A density assisted particle filter algorithm for target tracking with unknown ballistic coefficient

We present a density-assisted particle filter (DAPF) algorithm for ballistic target tracking with unknown, fixed ballistic coefficient. The proposed algorithm uses an optimized importance function to update the particle population and then utilizes the updated particles and their respective importance weights to build a parametric approximation of the joint posterior probability density function (PDF) of the target state and the unknown ballistic coefficient. A new set of particles is then resampled according to this approximate pdf and propagated to the next iteration of the algorithm. Simulation results confirm previous claims in the literature that DAPFs are viable alternatives for sequential estimation in nonlinear dynamic models with unknown, static parameters.