Novel sequential Monte Carlo method to target tracking

EKF and UKF are often used in target tracking, but the required PDF is approximated by a Gaussian, which may be a gross distortion of the true underlying structure and may lead to filter divergence, especially in the situations where the uncertainty of the measurements is large compared to the uncertainty of process model of tracking. Resample introduces the problem of loss of diversity among the particles with particle filter because the uncertainty of process model is small compared to the uncertainty of the measurements. The SMCEKF and SMCUKF algorithms given in this paper ensure the independency of particles by introducing parallel independent EKF and UKF. The required density of the state vector is represented as a set of random samples and its weights, which is updated and propagated recursively on their own estimate. The performance of the filters is greatly superior to the standard EKF and UKF. Analysis and simulation results of the bearing only tracking problem have proved validity of the algorithms.