Acoustic Source Localization and Beamforming: Theory and Practice
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Acoustic Source Localization and Beamforming: Theory and Practice Joe C. Chen Electrical Engineering Department, University of California, Los Angeles (UCLA), Los Angeles, CA 90095-1594, USA Email: [email protected]
Kung Yao Electrical Engineering Department, University of California, Los Angeles (UCLA), Los Angeles, CA 90095-1594, USA Email: [email protected]
Ralph E. Hudson Electrical Engineering Department, University of California, Los Angeles (UCLA), Los Angeles, CA 90095-1594, USA Email: [email protected] Received 17 February 2002 and in revised form 21 September 2002 We consider the theoretical and practical aspects of locating acoustic sources using an array of microphones. A maximumlikelihood (ML) direct localization is obtained when the sound source is near the array, while in the far-field case, we demonstrate the localization via the cross bearing from several widely separated arrays. In the case of multiple sources, an alternating projection procedure is applied to determine the ML estimate of the DOAs from the observed data. The ML estimator is shown to be effective in locating sound sources of various types, for example, vehicle, music, and even white noise. From the theoretical Cram´er-Rao bound analysis, we find that better source location estimates can be obtained for high-frequency signals than lowfrequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. Much experimentally measured acoustic data was used to verify the proposed algorithms. Keywords and phrases: source localization, ML estimation, Cram´er-Rao bound, beamforming.
1.
INTRODUCTION
Acoustic source localization has been an active research area for many years. Applications include unattended ground sensor (UGS) network for military surveillance, reconnaissance, or around the perimeter of a plant for intrusion detection [1]. Many variations of algorithms using a microphone array for source localization in the near field as well as direction-ofarrival (DOA) estimation in the far field have been proposed [2]. Many of these techniques involve a relative time-delayestimation step that is followed by a least squares (LS) fit to the source DOA, or in the near-field case, an LS fit to the source location [3, 4, 5, 6, 7]. In our previous paper [8], we derived the “optimal” parametric maximum likelihood (ML) solution to locate acoustic sources in the near field and provided computer simulations to show its superiority in performance over other methods. This paper is an extension of [8], where both the far- and the near-field cases are considered, and the theoretical analysis is provided by the Cram´er-Rao bound
(CRB), which is useful for both performance comparison and basic understanding purposes. In addition, several experiments have been conducted to verify the usefulness of the proposed algorithm. These experiments include both indoor and outdoor scenarios with half a dozen microphones to locate one or two acoustic sources
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