A Kalman filter-based radar track data fusion algorithm applied to a select ICBM case
John G Ferrante - Lockheed Martin MS2 Moorestown Advanced Systems
Tue, 27 April 2004, 3:20 PM - 4:10 PM
A Kalman filter-based approach to 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.
The simulated estimates were obtained by a Monte Carlo approach which averaged the results of simulation runs of fused and single radar position and velocity estimates of a known ICBM trajectory. The trajectory was generated by solving the matrix differential equations of motion for both the exo and endoatmospheric portions of the ICBM flight. MATHCAD-based simulations incorporating trajectory generation and track data fusion algorithms were developed to perform the described analyses.
Mr. John G Ferrante - Lockheed Martin MS2 Moorestown Advanced Systems
Mr. Ferrante has over 20 years of experience in the systems engineering and design of radars, surveillance and combat systems.