Distributed spatio-temporal association and tracking of multiple targets using multiple sensors

Abstract

The problem of tracking multiple targets using nonlinear observations acquired at multiple sensors is addressed by combining particle filtering (PF) with sparse matrix decomposition techniques. Sensors are spatially scattered, while the unknown number of targets may be time varying. A framework is put forth where norm-one regularized factorization is employed to decompose the sensor data covariance matrix into sparse factors whose support facilitates recovery of sensors that acquire informative measurements about the targets. This novel sensors-to-targets association scheme is integrated with PF mechanisms to perform accurate tracking. Precisely, distributed optimization techniques are employed to associate targets with sensors, and PF is integrated to perform target tracking using only the sensors selected by the sparse decomposition scheme. Different from existing alternatives, the novel algorithm can efficiently track and associate targets with sensors even in noisy settings. Extensive numerical tests are provided to demonstrate the tracking superiority of the proposed algorithm over existing approaches.