多传感器控制用于多目标跟踪的Cauchy-Schwarz散度应用
Multi-Sensor Control for Multi-Target Tracking Using Cauchy-Schwarz Divergence
AI总结：
本文提出基于Cauchy-Schwarz散度的多传感器控制方法，用于多目标跟踪，通过标签随机有限集在传感器网络中实现最优和次优的联合与独立决策方法。
摘要：
本文针对传感器网络系统中的多传感器控制多目标跟踪问题，基于信息论中的Cauchy-Schwarz散度，提出两种新的多传感器控制方法。该散度具有闭合形式解，适用于GLMB密度。首先提出的联合决策方法（JDM）最优且性能良好，而其次优的独立决策方法（IDM）计算更快且计算量更小。通过挑战性情境的仿真验证了两种方法的有效性。
英文摘要：
The paper addresses the problem of multi-sensor control for multi-target tracking via labelled random finite sets (RFS) in the sensor network systems. Based on an information theoretic divergence measure, namely Cauchy-Schwarz (CS) divergence which admits a closed form solution for GLMB densities, we propose two novel multi-sensor control approaches in the framework of generalized Covariance Intersection (GCI). The first joint decision making (JDM) method is optimal and can achieve overall good performance, while the second independent decision making (IDM) method is suboptimal as a fast realization with smaller amount of computations. Simulation in challenging situation is presented to verify the effectiveness of the two proposed approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
