A Multiple Model SNR/RCS Likelihood Ratio Score for Radar-Based Feature-Aided Tracking

摘要: Most approaches to data association in target tracking use a likelihood-ratio based score for measurement-to-track and track-to-track matching. The classical approach uses likelihood ratio on kinematic data. Feature-aided non-kinematic produce an "auxiliary score" that augments the score. This paper develops nonkinematic statistical models signal-to-noise (SNR) radar cross section (RCS) narrowband tracking. formulation requires estimate of mean RCS, key challenge is RCS through significant "jumps" due aspect dependencies. A novel multiple model used track jumps. Three solution are developed: one α-filter, second median filter, third IMM filter with pre-filter. Simulation results presented show effectiveness transitions aspect-angle changes.