A Kalman filter-based radar track data fusion algorithm applied to a select ICBM case

Abstract

A Kalman filter-based approach for fusing track data from two separate phased array radar sensors is developed and applied to a select ICBM case to demonstrate the potential enhancement of position and velocity estimates over a single radar. When compared to a theoretical assessment based on steady state filter performance, the Kalman filter approach yielded performance enhancements within 7% of theoretical prediction. The theoretical assessment indicated a 33% improvement in position accuracy and a 29% improvement in velocity accuracy for an assumed bias error in both radars. The simulation yielded a 29% improvement in position accuracy and a 22% improvement in velocity accuracy with the same bias assumption. The improvement was computed relative to the radar with twice the beamwidth and the same sensitivity as the second "fused" radar. The two radars were assumed to be collocated at the terminal area of ICBM flight.