A Noise Robust Micro-Range Estimation Method for Precession Cone-Shaped Targets

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.

Automated summary

This paper proposes a noise-robust m-R estimation method that combines the adaptive Kalman filter and the random sample consensus algorithm, which can obtain complete m-R tracks under low signal-to-noise ratio conditions.