Distributed spatio-temporal multi-target association and tracking

Particle filtering is combined with sparse matrix decomposition techniques to address the problem of tracking multiple targets using nonlinear sensor observations measuring signal strength. The unknown number of targets may be time-varying, while sensors are spatially scattered. Norm-one regularized matrix factorization is employed to decompose the sensing data covariance matrix into sparse factors whose support facilitates the task of associating the targets with sensor measurements. The novel sensors-to-targets association scheme is developed using distributed optimization which is further integrated with particle filtering mechanisms to perform accurate tracking. Numerical tests demonstrate the tracking superiority of the proposed algorithm over alternative approaches.