Recursive Weighted Robust Least Squares Filter for Frequency Estimation
Recursive Weighted Robust Least Squares Filter for Frequency Estimation
复制标题
用于频率估计的递归加权鲁棒最小二乘滤波器
DOI：
10.1109/sice.2006.315272
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
I. Whang
中科院分区：
文献类型：
--
作者：
W. Ra;I. Whang
提出了一种新的加权鲁棒最小二乘估计方法来设计单音正弦波递推频率估计器。将频率估计问题重新表述为在测量矩阵中包含随机参数不确定性的线性时变系统下对慢变参数的辨识问题。通过采用统计补偿方案，该鲁棒频率估计器成功地消除了名义加权最小二乘频率估计器的尺度因子误差。该算法在存在严重传感器测量噪声的情况下，具有准确的频率估计性能和较宽的鲁棒性。通过在估计量中加入遗忘因子，使算法具有较快的收敛性和自适应性。此外，由于与现有的估计器相比，它需要较少的计算量，因此对实时实现具有吸引力。介绍了该技术的理论基础和性能评价结果
A novel weighted robust least squares estimation approach to the design of recursive frequency estimator for single tone sinusoid is presented. The frequency estimation problem is reformulated as the identification of slowly varying parameters subject to a linear time-varying system which contains the stochastic parametric uncertainties in the measurement matrix. By employing the statistical compensation scheme, the proposed robust frequency estimator successfully eliminates the scale-factor error of nominal weighted least squares frequency estimator. The algorithm shows accurate frequency estimation performance and wide range of robustness in the presence of severe sensor measurement noises. By incorporating the forgetting factor to the estimator, the algorithm can achieve fast convergency and adaptability. Moreover, since it requires small amount of computations compared to the existing estimators, it is attractive for real-time implementation. Theoretical basis and performance evaluation results of this technique are described