A finite point process approach to multi-target localization using transient measurements.

Finite point process modeling of multi-sensor, multi-target localization problem.Proposed point process model accounts for missed detections as well as false alarms.Presents a multi-target localization algorithm based on the point process model.Algorithm assumes target number and measurement association are unknown.Detailed evaluation of the algorithm via numerical simulation and experimental data. A finite point process approach to multi-target localization from a transient signal is presented. After modeling the measurements as a Poisson point process, we propose a twofold scheme that includes an expectation maximization algorithm to estimate the target locations for a given number of targets and an information theoretic algorithm to select the number of targets. The proposed localization scheme does not require explicitly solving the data association problem and can account for clutter noise as well as missed detections. Although point process theory has been widely utilized for sequential tracking of multiple moving targets, the application of point process theory for multi-target localization from transient measurements has received very little attention. The optimal subpattern assignment metric is used to assess the performance and accuracy of the proposed localization algorithm. Implementation of the proposed algorithm on synthetic data yields desirable results. The proposed algorithm is then applied to the multi-shooter localization problem using acoustic gunfire detection systems.