ACOUSTIC VECTOR-SENSOR BEAMFORMING AND CAPON DIRECTION ESTIMATION

We examine the improvement attained by using acoustic vector sensors f or direction-of-arrival (DOA) estimation, instead of traditional press ure sensors, via optimal performance bounds and particular estimators. By examining the Cramer-Rao bound in the case of a single source, we show that a vector-sensor array's smaller estimation error is a result of two distinct phenomena: 1) an effective increase in signal-to-nois e ratio due to a greater number of measurements of phase delays betwee n sensors and 2) direct measurement of the DOA information contained i n the structure of the velocity field due to the vector sensors' direc tional sensitivity. Separate analysis of these two phenomena allows us to determine the array size, array shape, and SNR conditions under wh ich the use of a vector-sensor array is most advantageous and to quant ify that advantage, By extending the beamforming and Capon direction e stimators to vector sensors, we find that the vector sensors' directio nal sensitivity removes all bearing ambiguities. In particular, even s imple structures such as linear arrays can determine both azimuth and elevation, and spatially undersampled regularly spaced arrays may be e mployed to increase aperture and, hence, performance. Large sample app roximations to the mean-square error matrices of the estimators are de rived and their validity is assessed by Monte Carlo simulation.