Tracking of multiple sources in an acoustic sensor network using an extended Gaussian mixture PHD filter
Brendel A, Kellermann W (2018)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2018
Pages Range: 400-404
Event location: Sheffield
ISBN: 978-1-5386-4752-3/
Abstract
We propose an approach for tracking a varying number of simultaneously active acoustic wideband signal sources in an acoustic enclosure. Relying on the assumption of W-disjoint orthogonality, the method uses narrowband position estimates of the sources for the targets. The instantaneous position estimates form clusters rather than single points, as would be required for a conventional Probability Hypothesis Density (PHD) filter. Therefore, we model the position estimates as extended targets and use a special form of the PHD filter, the extended target Gaussian mixture PHD filter, for tracking the targets. This allows to model target birth and death, which correspond to speech onset and end of utterance, respectively. With this model and by using the well-developed theory of Finite Set Statistics (FISST)-based multi-target tracking, we provide a comprehensive, strictly Bayesian treatment of the problem of tracking wideband acoustic sources using narrowband position estimates. We validate the results by tracking a varying number of targets in an enclosure simulated with the image-source method.
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How to cite
APA:
Brendel, A., & Kellermann, W. (2018). Tracking of multiple sources in an acoustic sensor network using an extended Gaussian mixture PHD filter. In Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) (pp. 400-404). Sheffield, GB.
MLA:
Brendel, Andreas, and Walter Kellermann. "Tracking of multiple sources in an acoustic sensor network using an extended Gaussian mixture PHD filter." Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Sheffield 2018. 400-404.
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