Array processing of underwater acoustic sensors using weighted Fourier integral method

The spatial processing of platform-mounted acoustic sensors is complicated by platform generated noise. The weighted Fourier integral method (WFIM) beamformer has been shown to perform well in such cases, by reducing this coloured noise which is received by the sensors. In this paper, WFIM is modified by using maximum likelihood estimates of the spatial correlation lags. The proposed technique exploits the work of Burg et al. (Proc. IEEE, vol.70, no.9, Sept. 1982, pp.963-74) and estimates these lags for a sparse redundant linear array of hydrophones. The results obtained illustrate the significant performance improvement obtainable over that of the least-squares lag estimation procedure utilised in WFIM. The proposed approach “better” estimates the contributions of missing sensors, in the sparse array, and performance approaching the full array, with extra sensors, is attained. A beamformer which adaptively weights the covariance lags is also proposed and preliminary results are presented