Abstract:

When updating particles, a particle probability hypothesis density filter (SMC-PHDF) requires the probabilistic distribution of measurement noise to calculate the likelihood function, which makes it rely excessively on the probabilistic model of measurement noise. To overcome this drawback, a new multiple target tracking algorithm under unknown probabilistic distribution of measurement noise is proposed, namely, a risk evaluation-based probability hypothesis density filter (RE-PHDF). When SMC-PHDF updates probability hypothesis density(PHD) particles, the algorithm computes the risk of each particle using a risk function, and evaluates each particle by a risk evaluation function, then updates the particle weights by means of the evaluated results. Avoiding thus the likelihood function calculation in multi-dimensional measurement space, the algorithm does not depend on the probabilistic distribution of measurement noise and can save much computing time. The simulation results show that RE-PHDF possesses higher robustness and stability under unknown and complicated measurement noise environment in comparison with SMC-PHDF. In addition, the new algorithm can save up to 50% execution time while possessing similar accuracy as SMC-PHDF.

Key words: target tracking, random sets, probability hypothesis density, measurement signals, noise model