Monte Carlo-based filter for target tracking with feature measurement

Monte Carlo-based algorithm for tracking maneuvering target with a feature measurement is proposed in the paper Amplitude Information (AI) is used as a feature for state estimation of relatively low observable target (low Signal-to-Noise Ratio (SNR)) in the presence of high rate of false alarms. Rayleigh distributed noise amplitude and Swerling 3 type target model are assumed. The stochastic filter combines the Multiple Model (MM) approach with switching models for dealing with maneuvers and probabilistic association of features and measured kinematic data. The filter performance is analyzed by simulation. Results show that the suggested algorithm can track targets with SNR down to 10 dB with acceptable percentage of lost tracks, while the filter without AI works down to 13 dB. In the case of nonmaneuvering target these limits are at lower levels.