A Track-oriented Approach to Target Tracking with Random Finite Set Observations
Tiancheng Li, Xiaoxu Wang, Yan Liang, Junkun Yan, Hongqi Fan · 2019
We have earlier proposed a data-driven approach to target tracking, which models the target movement by using a trajectory function of time (T-FoT) rather than a Markov model. In this work, we extend the approach to account for random finite set observations consisting of both missing and false data. More challenging, the missing and false data are generated under unknown ratios, i.e., they can not be accurately modeled. To tackle this problem, we here propose a data-driven method for identifying the real measurement of the target from clutter if the target is detected and for declaring a misdetection otherwise. Simulation is conducted to demonstrate the effectiveness of our approach, in comparison with the Bayesian-optimal approach.