A Multiple Model SNR/RCS Likelihood Ratio Score for Radar-Based Feature-Aided Tracking
Abstract
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 a likelihood ratio based on kinematic data. Feature-aided tracking uses non-kinematic data to produce an auxiliary score that augments the kinematic score. This paper develops a non-kinematic likelihood ratio score based on statistical models for the signal-to-noise (SNR) and radar cross section (RCS) for use in narrowband radar tracking. The formulation requires an estimate of the target mean RCS, and a key challenge is the tracking of the mean RCS through significant jumps due to aspect dependencies. A novel multiple model approach is used to track through the RCS jumps. Three solutions are developed: one based on an alpha-filter, a second based on the median filter, and the third based on an IMM filter with a median pre-filter. Simulation results are presented that show the effectiveness of the multiple model approach for tracking through RCS transitions due to aspect-angle changes.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2005
- Accession Number
- ADA444135
Entities
People
- Benjamin J. Slocumb
- Michael E. Klusman Iii