Fusion of reduced-rank TMA estimates

Abstract

The problem of bearings-only target motion analysis (TMA) in incompletely observable situations is considered. Both target and observer trajectories are rectilinear uniform motions (RUMs). A specific method is first established for the estimation of such reduced-rank TMA (RR-TMA). Then, the statistical fusion of two (or more) RR-TMA estimates into a complete observable TMA is established. Remarkably, this second stage is analytical. Finally the fusion algorithm is shown to be efficient for a reasonable domain of measurement errors. Applications are listed among radar and sonar tracking problems. They are specially encountered in large-scale multiplatform situations where combinatorial testing of multiple hypotheses is a big issue, or in long-term integration of moving targets where the dimension reduction of the search-grid is critical.