We consider the problem of estimating and localizing a set of unknown real-valued signals simultaneously arriving at a sensor array composed of elements spaced far enough apart to induce measurable baseband time difference of arrivals.
Blindly Estimating and Localizing Multiple Signals from the Matrix Channel Impulse Response
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We consider the problem of estimating and localizing a set of unknown real-valued signals simultaneously arriving at a sensor array composed of elements spaced far enough apart to induce measurable baseband time difference of arrivals. The delayed and mixed line-of-sight signals form a convolutive mixture model. We recover the source signals through multichannel blind deconvolution (MBD) in which the channel impulse response estimate provides the direction of arrival (DOA) information. We consider the two cases in which either the source signal probability density is known or the attractor space for which the source density resides is known. The new MBD algorithm we present works with known source densities to minimize the symmetric Kullback-Liebler distance to the standardized equalized output. Simulation results show successful performance on acoustic data