Estimating target state distributions in a distributed sensor network using a Monte-Carlo approach
Distributed processing algorithms are attractive alternatives to centralized algorithms for target tracking applications in sensor networks. In this paper, we address the issue of determining a initial probability distribution of multiple target states in a distributed manner to initialize distributed trackers. Our approach is based on Monte-Carlo methods, where the state distributions are represented as a discrete set of weighted particles. The target state vector is the target positions and velocities in the 2D plane. Our approach can determine the state vector distribution even if the individual sensors are not capable of observing it. The only condition is that the network as a whole can observe the state vector. A robust weighting strategy is formulated to account for mis-detections and clutter. To demonstate the effectiveness of the algorithm, we use direction-of-arrival nodes and range-doppler nodes.