Novel sequential monte carlo method to bearing only tracking

Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are often used in target tracking, but the required Posterior Density Function (PDF) is still approximated by a Gaussian, which may be a gross distortion of the true underlying structure and lead to filter, divergence when performing EKF or UKF. Because the uncertainty of process model in bearing only tracking is small compared with the uncertainty of the measurements, resample introduces the problem of loss of diversity among the particles with Particle Filter. This may lead to undesired clustering of the samples and eventually inaccurate results. The SMCEKF and SMCUKF algorithms given in this paper ensure the independency of particles by introducing parallel independent EKFs and UKFs for the bearing only tracking problem. The resample technique, which was suggested in the particle filter as a method to reduce the degeneracy problem, is given up. The required density of the state vector is represented as a set of random samples, 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.