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 the classification a precession cone-shaped target. Under low signal-to-noise ratio (SNR) circumstances, modified Kalman filter (MKF) will obtain broken segments rather than complete m-R tracks due to missing trajectories, performance MKF restricted by unknown noise covariance. To solve these problems, noise-robust method, combines adaptive (AKF) random sample consensus (RANSAC) algorithm, proposed in this paper. AKF, where covariance not required state vector, applied associate trajectories higher accuracy lower wrong association probability. Due several associated are parts can be obtained AKF. Then, RANSAC algorithm utilized obtained. Compared with MKF, method instead segments, avoids influence under SNR circumstances. Experimental results based on electromagnetic simulation data demonstrate that more precise robust compared traditional methods.