This paper presents the advantages of using distributed arrays with the beamspace implementation of a wideband Capon algorithm. Distributed arrays have recently demonstrated a decreased sensitivity to environmental coherence loss and array mismatches. In an attempt to build upon this robustness, beamspace preprocessing is applied in this paper. Beamspace allows sector-focused beamforming and reduced computational complexity. A unique property of this implementation of beamspace, namely being indifferent to losses of entire channels of data, has been demonstrated. This implementation of beamspace to Capon beamforming does not require
beam orthogonalization, thus saving computational time, especially in the whitening process.
The problem of detection and localization of multiple acoustic sources using unattended passive sensors is considered.
Existing wideband Capon direction of arrival (DOA) estimation methods typically fail to accurately
detect and resolve multiple closely spaced sources in presence of model mismatches and wavefront perturbations
caused by senor location errors and near-field effects. This paper applies a set of adaptive beamforming methods
in reduced dimension subspace for non-ideal acoustic array sensing scenarios. A robust wideband Capon method
is studied to account for the inherent uncertainties in the array steering vector. To improve the resolution
within a sector of interest the beamspace method is extended and applied to this problem. These methods
are then implemented and benchmarked on real acoustic signatures of multiple ground vehicles moving in tight