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
相关论文
共 29 条
  • [21] CASTANON D A, CARIN L., Foundations and Applications of Sensor Management, (2007)
  • [22] GILHOLM K, SALMOND D., Spatial distribution model for tracking extended objects, IEE Proceedings-Radar, Sonar & Navigation, 152, 5, pp. 364-371, (2005)
  • [23] GILHOLM K, GODSILL S, MASKELL S, Et al., Poisson models for extended target and group tracking, The 2005 Proceedings of Signal and Data Processing of Small Targets, pp. 230-241, (2005)
  • [24] ARASARATNAM I, HAYKIN S, HURD T R., Cubature Kalman filtering for continuous-discrete systems: Theory and simulations, IEEE Transactions on Signal Processing, 58, 10, pp. 4977-4993, (2010)
  • [25] KAMPA K, HASANBELLIU E, PRINCIPE J C., Closed-form Cauchy-Schwarz PDF divergence for mixture of Gaussians, The 2011 International Joint Conference on Neural Networks, pp. 2578-2585, (2011)
  • [26] GRANSTROMK LUNDQUIST C, ORGUNER U., A Gaussian mixture PHD filter for extended target tracking, The 13th International Conference on Information Fusion, pp. 1-8, (2010)
  • [27] CHEN Hui, HE Zhongliang, DENG Dongming, Et al., Sensor control using Cauchy-Schwarz divergence via Gaussian mixture multi-Bernoulli filter, Acta Electronica Sinica, 48, 4, pp. 706-716, (2020)
  • [28] MAHLER R P S, ZAJIC T R., Probabilistic objective functions for sensor management, Proceedings of the Signal Processing, Sensor Fusion, and Target Recognition XIII, pp. 233-244, (2004)
  • [29] SCHUHMACHER D, VO B T, VO B N., A consistent metric for performance evaluation of multi-object filters, IEEE Transactions on Signal Processing, 56, 8, pp. 3447-3457, (2008)
123