随机有限集联合优化搜索与跟踪
Jointly-Optimized Searching and Tracking with Random Finite Sets
摘要：
本文研究了一组移动Agent对多个移动目标的联合搜索与跟踪问题。目标在监视区域内随机出现和消失，其位置是随机的和未知的。代理具有有限的感测范围，并且接收来自目标的噪声测量。出现了决策和控制问题，其中操作模式（即，搜索或跟踪）以及每个代理在每个时刻的移动性控制动作，必须被确定，以便实现搜索和跟踪的共同目标。我们建立我们的方法随机有限集（RFS）的理论，我们使用贝叶斯多目标随机滤波，同时估计随时间变化的目标数量和它们的状态从一系列的噪声测量。我们制定上述问题作为一个非线性二进制程序（NLBP），并表明它可以近似的遗传算法。最后，为了研究所提出的方法的有效性和性能，我们进行了大量的仿真实验。
英文摘要：
In this paper, we investigate the problem of joint searching and tracking of multiple mobile targets by a group of mobile agents. The targets appear and disappear at random times inside a surveillance region and their positions are random and unknown. The agents have limited sensing range and receive noisy measurements from the targets. A decision and control problem arises, where the mode of operation (i.e., search or track) as well as the mobility control action for each agent, at each time instance, must be determined so that the collective goal of searching and tracking is achieved. We build our approach upon the theory of random finite sets (RFS) and we use Bayesian multi-object stochastic filtering to simultaneously estimate the time-varying number of targets and their states from a sequence of noisy measurements. We formulate the above problem as a non-linear binary program (NLBP) and show that it can be approximated by a genetic algorithm. Finally, to study the effectiveness and performance of the proposed approach we have conducted extensive simulation experiments.
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