This article addresses the problem of deploying a sparse network of sensors for surveillance of moving targets. The sensor networks of interest consist of sensors which perform independent binary detection on a target, and report detections to a central node for fusion. An optimization framework is developed for placement of sensors within a bounded search region, given sensor performance characteristics, prior information on anticipated target characteristics, and a distributed detection criteria. Individual sensor performance is represented parametrically as are priors on target dynamics. Several numerical examples are included that illustrate the utility of the optimization approach.
We consider the optimal deployment of a sparse network of sensors against moving targets, under multiple conflicting objectives of search. The sensor networks of interest consist of sensors which perform independent binary detection on a target, and report detections to a central control authority. A multiobjective optimization framework is developed to find optimal trade-offs as a function of sensor deployment, between the conflicting objectives of maximizing the Probability of Successful Search (P SS ) and minimizing the Probability of False Search (P FS ), in a bounded search region of interest. The search objectives are functions of unknown sensor locations (represented parametrically by a probability density function), given sensor performance parameters, statistical priors on target behavior, and distributed detection criteria. Numerical examples illustrating the utility of this approach for varying target behaviors are given.
We consider the problem of determining the correct mance that connect the design parameters of sensor numbers, set of sensors to employ in the design of large area undersea sensor detection threshold, and fusion strategy to the design surveillance sensor networks. As sensor technologies evolve, such objectives of maximal detection performance, minimal false networks are becoming increasingly practical. In turn, optimal alarms, and minimal cost. The field level performance of disselection of the number and type of sensors to deploy becomes an increasingly nontrivial process. Choices of field level detection tributed sensor networks involves more than the concatenation and false alarm performance, as well as cost, all enter into this of numerous individual sensor detection decisions, specifically, tradeoff decision space. In particular, the multiobjective nature it involves the examination of multiple sensor detections that of the problem leads to families of "optimal" solutions that each all originate from the same target over a fixed interval of time.correspond to different tradeoffs between these often conflicting objectives. In this paper, we address these tradeoffs using a simple We refer to this process as track-before-detect (see [5] for a model of multi-sensor search performance and show the tradeoffs description), since the final determination of a target presence as Pareto efficient sets of solutions that satisfy system constraints. is not made until multiple sensor detections occur and areWe also provide a means to determine the specific characteristics kinematically consistent with target motion (the track). Thus, of the systems that lead to different design choices and explain the process is more of a process of search (searching for the how these designs perform as comprehensive search systems.combination of detections that are consistent with a target
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