Particle Filter for Ballistic Target Tracking with Glint Noise

The performance of ballistic target interception is critically dependent on the performance of the target state estimation. The estimation performance then strongly depends on the accuracy of the measurement model. The Gaussian uncertainty distribution has commonly been used for representing the statistical properties of sensor noise, due to its mathematical simplicity and effectiveness. However, seeker sensor measurements are often corrupted by glint noise which is highly non-Gaussian, and conventional Gaussian filtering algorithms are known to show unsatisfactory performance in the presence of glint noise. This research proposes the use of a particle filter for ballistic target tracking in a glint noise environment. The target tracking performance of the particle filter is compared with that of the extended Kalman filter.