Adaptive beamforming of sensor arrays immersed into reverberant fields can easily result in the cancellation of the useful signal because of the temporal correlation existing among the direct and the reflected path signals. Wideband beamforming can somewhat mitigate this phenomenon, but adaptive solutions based on the minimum variance (MV) criterion remain nonrobust in many practical applications, such as multimedia systems, underwater acoustics, and seismic prospecting. In this paper, a steered wideband adaptive beamformer, optimized by a novel concentrated maximum likelihood (NIL) criterion in the frequency domain, is presented and discussed in the light of a very general reverberation model. It is shown that NIL beamforming can alleviate the typical cancellation problems encountered by adaptive MV beamforming and preserve the intelligibility of a wideband and colored source signal under interference, reverberation, and propagation mismatches. The difficult optimization of the NIL cost function, which incorporates a robustness constraint to prevent signal cancellation, is recast as an iterative least squares problem through the concept of descent in the neuron space, which was originally developed for the training of multilayer neural networks. Finally, experiments with computer-generated and real-world data demonstrate the superior performance of the proposed beamformer with respect to its MV counterpart.

Robust ML wideband beamforming in reverberant fields / DI CLAUDIO, Elio; Parisi, Raffaele. - In: IEEE TRANSACTIONS ON SIGNAL PROCESSING. - ISSN 1053-587X. - STAMPA. - 51:2(2003), pp. 338-349. [10.1109/tsp.2002.806866]

Robust ML wideband beamforming in reverberant fields

DI CLAUDIO, Elio;PARISI, Raffaele
2003

Abstract

Adaptive beamforming of sensor arrays immersed into reverberant fields can easily result in the cancellation of the useful signal because of the temporal correlation existing among the direct and the reflected path signals. Wideband beamforming can somewhat mitigate this phenomenon, but adaptive solutions based on the minimum variance (MV) criterion remain nonrobust in many practical applications, such as multimedia systems, underwater acoustics, and seismic prospecting. In this paper, a steered wideband adaptive beamformer, optimized by a novel concentrated maximum likelihood (NIL) criterion in the frequency domain, is presented and discussed in the light of a very general reverberation model. It is shown that NIL beamforming can alleviate the typical cancellation problems encountered by adaptive MV beamforming and preserve the intelligibility of a wideband and colored source signal under interference, reverberation, and propagation mismatches. The difficult optimization of the NIL cost function, which incorporates a robustness constraint to prevent signal cancellation, is recast as an iterative least squares problem through the concept of descent in the neuron space, which was originally developed for the training of multilayer neural networks. Finally, experiments with computer-generated and real-world data demonstrate the superior performance of the proposed beamformer with respect to its MV counterpart.
2003
acoustics; adaptive beamforming; cepstrum; frequency domain analysis; least squares approximations; maximum likelihood; maximum likelihood estimation; minimum variance; multilayer neural networks; multipath; multipath propagation; neural network learning; optimization; real time systems; reverberation; ridge regression; robust algorithms; sensor arrays; signal interference; wideband beamforming; wideband coherent focusing
01 Pubblicazione su rivista::01a Articolo in rivista
Robust ML wideband beamforming in reverberant fields / DI CLAUDIO, Elio; Parisi, Raffaele. - In: IEEE TRANSACTIONS ON SIGNAL PROCESSING. - ISSN 1053-587X. - STAMPA. - 51:2(2003), pp. 338-349. [10.1109/tsp.2002.806866]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/248940
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