基于多目标均方误差界的多传感器控制算法  

Multi-Sensor Control Based on Multi-Target Mean Square Error Bound

作　　者：连峰[1] 侯利明 刘静[1] 韩崇昭[1] LIAN Feng;HOU Li-Ming;LIU Jing;HAN Chong-Zhao(Ministry of Education Key Laboratory for Intelligent Networks and Network Security(MOE KLINNS),School of Automation Science and Engineering,Xi'an Jiaotong University,Xi'an 710049)

机构地区：[1]西安交通大学自动化科学与工程学院智能网络与网络安全教育部重点实验室,西安710049

出　　处：《自动化学报》2020年第10期2177-2190,共14页Acta Automatica Sinica

基　　金：国家重点基础研究发展计划(973计划)(2013CB329405);国家自然科学基金(61473217,61573276,61873116)。

摘　　要：提出了一种新的基于集中式处理结构的有约束多传感器控制算法.该算法将多目标均方误差界作为传感器控制的代价函数.为了应用信息不等式得到该误差界,2阶最优子模式分配测度被用于度量状态集和其估计集间的误差,并采用δ-广义标签多伯努利滤波器执行多目标Bayes递推.混合罚函数法和复合形法被用来降低求解该有约束优化问题的计算量.仿真结果表明对于由多个不同观测性能传感器组成的带约束条件的控制系统,本方法的跟踪精度显著优于柯西–施瓦茨散度法;并且当传感器个数较多时,混合罚函数和复合形法的计算时间相比穷尽搜索法显著缩短而跟踪精度损失很小.The paper proposes a new constrained multi-sensor control algorithm based on the centralized processing architecture. In this method, a multi-target mean-square error bound is served as cost function of sensor control. In order to derive the bound by using the information inequality, the error between state set and its estimation is measured by the 2 nd-order optimal sub-pattern assignment metric while the multi-target Bayes recursion is performed by using aδ-generalized labeled multi-Bernoulli filter. Mixed penalty function method and complex method are used to reduce the computation cost of solving the constrained optimization problem. Simulation results show that for the constrained multisensor control system with different observation performance, our method significantly outperforms the Cauchy-Schwarz divergence method in tracking precision. Besides, when the number of sensors is relatively large, the computation time of the mixed penalty function and complex methods is much shorter than that of the exhaustive search method at the expense of completely acceptable loss of tracking accuracy.

关 键 词：多传感器控制 标签随机有限集 多目标跟踪 贝叶斯估计 误差界 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置] TP273[自动化与计算机技术—控制科学与工程]