This paper addresses the intractable track matching problem involved in multi-sensor multi-target tracking using the labeled multi-Bernoulli filters. Unlike the unlabeled density defined in the common state space, the labeled multi-target density is defined in the joint state and label space, where the label contains time-series/history information of the underlying track. To measure the similarity between labeled densities (individual tracks) that is required for inter-sensor track matching and fusion, one has to account for the divergences in both state and label spaces. The challenge, however, arises from the lack of a proper metric to measure the label difference. It requires considering the entire trajectory of the track, encompassing the whole-life information from the birth of the track to the present. In this paper, we provide a solution of comparing and matching labels based on the whole-life time-series state distributions of the labels/tracks, by extending the common divergences like the Cauchy-Schwarz and Kullback-Leibler from distributions at a single time-instant to those over time-series. Representative scenarios are considered for illustration.

In this paper, a joint beam selection and power allocation (JBSPA) scheme for multi-target tracking is proposed in a collocated MIMO (C-MIMO) radar network. The goal of this scheme is to achieve better resource utilization efficiency with a given resource budget. Under the condition of sufficient resources, the scheme minimizes the total resource consumption of the C-MIMO radar network. When the sensor resources are insufficient, the scheme maximizes the number of tracked targets that meet the tracking requirements. To evaluate the performance of multi-target tracking, we normalize and utilize the Bayesian Cramér-Rao lower bound (BCRLB) as the performance evaluation criterion. The JBSPA scheme is formulated as a non-convex optimization problem involving integer and continuous variables that are coupled. To address this problem, we propose a fast and effective three-step solution technique. Simulation results demonstrate that the proposed JBSPA scheme can save resources, significantly increase the target capacity, and improve the resource utilization efficiency of the C-MIMO radar network.

In this paper, we address the multiple sound source localization problem using time differences of arrival (TDOAs) of sound sources to a microphone array. Typically, TDOAs are estimated based on the peak extraction of the generalized crosscorrelation function. In multi-source cases, for any given microphone pair, it is hard to tell the correspondence between the sound sources and the extracted peaks. In this work, we develop a novel localization approach based on data association which combines multiple TDOAs from the same source across different microphone pairs. Firstly, the generalized cross correlation-phase transform (GCC-PHAT) function is evaluated and multiple peaks of the GCC function indicating candidate TDOAs are extracted for each pair of microphones. Next, we employ the multi-dimensional assignment algorithm to associate multiple TDOAs from the same source. Finally, multiple sound source localization is carried out based on the obtained TDOA associations across different microphone pairs. Experimental results show the proposed method achieves superior performance for multiple sound source localization compared to the competing algorithm, especially in noisy environments.

In this paper, we address the problem of sound source localization in reverberant environments. Time-delay estimation (TDE) methods are widely employed to locate sound sources based on the time differences of arrival (TDOAs) of signals received at different microphone pairs. In strong reverberations, the highest peak of the localization function is not necessarily from the true source resulting from the multi-path effect. Our previously proposed method based on the optimal peak association (OPA) aims to extract multiple peaks from the localization function for each microphone pair and find out the optimal association of TDOAs corresponding to the same sound source. However, due to the limitation of geometric configuration of microphones and possible missed detections, some microphone pairs fail to provide high-quality TDOA measurements. An improved OPA method is developed in this work based on the robust least squares which can determine the weights adaptively in terms of their respective observation accuracy. Experimental results demonstrate the superiority of the proposed method compared with the original OPA method in reverberant environments.