Recursive LMMSE Sequential Fusion with Multi-Radar Measurements for Target Tracking

Abstract

By simply stacking all converted measurements, recursive LMMSE (linear minimum mean square error) filtering for a single radar has been extended to the case of centralized fusion with multiple radars. To further improve the performance of the LMMSE centralized fusion, [1] ranks all scalar measurements from multiple radars dimension by dimension, and then recombines these measurements for LMMSE filtering. However, due to the inherent shortcomings of centralized fusion, they have potential limitations in practical application. In this paper, we first develop an information filtering form of the recursive LMMSE filter by equivalent transformation, to avoid the inverse operation of innovation covariance. Then, a recursive LMMSE sequential fusion with multi-radar measurements is presented depending on the information filter. The sequential fusion is theoretically optimal in the sense that it is equivalent to the LMMSE centralized fusion. Numerical examples show that the recursive LMMSE sequential fusion with recombined multi-radar measurements performs better in terms of estimation accuracy.