Sensor Control Based on Multiple Feature Optimization in Multiple Extended Targets Tracking

CHEN Hui; WEI Fengqi; HAN Chongzhao

doi:10.11999/JEIT211244

Volume 45 Issue 1

Jan. 2023

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CHEN Hui, WEI Fengqi, HAN Chongzhao. Sensor Control Based on Multiple Feature Optimization in Multiple Extended Targets Tracking[J]. Journal of Electronics & Information Technology, 2023, 45(1): 191-199. doi: 10.11999/JEIT211244

Citation:

CHEN Hui, WEI Fengqi, HAN Chongzhao. Sensor Control Based on Multiple Feature Optimization in Multiple Extended Targets Tracking[J]. Journal of Electronics & Information Technology, 2023, 45(1): 191-199. doi: 10.11999/JEIT211244

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Sensor Control Based on Multiple Feature Optimization in Multiple Extended Targets Tracking

doi: 10.11999/JEIT211244

1.
School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
2.
School of Automation Science and Engineering, Xi'an Jiaotong University, Xi’an 710049, China

Funds: The National Natural Science Foundation of China (62163023, 61763029, 61873116), The Industrial Support Project of Education Department of Gansu Province (2021CYZC-02)

Received Date: 2021-11-09
Accepted Date: 2022-06-22
Rev Recd Date: 2022-06-02

Available Online: 2022-06-29

Publish Date: 2023-01-17

Abstract

Abstract

Focusing on the optimal tracking problem of multiple extended targets, a sensor control method is proposed, which can comprehensively optimize the tracking performance of multiple extended targets in the framework of FInite Set STatistics (FISST). First, a Weighted Generalized Optimal Sub-Pattern Assignment (WGOSPA) distance is proposed to construct multiple extended targets tracking multiple feature estimation of generalized dispersion around its statistical average. In addition, an optimal decision-making method of sensor control through the multi-characteristic fusion is studied and proposed. Furthermore, the numerical solution method of the optimal decision-making process of sensor control is studied by using Sequential Monte Carlo (SMC) technology. Then, the proposed sensor control strategy is realized by using Gamma Gaussian Inverse Wishart Multi-Bernoulli (GGIW-MBer) filter. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments.
- Sensor control,
- Multiple extended targets tracking,
- Evaluation function,
- Finite set statistics,
- Gamma Gaussian Inverse Wishart mixture

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References(30)

References

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