Maximum-likelihood spatial spectrum estimation in dynamic environments with a short maneuverable array.

This work concerns the development of field directionality mapping algorithms for short acoustic arrays on mobile maneuverable platforms that avoid the left/right ambiguities and endfire resolution degradation common to longer non-maneuverable line arrays. In this paper, it is shown that short maneuverable arrays can achieve a high fraction of usable bearing space for target detection in interference-dominated scenarios, despite their lower array gain against diffuse background noise. Two narrowband techniques are presented which use the expectation-maximization maximum likelihood algorithm under different models of the time-varying field directionality. The first, derivative based maximum likelihood, uses a deterministic model while the second, recursive Bayes maximum likelihood, uses a stochastic model for the time-varying spatial spectrum. In addition, a broadband extension is introduced that incorporates temporal spectral knowledge to suppress ambiguities when the average sensor array spacing is greater than a half-wavelength. Dynamic multi-source simulations demonstrate the ability of a short, maneuvering array to reduce array ambiguities and spatial grating lobes in an interference dominated environment. Monte Carlo evaluation of receiver operating characteristics is used to evaluate the improvement in source detection achieved by the proposed methods versus conventional broadband beamforming.

Duke Scholars

Author Jeffrey L. Krolik Electrical and Computer Engineering