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.

中文翻译：

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进动圆锥形目标的噪声鲁棒微范围估计方法

微范围（mR）的估计对于微运动特征提取和成像很重要，这为旋进锥形目标的分类提供了重要的支持。在低信噪比（SNR）的情况下，由于缺少轨迹，改进的卡尔曼滤波器（MKF）将获得断线段，而不是完整的mR轨道，并且MKF的性能受到未知噪声协方差的限制。为了解决这些问题，本文提出了一种结合了自适应卡尔曼滤波器（AKF）和随机样本一致性（RANSAC）算法的鲁棒mR估计方法。AKF用于状态矢量的估计，不需要噪声协方差，该AKF用于关联mR轨迹，以实现更高的估计精度和更低的错误关联概率。由于缺少轨迹，AKF可以获取作为mR轨迹一部分的几个相关段。然后，使用RANSAC算法将片段关联起来，可以获得完整的mR轨道。与MKF相比，该方法可以获得完整的mR轨迹，而不是几个片段，并且避免了在低SNR情况下未知噪声协方差的影响。基于电磁仿真数据的实验结果表明，与传统方法相比，该方法具有更高的精确度和鲁棒性。所提出的方法可以获得完整的mR轨迹，而不是几个片段，并且避免了在低SNR情况下未知噪声协方差的影响。基于电磁仿真数据的实验结果表明，与传统方法相比，该方法具有更高的精确度和鲁棒性。所提出的方法可以获得完整的mR轨迹，而不是几个片段，并且避免了在低SNR情况下未知噪声协方差的影响。基于电磁仿真数据的实验结果表明，与传统方法相比，该方法具有更高的精确度和鲁棒性。