Abstract

1. Acoustic vector sensor (AVS) Pressure sensor Particle velocity sensor ACOUSTIC SOURCE LOCALIZATION USING ACOUSTIC VECTOR SENSORS Joshua York , Patricio S. La Rosa, Ed Richter, and Arye Nehorai Department of Electrical and Systems Engineering Azimuth and elevation estimation Abstract Experimental Setup and GUI Measurement model Acoustic source Acoustic source Illustration of the main components of our experimental setup An acoustic vector sensor (AVS) measures all the three components of the acoustic particle velocity and the pressure at a single point in space. Through real experiments, the study evaluated the advantages of AVS for source localizing problems, compared with standard pressure sensor arrays. For this aim, we built a linear array of four AVS and design a graphical user interface for processing the measurements and estimating the source location in 3D. This research considered the source identifiability using a single AVS, as well as 3D location estimation using a linear array of AVS. Array of AVS Capon Spectra AVS Far acoustic field Signal conditioner Overview Euler equation DAQ • Goal • Estimate the position of an acoustic source using spatial and temporal measurements of pressure and particle acoustic field • Approach • Physical modeling of the propagation of an acoustic waveform through the air. • Statistical analysis of pressure and particle velocity measurements taken by an array of acoustic vector sensors. • Applications • Assisted navigation, defense, teleconference, vibration analysis • Background pressure at position r and time t Figure: Power distribution as a function of azimuth and elevation The red arrow indicates the maximum spectral value. particle velocity at position r and time t direction of particle velocity Numerical Example: Source identifiability speed of sound, Ambient pressure SNR1 = SNR2 = SNR A) Single AVS Single AVS and source model pressure-sensor-measurement noise particle-velocity-measurement noise SNR = -3 dB SNR = 12 dB SNR = 6 dB Multiple AVS and single source model B) Two AVS Steering vector Estimation algorithm References • A. Nehorai and E. Paldi, ``Acoustic vector sensor array processing," Proc. 26th Asilomar Conf. Signals, Syst. Comput., pp. 192-198, Pacific Grove, CA, Oct. 1992. • A. Nehorai and E. Paldi, "Acoustic vector-sensor array processing," IEEE Trans. on Signal Processing, Vol. SP-42, pp. 2481-2491, Sept. 1994. • M. Hawkes and A. Nehorai, "Acoustic vector-sensor beamforming and capon direction estimation," IEEE Trans. on Signal Processing, Vol. SP-46, pp. 2291-2304, Sept. 1998. Figure:Photograph of a three dimensional sound intensity probe consisting of one pressure sensor and three particle velocity sensors mounted together (Source: Microflown Technologies, B.V.) Capon Spectra sample-correlation matrix for N samples