PROBABILISTIC SENSOR MANAGEMENT FOR TARGET TRACKING VIA COMPRESSIVE SENSING

In this paper, we consider the problem of sensor management for target tracking in a wireless sensor network (WSN). To determine the set of sensors that have the most information, we develop a probabilistic sensor management scheme based on the concepts developed in compressive sensing. In the proposed scheme, each senor node decides whether it should transmit its observation via multiple access channels to the fusion center with a certain probability. With this probabilistic transmission scheme, the observation vector received at the fusion center becomes a compressed version of the original observations. Our goal is to determine the optimal values of the probability using which each node should transmit so that the determinant of the Fisher information matrix (FIM) is maximized at any given time instant with a constraint on the available energy. Numerical examples are provided to show the performance of the proposed scheme.