多扩展目标跟踪中基于多特征优化的传感器控制方法

针对多扩展目标的优化跟踪问题，该文在有限集统计(FISST)理论框架下，提出一种能够综合优化多扩展目标跟踪性能的传感器控制方法。首先，该文给出加权广义最优子模式分配(WGOSPA)距离构造多扩展目标跟踪多特征估计在其统计平均周围的广义离差，进而研究提出多特征融合下的传感器控制最优决策方法，并利用序贯蒙特卡罗(SMC)技术研究传感器控制最优决策过程的数值求解方法，然后利用伽马高斯逆威沙特多伯努利(GGIW-MBer)滤波器实现所提出的传感器控制策略。最后通过仿真实验验证了所提算法的有效性。

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

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.