Centralized multisensor general association algorithm based on data compress technique

Aim to solve the problem that the memory of the centralized multi-sensor system is easy to be saturated, this paper has proposed a new algorithm named centralized multi-sensor general association algorithm based on data compress technique against a background, which has a relative demand of real-time and tracking precision. In this algorithm, the measurement sets associated with the same target from each sensor are obtained and compressed into equivalents. A lot of association hypotheses are built with the equivalents, the remainder measurements and the track of each target. The score of every hypothesis could be obtained with the formula of the score function in multi-sensor general association algorithm, and the state estimation of the fusion center is gained finally. The simulation results show that this algorithm could track the targets effectively with the high density clutter and the general performance of this algorithm is better than that of sequential centralized multi-sensor joint probabilistic data association algorithm and parallel centralized multi-sensor general association algorithm.