SPARLS：一种低复杂度的递归$\mathcal{L}_1$ -正则化最小二乘算法
SPARLS: A Low Complexity Recursive $\mathcal{L}_1$-Regularized Least Squares Algorithm
摘要：
我们开发了一种递归$\mathcal{L}_1$ -正则化最小二乘（SPARLS）算法，用于估计自适应滤波设置中的稀疏抽头权重向量。SPARLS算法利用抽头权重向量输出流的噪声观测，并使用期望最大化类型算法来产生其估计。在信道估计的背景下，采用多径无线信道的仿真研究表明，SPARLS算法有显着的改善，比传统的广泛使用的递归最小二乘（RLS）算法，在均方误差（MSE）和计算复杂性。
英文摘要：
We develop a Recursive $\mathcal{L}_1$-Regularized Least Squares (SPARLS) algorithm for the estimation of a sparse tap-weight vector in the adaptive filtering setting. The SPARLS algorithm exploits noisy observations of the tap-weight vector output stream and produces its estimate using an Expectation-Maximization type algorithm. Simulation studies in the context of channel estimation, employing multi-path wireless channels, show that the SPARLS algorithm has significant improvement over the conventional widely-used Recursive Least Squares (RLS) algorithm, in terms of both mean squared error (MSE) and computational complexity.
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