Improving target position and velocity estimation for air-to-air radar

Abstract

This paper presents a study on the target position and velocity estimation for air-to-air radar. Two types of post-Kalman filtering (post-KF) processing methods, weighted least-square (WLS) estimators and tracking filters, are investigated to improve the filtering accuracy and thus to improve the target position and velocity estimation accuracy. The WLS estimators are considered up to the second order in order to effectively handle target acceleration. For this type of post- KF processing, the KF outputs within a sliding window are passed through a WLS estimator to refine the estimates of current target position and velocity and their variances. For the tracking filter method, an alpha-beta (α-β) filter and an alpha-beta-gamma (α-β-γ) filter are utilized; both dynamically smooth the KF outputs. For the linear motion scenarios where the target flies with constant velocity, the first-order WLS estimator and the α-β filter are expected to be a good fit, which permits accurate projection of the target position to a future time of interest. The second-order WLS estimator and the α-β-γ filter are capable of handling more general scenarios where the targets may be accelerating. The performance and effectiveness of these proposed post-KF processing methods are demonstrated by using Monte Carlo simulations.