Distributed Acoustic Source Tracking in Noisy and Reverberant Environments With Distributed Microphone Networks

Abstract

In this paper, an improved distributed unscented Kalman particle filter (DUKPF) is proposed for the problem of tracking a single moving acoustic source in noisy and reverberant environments with distributed microphone networks. The conventional DUKPF employs the unscented Kalman filter (UKF) for its proposal of particle sampling, whereas the UKF incorporates one single observation from a certain localization function, which is vulnerable to noise or reverberation. To alleviate this problem, multiple observations are extracted from the localization function at each node and incorporated into the state update of the UKF via the probability data association (PDA) technique, yielding the PDA-UKF. Next, employing the PDA-UKF for the proposal of particle sampling, the improved DUKPF is further developed. Finally, the improved DUKPF is adapted for the acoustic source tracking problem, and a distributed acoustic source tracking method is presented. Simulation results reveal that the improved DUKPF achieved better tracking performance than the conventional DUKPF in different noisy and reverberant conditions. INDEX TERMS Acoustic source tracking, distributed microphone networks, distributed unscented Kalman particle filter, probability data association.

Acoustic Source Tracking Based on Probabilistic Data Association and Distributed Cubature Kalman Filtering in Acoustic Sensor Networks

“…In acoustic sensor networks, the discrete-time signal acquired by the microphone ( ) of node can be modeled as [ 23 ] where is the discrete-time index, is the room impulse response (RIR) between the microphone and the acoustic source, denotes the convolution operator, is the source signal, and is the additive noise.…”

“…In order to solve this problem, the local largest of the first largest peaks of are taken as the candidate measurement value of multiple TDOA of node at time . In this paper, multiple TDOA observations were extracted through a two-step selection process, taking node as an example [ 23 ].…”

“…At time , the state of the speaker is defined as , where and represent the position and moving speed of the speaker, respectively. In this model, the speaker’s motion in the Cartesian coordinate system is considered to be independent and modeled as [ 23 ] where , and ; and are the rate constant and the steady velocity parameter, respectively. denotes the s-order identical matrix, stands for the Kronecker product, is the sampling period for position estimation, and is the zero-mean white Gaussian noise with identity covariance matrix, which describes the uncertainty of the acoustic source motion.…”

“…No central processor is needed in the distributed method, and all nodes realize the estimation of the global state only by exchanging data with their neighbors. In reference [ 18 ], a distributed extended Kalman particle filter (DEKPF) for speaker tracking was developed, which combined the current TDOA observations into EKF to propose particle filter. In reference [ 19 ], a distributed particle filter (DPF) was proposed, which applied the improved iterative covariance intersection (MICI) algorithm and interactive multiple model (IMM) to speaker tracking in distributed microphone networks.…”

“…Probabilistic Data Association (PDA) [ 22 ] is an effective method to combine multiple observations into Kalman filter state update, which has been proved to be suitable for target tracking in clutter environment. In reference [ 23 ], an improved distributed unscented Kalman particle filter (DUKPF) was proposed to track a single moving acoustic source using a distributed microphone network in noise and reverberation environments. This method proposed to extract multiple observations from the observation function of each node and combined them into the status update of UKF through probabilistic data association (PDA) technology, so as to generate PDA-UKF, and then brought in particle filter.…”