Improved probabilistic data association and its application for target tracking in clutter
Long-qiang Ni, Gao Shesheng, Li Xue · 2011
In this paper a new association probability was proposed to enhance the accuracy and stability of the probabilistic data association filter results in dense clutter environment. Firstly, the most popular data association algorithms (nearest-neighbor standard filter and probabilistic data association) were introduced, and then the advantages and disadvantages about these tow algorithms were analyzed. Secondly a new association probability was calculated based on the discussion. Finally, a data simulation was given to improve the efficiency about this new method, simulation results show that this new approach is more efficient than the traditional data association algorithms.