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| Eigenstructure-based direction-of-arrival (DOA) estimation algorithms such as Multiple Signal Classification (MUSIC), Root-MUSIC, Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), encounter great difficulty in the presence of perfectly correlated incident signals. For an array composed of a number of translational invariant subarrays such as a uniform linear scalar-sensor array, this problem can be solved by spatial smoothing. An array of identically oriented acoustic vector-sensors can be grouped into four coupled subarrays of identical grid geometry, respectively corresponding to the pressure sensors and differently oriented velocity-sensors. These four subarrays are angular invariant dependent only on signals’ direction cosines and an angular smoothing can be exploited for source decorrelation. In this paper, the performance of root-MUSIC incorporated with angular smoothing for correlated source direction finding with an L-shaped acoustic vector-sensor array is analyzed in terms of the overall root mean-square errors (RMSE) of DOA estimates. We derive the analytical expression of the RMSE and compare it with simulation results and that of spatial smoothing for a rectangular pressure-sensor array instead. |
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| Keywords:Antenna arrays, array signal processing, direction-of-arrival estimation, acoustic array |
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