This paper presents the searching algorithm to detect a Markovian target which moves randomly in M-cells. Our algorithm is based on maximizing the discounted effort reward search. At each fixed number of time intervals, the search effort is a random variable with a normal distribution. More than minimizing the non-detection probability of the targets at time interval i, we seek for the optimal distribution of the search effort by maximizing the discounted effort reward search. We present some special cases of one Markovian and hidden target. Experimental results for a Markovian, hidden target are obtained and compared with the cases of applying and without applying the discounted effort reward search.

中文翻译：

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基于最大化折扣努力奖励搜索的马尔可夫目标检测算法

本文提出了一种搜索算法来检测在 M 单元格中随机移动的马尔可夫目标。我们的算法基于最大化折扣努力奖励搜索。在每个固定数量的时间间隔内，搜索工作量是一个具有正态分布的随机变量。除了在时间间隔 i 最小化目标的未检测概率之外，我们还通过最大化折扣努力奖励搜索来寻求搜索努力的最佳分布。我们展示了一个马尔可夫和隐藏目标的一些特殊情况。获得了马尔可夫隐藏目标的实验结果，并与应用和不应用折扣努力奖励搜索的情况进行了比较。