementation of the posterior expected number of extended targets (PENET) in detail and proposes a sensor control method using GM-PENET as an evaluation function. Finally, the effectiveness of the proposed methods is verified by the tracking optimization simulations of multiple extended targets with random star-convex shape. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码：2627 / 2637
页数：10
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