Improved sequential Monte Carlo filtering for ballistic target tracking

We present in this correspondence an improved sequential Monte Carlo (SMC) filter for ballistic target tracking with random, time-varying ballistic coefficient. The proposed tracker is a sampling/importance resampling (SIR) filter that uses an optimized importance function to combat particle degeneracy, and also incorporates an additional measurement-driven Markov chain Monte Carlo (MCMC) move step to prevent particle impoverishment. Simulation results show that, using significantly fewer particles than previously reported in the literature for similar tracking problems, the root mean-square error (RMSE) curves for the proposed optimized SIR filter approach the square root of the ideal posterior Cramer-Rao lower bound (PCRLB).