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Abstract
The estimation of micro-Range (m-R) is important for micro-motion feature extraction and imaging, which provides significant supports for the classification of a precession cone-shaped target. Under low signal-to-noise ratio (SNR) circumstances, the modified Kalman filter (MKF) will obtain broken segments rather than complete m-R tracks due to missing trajectories, and the performance of the MKF is restricted by unknown noise covariance. To solve these problems, a noise-robust m-R estimation method, which combines the adaptive Kalman filter (AKF) and the random sample consensus (RANSAC) algorithm, is proposed in this paper. The AKF, where the noise covariance is not required for the estimation of the state vector, is applied to associate m-R trajectories for higher estimation accuracy and lower wrong association probability. Due to missing trajectories, several associated segments which are parts of the m-R tracks can be obtained by the AKF. Then, the RANSAC algorithm is utilized to associate the segments and the complete m-R tracks can be obtained. Compared with the MKF, the proposed method can obtain complete m-R tracks instead of several segments, and avoids the influence of unknown noise covariance under low SNR circumstances. Experimental results based on electromagnetic simulation data demonstrate that the proposed method is more precise and robust compared with traditional methods.
Highlights
With increasing human activities in outer space, research of space targets has attracted more and more attention [1,2]
We use the electromagnetic computation data of the cone-shaped target in the comparison experiment to analyze the robustness of the proposed algorithm, and show the estimation and computational advantages compared with conventional algorithms
The electromagnetic computation data were obtained by FEKO using the physical optics (PO) method
Summary
With increasing human activities in outer space, research of space targets has attracted more and more attention [1,2]. Since trajectories of the weak scatter center cannot be extracted under low SNR circumstances, several associated segments, not the complete m-R tracks, will be obtained by the MKF. A trajectory association method, which combines the adaptive Kalman filter (AKF) [21] and the random sample consensus (RANSAC) algorithm [22], is proposed to obtain complete m-R tracks and deal with the unknown noise covariance problem in a low SNR circumstance. Compared with the MKF, the proposed method can obtain complete m-R tracks instead of segments, and has a better performance on the estimation accuracy and time cost. When noise covariance is inaccurate or mismatched, the proposed method can obtain higher accuracy estimation results and decrease wrong association probability under low SNR circumstances.
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