Constrained robust submodular sensor selection with application to multistatic sonar arrays. Issue 12 (1st December 2017)

• Record Type: Journal Article
• Authors: Powers, Thomas; Krout, David W.; Bilmes, Jeff; Atlas, Les
• Is Part Of: IET radar, sonar & navigation. Volume 11:Issue 12(2017)
• Page Start: 1776
• Page End: 1781
• Publication Date: 2017-12-01
• Subjects: sonar arrays -- sensor arrays -- approximation theory -- optimisation -- probability; independence graph formation -- submodular objective function -- generalised saturate -- GENSAT -- PSO -- ping sequence optimisation -- active multistatic sonar array -- probability -- pairwise independence -- arbitrary independence pattern -- ingrained independence structure -- constrained robust submodular sensor selection; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2017.0075
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : The authors develop a framework to select a subset of sensors from a field in which the sensors have an ingrained independence structure. Given an arbitrary independence pattern, the authors construct a graph that denotes pairwise independence between sensors, which means those sensors may operate simultaneously without interfering. The set of all fully‐connected subgraphs (cliques) of this independence graph forms the independent sets of matroids over which the authors maximise the average and minimum of a set of submodular objective functions. The average case is submodular, so it can be approximated. The minimum case is both non‐submodular and inapproximable. The authors propose a novel algorithm GENSAT that exploits submodularity and, as a result, returns a near‐optimal solution with approximation guarantees on a relaxed problem that are within a small factor of the average case scenario. The authors apply this framework to ping sequence optimisation for active multistatic sonar arrays by maximising sensor coverage for average and minimum case scenarios and derive lower bounds for minimum probability of detection for a fractional number of targets. In these ping sequence optimisation simulations, GENSAT exceeds the fractional lower bounds and reaches near‐optimal performance, and submodular function optimisation vastly outperforms traditional approaches and nearly achieves optimal performance.