Abstract
The localization of noise sources from a specified direction may often be accomplished with an array of sensors. One commonly used processor consists of delay and add networks: a conventional beamformer, however its spectrum suffers from the Rayleigh resolution and its performance is highly degraded, specially in lower frequency range. In the communication, the performance of some typical high‐resolution sensor array processing algorithms: Minimum Variance, MUSIC, Mini‐Norm algorithms are investigated for wideband source location. Their performances are compared with a new source localization algorithm which is based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. The key of the method is the use of the SVD for data reduction and the formulation of a joint multiple‐sample sparse representation problem in the signal subspace domain. Increased resolution and improved robustness to noise is obtained with this algorithm applied to vario...
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