Performance of the shifted Rayleigh filter in single-sensor bearings-only tracking

The problem of single-sensor bearings-only tracking continues to present challenges to tracking algorithms, particularly in certain difficult scenarios such as ones with high bearing rates. In such scenarios, the performance of the recently introduced shifted Rayleigh filter (SRF) is compared with that of other techniques such as extended Kalman filter (EKF), unscented Kalman filter (UKF) and particle filter (PF). The results are also compared with the theoretical Cramer-Rao Lower Bound (CRLB). The SRF is a moment matching algorithm, and its key feature is that it generates the exact conditional distribution of target motion, given normal approximation to the prior. Simulations show that the SRF is superior to other moment matching algorithms such as EKF and UKF and is able to achieve comparable performance to PF while being orders of magnitude faster.