Performance of certain direction of arrival (DOA) estimation
algorithms using a uniform linear array of scalar sensors has been
analyzed and reported. Velocity hydrophones, which measure one Cartesian
component of the three-dimensional particle velocity vector of the
incident wave field, were first reported in underwater acoustics
literature in 1956. But the importance of simultaneously measuring both
pressure and particle velocity was recognized almost three decades later
when Mann etc. published a classic paper addressing the fundamentals of
energy transfer in acoustic field.
The relevance of this new approach was further elucidated by G. L. D'Spain. The technology of vector hydrophones or acoustic vector sensors (AVS), consisting of three orthogonally oriented velocity hydrophones plus a pressure hydrophone, all spatially collocated, for simultaneous measurement of pressure and particle velocities is of relatively recent vintage. It has attracted considerable interest, especially within the signal processing community, after Nehorai and Paldi presented the AVS measurement model and a method for localising acoustic sources using an array of such sensors. Hochwald and Nehorai later addressed the important issue of identifiability with vector sensors, namely, the bound on the number of sources identifiable in a class of array processing models with multiple parameters and signals per source. Hawkes and Nehorai have quantified the impact of sensor locations on the performance (Cramer Rao Bound) of an AVS array.
In a separate paper they have defined some performance measures such as mean square angular error (MSAE) and mean square range error (MSRE) and derived asymptotic lower bounds on these quantities in terms of the CRB. The same authors have also discussed the correlations between the measurements within an AVS as well as between two spatially separated acoustic vector sensors. They have also analysed the effect of a reflecting boundary and presented a fast method for localising a wideband source using a distributed AVS array.
A host of beamforming techniques using vector sensors have also appeared in the literature. Wong and Zoltowski introduced ESPRIT-based closed form source localisation for arbitrarily spaced 3D arrays of vector hydrophones. They have also proposed aperture extension using a uniform rectangular array of vector hydrophones spaced much farther than half a wavelength to achieve enhanced array resolution and direction-finding precision. Narrowband Beamforming and Capon direction estimation methods with an AVS array have been discussed by Hawkes and Nehorai. Root-MUSIC based azimuth-elevation DOA estimation technique using uniformly spaced velocity hydrophones has been developed by Wong and Zoltowski. They have also proposed a blind MUSIC-based source localisation algorithm using arbitrary 3D array of vector hydrophones. The same authors have also presented a near-field/far-field azimuth-elevation angle estimation algorithm using a single vector hydrophone.
The relevance of this new approach was further elucidated by G. L. D'Spain. The technology of vector hydrophones or acoustic vector sensors (AVS), consisting of three orthogonally oriented velocity hydrophones plus a pressure hydrophone, all spatially collocated, for simultaneous measurement of pressure and particle velocities is of relatively recent vintage. It has attracted considerable interest, especially within the signal processing community, after Nehorai and Paldi presented the AVS measurement model and a method for localising acoustic sources using an array of such sensors. Hochwald and Nehorai later addressed the important issue of identifiability with vector sensors, namely, the bound on the number of sources identifiable in a class of array processing models with multiple parameters and signals per source. Hawkes and Nehorai have quantified the impact of sensor locations on the performance (Cramer Rao Bound) of an AVS array.
In a separate paper they have defined some performance measures such as mean square angular error (MSAE) and mean square range error (MSRE) and derived asymptotic lower bounds on these quantities in terms of the CRB. The same authors have also discussed the correlations between the measurements within an AVS as well as between two spatially separated acoustic vector sensors. They have also analysed the effect of a reflecting boundary and presented a fast method for localising a wideband source using a distributed AVS array.
A host of beamforming techniques using vector sensors have also appeared in the literature. Wong and Zoltowski introduced ESPRIT-based closed form source localisation for arbitrarily spaced 3D arrays of vector hydrophones. They have also proposed aperture extension using a uniform rectangular array of vector hydrophones spaced much farther than half a wavelength to achieve enhanced array resolution and direction-finding precision. Narrowband Beamforming and Capon direction estimation methods with an AVS array have been discussed by Hawkes and Nehorai. Root-MUSIC based azimuth-elevation DOA estimation technique using uniformly spaced velocity hydrophones has been developed by Wong and Zoltowski. They have also proposed a blind MUSIC-based source localisation algorithm using arbitrary 3D array of vector hydrophones. The same authors have also presented a near-field/far-field azimuth-elevation angle estimation algorithm using a single vector hydrophone.
