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.
Highlights
Remote monitoring systems embedded in robots and vehicles require the capability to accurately track moving objects
In this paper, we have examined the performance of two PVM α-β-γ filters: the Acceleration smoothed by measured velocity (A-V) filter and the newly proposed Acceleration smoothed by measured position (A-P) filter
Based on these performance indices, we calculated the optimal gains of the PVM α-β-γ filters with the MV filter criterion
Summary
Remote monitoring systems embedded in robots and vehicles require the capability to accurately track moving objects. These indices are more effective in evaluating the steady-state tracking accuracy than the error covariance matrix in the Kalman filter equation, which is the usual performance indicator for tracking filters This is because the error covariance matrix overrates the variance in the errors that is caused by measurement noise, as verified by Ekstrand (see Section 9.8 of [6]). Steady-state error for a target under constant acceleration (smoothing performance index) An important function of the tracking filter is the reduction of random errors caused by measurement noise. The second type is a new PVM α-β-γ filter that is being proposed for the first time in this paper The aim of this new filter is to achieve accurate tracking, even when the noise in the velocity measurements is comparatively large. The performance indices σp and efin are derived analytically for each PVM α-β-γ filter
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