Multi-sensor Target Tracking Fusion Algorithm

In recently years, Multi-sensor information fusion has devcloped fast and was given a wide attention both in military and civil fields. Position fusion level is one of the most important and widely used levels in Multi-sensor information fusion system. There are mainly two roles in this level: one is associate, the other is estimate.Under the assumption of the completion of the target detection, data calibration and association, the problem of the state estimation in the position fusion level is studied and the previous algorithms are improved in this paper. Overview the history and what is going on about the information fusion. In the distributed two-sensor tracking system, presents a non-equal dimension maximum a posterior estimate fusion algorithm between the output target estimate state with feedback by using of mapping between the state spaces. Discusses the out-of-sequence measurement state estimate, and develops the optimal state update estimate algorithms of the 1-step-lag case and arbitrary-step-lag when the processor.noise and the measurement noise correlate. In the distributed multi-sensor tracking system, presents the local optimal measurement noise update estimate and the measurement noise update estimate fusion algorithm by weighted, when measurement reaches out-of-sequence, the processor noise and the measurement noise are correlate. Furthermore, this paper gives the numerical simulation and analysis for all algorithms. The simulation indicates that all of the algorithms are the feasibility and validity. In the end, the main work in the paper is summarized and further work is proposed at the aspect of the state estimate in information fusion.