Abstract
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.
Highlights
The searching problem for missing targets had begun since the fifties of the last century
In order to increase the probability of detection or minimize the search effort, specialists in this field dived the areas to be searched in a set with identical or different states
Song and Teneketizs [3] determined the optimal search strategies with multiple sensors that maximize the total probability of successful search where the target is hidden in one of a finite set of different cells
Summary
The searching problem for missing targets had begun since the fifties of the last century. El-Hadidy [5] showed that the probability of detecting the first target in the cell j at time interval i is Pij 1 − b i, j, Zij , where Zij is the amount of effort, given that the target is located in cell j.
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