We present a novel data association algorithm based on an integrated random coefficient matrices Kalman filtering (DAIRKF) for the multiple targets and sensors tracking association problem. The basic idea of this algorithm is to integrate all targets and measurements which need to be associated to a new whole system. Then the random coefficient matrices Kalman filtering is applied to this integrated dynamic system to derive the estimates of these target states. Since this algorithm violates some independence conditions for the optimality of the random coefficient matrices Kalman filtering, it is suboptimal in the mean square error (MSE) sense. Nevertheless, in some degree, there is still a correct theoretical basis in DAIRKF and the idea of this algorithm is significantly different from that of joint probabilistic data association (JPDA). Moreover, we can extend the single-sensor DAIRKF algorithm to a multisensor DAIRKF (MSDAIRKF) algorithm with high survivability in poor environment. The computation burden of MSDAIRKF grows linearly as the number of sensors increases. Numerical examples show that the new algorithm works significantly better than JPDA in many cases.
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