A neural solution for multitarget tracking based on a maximum likelihood approach
Michel Winter, Gérard Favier · 2002
This paper presents a new neural solution for multitarget tracking based on a maximum likelihood approach. In the radar tracking context, neural networks are generally used to decide which plot can be assigned to each predetected track, in taking into account only the plots received during the last scan. A neural approach is proposed to determine which particular combinations of the plots received during the k latest scans are likely to represent true target tracks. This data association problem is viewed as a multiple hypothesis test that can be solved in maximizing a likelihood function by means of an Hopfield (1985) neural network. Some simulation results are presented to illustrate the behaviour of the proposed neural tracking solution.