Sparsity based multi-target tracking using mobile sensors

Abstract

Sensor mobility and sparse sensor data correlations are exploited in this work for the problem of tracking multiple targets. Sparse matrix decomposition is integrated with the design of proper kinematic rules to identify informative sensors, associate them with the targets and enable them to follow closely the moving targets. The modified barrier method is employed to minimize proper error covariance matrices obtained by extended Kalman filtering recursions. Distributed updating recursive rules are obtained enabling only the informative sensors to update their location, and follow closely the corresponding targets while staying connected. This is to be contrasted with existing alternatives where all sensors need to move constantly. Numerical tests corroborate that the proposed scheme outperforms existing alternatives while accurately tracks multiple targets by imposing mobility only on target-informative sensors.