Researches on low frequency source location based on partially adaptive subarray focused beamformer under finite sample-support

We introduce a partially adaptive subarray focused beamformer for measuring the distribution image of ship´s noise sources in a reduced-dimensional space. When only a small number of snapshots is available for estimating the spatial correlation matrix, reduced-rank solutions provide high performance to common methods. We study the detection performance of the partially adaptive subarray detector compared with the full array detector and a reduced-rank method called Krylov subspace. It is demonstrated, via simulation, that the minimum-variance, partially adaptive subarrays beamformer is a solid approach in small snapshots, low signal-to-noise (SNR) situations. In addition, data of a fiber optic hydrophone array experiment has been used to validate the method.