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The tracking performance of the Particle Filter is compared with that of the Range-Parameterised EKF (RPEKF) and Modified Polar coordinate EKF (MPEKF) for a single-sensor angle-only tracking problem with ownship maneuver. The Particle Filter is based on representing the required density of the state vector as a set of random samples with associated weights. This filter is implemented for recursive estimation, and works by propagating the set of samples, and then updating the associated weights according to the new received measurement. The RPEKF, which is essentially a weighted sum of multiple EKF outputs, and the MPEKF are known for their robust angle-only tracking performance. This comparative study shows that the Particle Filter performance is the best, although the RPEKF is only marginally worse. The superior performance of the Particle Filter is particularly evident for high noise conditions where the EKF type trackers generally diverge. Also, the Particle Filter and the RPEKF are found to be robust to the level of a priori knowledge of initial target range. On the contrary, the MPEKF exhibits degraded performance for poor initialisation.
Arulampalam et al. (Thu,) studied this question.