Detection of a Randomly Hidden Target in Constrained Domains: A Model with Discounted Effort-Driven Incentives
Detection of a Randomly Hidden Target in Constrained Domains
DOI:
https://doi.org/10.26713/cma.v17i1.3438Keywords:
Detection model, Internal truncated distribution, Stability of the minimum search effort, Detection probability, Normal distributionAbstract
In this work, a search region is a bounded interval on the line. This interval is divided into a number of small subintervals. The target probability in each subinterval is determined from the internal truncation method of the double truncated distribution of the target position, where the sub-intervals that had little chance of containing the hidden target were eliminated. Due to this uncertainty principle, we can apply the discount effort-reward search parameter in the detection probability function. We solve this discrete problem to determine the least amount of effort needed to detect a target, where this effort is constrained by a normal distribution. Furthermore, we determine the target detection probability's maximum value and examine the stability of the minimal search effort. We provide an example to demonstrate the usefulness and relevance of our model.
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