Recursive LMMSE centralized fusion with recombination of multi-radar measurements

For target tracking with radar measurements, recursive LMMSE (Linear Minimum Mean Squared Error) filtering outperforms the popular measurement conversion based Kalman filters, which have some serious drawbacks in terms of both estimation accuracy and credibility. The existing recursive LMMSE with measurements from a single radar is first extended to the multi-radar case. It is then shown that recombination plays an important role in performance improvement for recursive LMMSE centralized fusion using multiple radars. Here, “recombination” means shuffling all scalar measurements from the multiple radars, dimension by dimension. This differs from the case of centralized fusion with linear measurements from multiple sensors. Numerical simulation examples are provided to illustrate the use of recombination in recursive LMMSE centralized fusion for the nonlinear radar measurements.