Combined Kalman filter (CKF) and JVC algorithms for AEW target tracking applications

Tracking for airborne early warning (AEW) weapon systems present a number of formidable challenges for any tracking and data fusion algorithms. Realistic scenarios involve thousands of targets in highly cluttered environments with multiple sensors. The E-2C weapon system must detect, track and identify these targets in as small a time frame as possible. As part of ongoing E-2C advanced tracking algorithm development activities a novel approach has been developed that utilizes the debiased coordinate conversion filter developed by Bar-Shalom and Lerro (1993) for range, and azimuth angle processing from the radar and standard EKF for rdot and other angular measurements from other sensors identified as a combined Kalman filter (CKF). To solve the data association problem the JVC algorithm [Jonker-Volgenant Castanon (1988)] was chosen because of favorable results from published studies and internally conducted in-house studies that demonstrate its speed and efficiency in solving the assignment problem for sparse matrices which is typical for E-2C applications. Results shown are based on a scenario consisting of 120 straight line and maneuvering targets overlaid on a previously recorded dense radar environment. Future plans have been initiated to incorporate other sensors and consider other association algorithms such as multi-hypothesis tracking (MHT) or interactive multiple model joint probabilistic data association filter (IMMJPDAF).© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.