Abstract
In this paper, a novel time-phased directional-sensor network deployment strategy is presented for the mobile-target search problem, e.g., wilderness search and rescue (WiSAR). The proposed strategy uses probabilistic target-motion models combined with a variation of a standard direct search algorithm to plan the optimal locations of directional-sensors which maximize the likelihood of target detection. A linear sensing model is employed as a simplification for directional-sensor network deployment planning, while considering physical constraints, such as on-time sensor deliverability. Extensive statistical simulations validated our method. One such illustrative experiment is included herein to demonstrate the method’s operation. A comparative study was also carried out, whose summary is included in this paper, to highlight the tangible improvement of our approach versus three traditional deployment strategies: a uniform, a random, and a ring-of-fire type deployment, respectively.
Highlights
This paper considers the mobile-target search problem, which has been addressed in the literature using a variety of solutions via sensor networks and robotic UAVs/UGVs
A comparative study contrasting the performance of a sensor network planned by the proposed strategy against the performance of Extensive simulated search experiments were performed to demonstrate the effectiveness of the proposed deployment methodology
We present a novel directional static-sensor deployment strategy for mobile-target detection
Summary
Wireless sensor networks (WSNs) have been used to effectively monitor various physical phenomena in real-time, where they collect, transmit, and process information in an on-line manner [1]. Their applications include environmental monitoring, [2,3,4,5,6], border security [7], target tracking and localization [8,9,10], urban search and rescue (USAR) [11,12] and lost person detection in wilderness search and rescue (WiSAR) [13,14]. Often assumes omni-directional sensing models for network-topography planning. This assumption may not hold true for many sensor types, such as video or infrared, which have a directional sensing range [15]. This contrasts with the sensing area of an omnidirectional sensor, which is a (complete) disk, which is formed when the angular range of a directional sensor is very wide
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