Denoising Multisensor Data

Multisensor array processing of noisy measurements has received considerable attention in many areas of signal processing. The optimal processing techniques developed so far usually assume the signal and noise processes are at least wide-sense-stationary, yet a need exists for efficient, effective methods for processing nonstationary signals. While wavelets have proven to be useful tools in dealing with certain nonstationary signals, the way in which wavelets are to be used in the multisensor setting has only recently been considered. In this work we show how multisensor denoising can be carried out in perturbed , narrowband arrays even in the absence of the signal source’s direction of arrival. We show that our proposed blind estimator can be implemented efficiently and robustly employing only wavelet and discrete Fourier transforms while entailing only a small loss in performance.