Kalman Filters for Time Delay of Arrival-Based Source Localization

Abstract

In this work, we propose an algorithm for acoustic source localization based on time delay of arrival (TDOA) estimation. In earlier work by other authors, an initial closed-form approximation was first used to estimate the true position of the speaker followed by a Kalman filtering stage to smooth the time series of estimates. In the proposed algorithm, this closed-form approximation is eliminated by employing a Kalman filter to directly update the speaker's position estimate based on the observed TDOAs. In particular, the TDOAs comprise the observation associated with an extended Kalman filter whose state corresponds to the speaker's position. We tested our algorithm on a data set consisting of seminars held by actual speakers. Our experiments revealed that the proposed algorithm provides source localization accuracy superior to the standard spherical and linear intersection techniques. Moreover, the proposed algorithm, although relying on an iterative optimization scheme, proved efficient enough for real-time operation.

Sub-sampling-based 2D localization of an impulsive acoustic source in reverberant environments

“…With the multiple TDEs obtained from multiple pair of microphones, we apply conventional TDOA-or DOA-based localization method [6][7][8][9][10] to locate the impulsive acoustic source.…”

“…However, for large N, there will be 2 N such sets and it would become hard to evaluate this sum. The computational complexity of the estimation can be reduced by finding ways to approximate (7). In the following, the ways to calculate various terms in (7) are discussed.…”

“…Another TDE-based method called the time difference of arrival (TDOA), assumes the presence of the sound source in the near field of the microphones [7][8][9][10]. The TDE from a pair of microphones is converted into range difference from which the range difference equation is formed.…”

Time-difference of arrival model for spherical microphone arrays and application to direction of arrival estimation

“…The proposed model is based on the distance and corresponding travel time the wavefront is required to curve in order to reach the microphones on the opposite side of the sphere. The applications of the proposed method include all TDOA-based spatial audio signal processing, e.g., DOA estimation [7], source tracking [8] and source separation [9], [10]. The benefit of proposed TDOA model in comparison to using analytic IRs for steering the array is the low computational complexity in time-domain processing where the steering could be done by a single delay element.…”

Tracking sound sources by means of HMM

“…The localization errors can be reduced using arrays with a large aperture [13]. This approach, however, needs a large number of microphones, thus requiring greater computational resources and a larger space, not always available in a real scenario, to install the array.Another approach involves the use of algorithms based on the Kalman filter [8] or particle filter [11] for the tracking of moving sound events. In general, these algorithms exploit a priori information given by the previous positions of the event in motion.…”