Paper
16 April 2008 Joint path planning and sensor subset selection for multistatic sensor networks
Author Affiliations +
Abstract
Due to the availability of cheap passive sensors, it is possible to deploy a large number of them for tracking purposes in anti-submarine warfare (ASW). However, modern submarines are quiet and difficult to track with passive sensors alone. Multistatic sensor networks, which have few transmitters (e.g., dipping sonars) in addition to passive receivers, have the potential to improve the tracking performance. We can improve the performance further by moving the transmitters according to existing target states and any possible new targets. Even though a large number of passive sensors are available, due to frequency, processing power and other physical limitations, only a few of them can be used at any one time. Then the problems are to decide the path of the transmitters and select a subset from the available passive sensors in order to optimize tracking performance. In this paper, the PCRLB, which gives a lower bound on estimation uncertainty, is used as the performance measure. We present an algorithm to decide jointly the optimal path of the movable transmitters, by considering their operational constraints, and the optimal subset of passive sensors that should be used at each time steps for tracking multiple, possibly time-varying, number of targets. Finding the optimal solution in real time is difficult for large scale problems, and we propose a genetic algorithm based suboptimal solution technique. Simulation results illustrating the performance of the proposed algorithm are also presented.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
R. Tharmarasa, Tom Lang, and T. Kirubarajan "Joint path planning and sensor subset selection for multistatic sensor networks", Proc. SPIE 6969, Signal and Data Processing of Small Targets 2008, 69690T (16 April 2008); https://doi.org/10.1117/12.779217
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Cited by 1 scholarly publication.
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KEYWORDS
Sensors

Transmitters

Receivers

Sensor networks

Passive sensors

Genetic algorithms

Target detection

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