Message Passing for Joint Registration and Tracking in Multistatic Radar

Abstract

Sensor registration is fundamental in sensor fusion. Inaccuracies in sensor location and rotation can manifest themselves into the measurements used in Multiple Target Tracking (MTT), and dramatically degrade its performance. These registration parameters are often estimated separately to any multitarget estimation, which could lead to increased computational expense, and also to systematic errors. Recent works have shown that MTT algorithms derived from Belief Propagation (BP) are computationally efficient and highly scalable for large tracking scenarios. This work presents a hierarchical Bayesian model inspired by single-cluster methods from the Random Finite Set (RFS) literature, that allow for the registration parameters to be estimated jointly with the multiple target tracking. Simulations are carried out on a multistatic radar network containing two radars with a relative range and azimuth bias between them. Results are presented for a particle-BP MTT algorithm, and it's performance is compared to that of a Sequential Monte Carlo (SMC)-Probability Hypothesis Density (PHD) filter. The results show that the BP algorithm outperforms the PHD implementation in terms of accuracy by around 10%.