Much faster but still good algorithm for tracking multiple maneuvering targets

The traditional IMMJPDA (interactive multiple models joint probabilistic data association) algorithm gives good tracking performance but the computational burden increases explosively with increasing number of maneuvering targets. We now present a novel algorithm NDA-DAF (neural data association-data fusion adaptive filtering), which makes good use of neural network and, we believe, succeeds in preventing explosive growth of computational burden while retaining almost the same tracking performance as IMMJPDA. We explain in detail our NDA-DAF algorithm. We give a numerical example to illustrate the application of our novel NDA-DAF algorithm. The paper gives the U-type trajectories of two maneuvering targets. Simulation results for comparing IMMJPDA with NDA-DAF are given. These results show preliminarily that the novel algorithm NDA-DAF, which makes good use of neural network, has almost the same performance as the traditional IMMJPDA but with greatly reduced computational burden for tracking multiple maneuvering targets.