Multi-Hypothesis Post-Processing for Improving Air-to-Air Radar Tracking Accuracy

This paper presents a study on target track accuracy improvement for air-to-air (A/A) radar. A Multi-hypothesis Post-processing (MHPP) approach is proposed for improving the air target track accuracy. The MHPP approach consists of a Target Maneuver Detector (TMD) and a Target Maneuver-adapted Track Smoother (TMATS). TMD applies a simple tracking filter to the Kalman Filter (KF) outputs for making a decision on the presence of target maneuver. The TMD's decision is then utilized to guide TMATS to improve the overall track accuracy. TMATS is a filterbank that takes into account multiple hypotheses on the target maneuvering status. In particular, TMATS is constructed with multiple smoothing/tracking filters, each of which is dedicated to a different target maneuvering scenario. The final track outputs are selected from a particular TMATS component according to the target maneuvering status. Monte Carlo simulations are conducted to demonstrate the effectiveness of the proposed MHPP approach. The robustness of the proposed MHPP approach against the degree of target maneuvering is also verified with simulations.