A Sequential Monte Carlo Method for Target Tracking in an Asynchronous Wireless Sensor Network

Target tracking in a wireless sensor network (WSN) has become a relatively standard problem. The WSN typically consists of a collection of sensor nodes, which acquire physical data related to the target dynamics, and a fusion center (FC) where the available data are processed together to sequentially estimate the target state (its instantaneous location and velocity). Very often, tracking algorithms are designed under the assumption that the network is synchronous, i.e., that the local clocks of the sensor nodes and the FC are perfectly aligned or, at least, that their offsets are known. In this paper, we consider an asynchronous WSN, in which the local clocks of the sensors are misaligned and the corresponding offsets are unknown, and aim at designing recursive algorithms for optimal (Bayesian) tracking. In particular, we propose sequential Monte Carlo (SMC) techniques that enable the approximation of the joint posterior probability distribution of the target state and the set of local clock offsets by means of a discrete probability measure with a random support. From this approximation, estimates of the target position and velocity, as well as of the clock offsets, can be readily derived. We illustrate the validity of the proposed approach and assess the performance of the resulting algorithms by means of computer simulations.