Performance analysis of α- β- γtracking filters using position and velocity measurements

Abstract

This paper examines the performance of two position-velocity-measured (PVM) α- β- γ tracking filters. The first estimates the target acceleration using the measured velocity, and the second, which is proposed for the first time in this paper, estimates acceleration using the measured position. To quantify the performance of these PVM α- β- γ filters, we analytically derive steady-state errors that assume that the target is moving with constant acceleration or jerk. With these performance indices, the optimal gains of the PVM α- β- γ filters are determined using a minimum-variance filter criterion. The performance of each filter under these optimal gains is then analyzed and compared. Numerical analyses clarify the performance of the PVM α- β- γ filters and verify that their accuracy is better than that of the general position-only-measured α- β- γ filter, even when the variance in velocity measurement noise is comparatively large. We identify the conditions under which the proposed PVM α- β- γ filter outperforms the general α- β- γ filter for different ratios of noise variance in the velocity and position measurements. Finally, numerical simulations verify the effectiveness of the PVM α- β- γ filters for a realistic maneuvering target.