Tracking multiple speakers using CPHD filter
Proceedings of the 15th international conference on Multimedia
Localization of multiple emitters based on the sequential PHD filter
Signal Processing
Sequential estimation of multipath MIMO-OFDM channels
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Mobile multi-target tracking in two-tier hierarchical wireless sensor networks
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
Ant clustering PHD filter for multiple-target tracking
Applied Soft Computing
Acoustic source localization and tracking using track before detect
IEEE Transactions on Audio, Speech, and Language Processing
Dim target tracking base on GM-PHD filter
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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Speaker location estimation techniques based on time-difference-of-arrival measurements have attracted much attention recently. Many existing localization ideas assume that only one speaker is active at a time. In this paper, we focus on a more realistic assumption that the number of active speakers is unknown and time-varying. Such an assumption results in a more complex localization problem, and we employ the random finite set (RFS) theory to deal with that problem. The RFS concepts provide us with an effective, solid foundation where the multispeaker locations and the number of speakers are integrated to form a single set-valued variable. By applying a sequential Monte Carlo implementation, we develop a Bayesian RFS filter that simultaneously tracks the time-varying speaker locations and number of speakers. The tracking capability of the proposed filter is demonstrated in simulated reverberant environments