Multi-shooter localization using finite point process

A finite point process approach to multi-shooter localization from gunfire events 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 solving the data association problem and can account for clutter noise as well as missed detections. The optimal subpattern assignment metric is used to assess the performance and accuracy of the proposed localization algorithm. Implementation of the proposed algorithm on experimental data yields desirable results.