Improved N - Best Multiple Hypothesis Tracking Algorithm

Abstract

In the tracking process of the multi-hypothesis tracking algorithm, many hypothesis are made about the correlation between the track and the measured values. Although its tracking performance is better than other tracking algorithms, it needs a lot of computation time and memory resources when tracking multiple targets or in environments with large clutter due to exponential growth of hypothesis. In view of this situation, an improved N-Best algorithm is proposed to be applied to MHT. The simulation results show that this method can reduce the computation time and realize the track correlation, so it has certain practicability.