Recursive least squares ladder estimation algorithms
Recursive least squares ladder estimation algorithms
复制标题
DOI：
10.1109/tcs.1981.1085020
复制
发表时间：
1981-06
期刊：
影响因子：
--
通讯作者：
D. Lee;M. Morf;B. Friedlander
中科院分区：
文献类型：
--
作者：
D. Lee;M. Morf;B. Friedlander
与基于梯度的算法相比，递推最小二乘阶梯估计算法由于其优良的收敛性能和快速的参数跟踪能力而受到广泛关注。我们提出了一些最近开发的平方根归一化精确最小二乘梯形算法，具有更少的存储要求，和较低的计算要求比非归一化的。提出了一种基于Hilbert空间的幅度归一化信号和增益递推的推导方法。规范化的形式预计将有更好的数值性能比非规范化的版本。其他归一化形式，例如联合过程估计器（例如，"自适应谱线增强器"）和阿尔马（极点-零）模型。这些算法的应用快速（或“零”）启动均衡器，自适应噪声和回声消除器，非高斯事件检测器，和控制问题的逆模型也提到。
Recursive least squares ladder estimation algorithms have attracted much attention recently because of their excellent convergence behavior and fast parameter tracking capability, compared to gradient based algorithms. We present some recently developed square root normalized exact least squares ladder form algorithms that have fewer storage requirements, and lower computational requirements than the unnormalized ones. A Hilbert space approach to the derivations of magnitude normalized signal and gain recursions is presented. The normalized forms are expected to have even better numerical properties than the unnormalized versions. Other normalized forms, such as joint process estimators (e.g., "adaptive line enhancer") and ARMA (pole-zero) models, will also be presented. Applications of these algorithms to fast (or "zero") startup equalizers, adaptive noise- and echo cancellers, non-Gaussian event detectors, and inverse models for control problems are also mentioned.