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Multiscale Adaptive Search | IEEE Journals & Magazine | IEEE Xplore
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Abstract:

We present a continuous-space multiscale adaptive search (MAS) algorithm for single or multiple searchers that finds a stationary target in the presence of uncertainty in...Show More

Abstract:

We present a continuous-space multiscale adaptive search (MAS) algorithm for single or multiple searchers that finds a stationary target in the presence of uncertainty in sensor diameter. The considered uncertainty simulates the influence of the changing environment and terrain as well as adversarial actions that can occur in practical applications. When available, information about the foliage areas and a priori distribution of the target position is included in the MAS algorithm. By adapting to various uncertainties, MAS algorithm reduces the median search time to find the target with a probability of detection of at least PD and a probability of false alarm of at most PFA. We prove that MAS algorithm discovers the target with the desired performance bounds PD and PFA. The unique features of the MAS algorithm are realistic second-order dynamics of the mobile sensors that guarantees uniform coverage of the surveyed area and a two-step Neyman-Pearson-based decision-making process. Computer simulations show that MAS algorithm performs significantly better than lawnmower-type search and billiard-type random search. Our tests suggest that the median search time in the MAS algorithm may be inversely proportional to the number of participating searchers. As opposed to lawnmower search, the median search time in the MAS algorithm depends only logarithmically on the magnitude of uncertainty.
Page(s): 1076 - 1087
Date of Publication: 07 February 2011

ISSN Information:

PubMed ID: 21303742

References

References is not available for this document.