Sensor Selection under Unknown but Bounded Disturbances in Multi-Target Tracking Problem

The problem of sensor selection arises in various applications. In multi-target tracking, the main challenge is to select sensors for each target in such a way as to minimize the estimation error, taking into account the limitations of the computation and communication resources of the sensors. In this paper, we deal with two problems arising in sensor selection. First, we try to reduce the situations, in which selected sensors might be loaded more than the rest of the nodes. Secondly, we discard the assumption, requiring the measurement noise to have the Gaussian distribution. Instead of that, we consider the measurements corrupted by the unknown but bounded noise. We present a sensor selection strategy based on linear matrix inequalities and show its performance.