最优多步长自适应滤波

本文研究具有最优下降步长序列的自适应滤波。最优一词此处系指：①对于给定的均方误差的初值，使均方误差经给走次数学习后达到最小；②对于均方误差的给定初值和终值，经过最少次数的学习，均方误差由初值下降至终值。对于①②两种不同意义的最优，我们采用动态规划和对数比例分割方法来求各自的最优步长序列。同时提供一组仿真结果。     

一种新的最小均方误差线性合并算法

针对未编码的多输入多输出系统,将基于训练序列的最小均方误差(MMSE)信道估计算法与最优线性无偏估计结构(BLUE)相结合对已估计的信道参数进行估计.仿真结果表明,使用线性合并的MMSE算法比传统的MMSE算法具有较小的参数估计误差,比使用线性合并的LS算法性能更好.     

An improved sparsity-aware normalized least-mean-square scheme for underwater communication

Underwater communication (UWC) is widely used in coastal surveillance and early warning systems. Precise channel estimation is vital for efficient and reliable UWC. The sparse direct-adaptive filtering algorithms have become popular in UWC. Herein, we present an improved adaptive convex-combination method for the identification of sparse structures using a reweighted normalized least-mean-square (RNLMS) algorithm. Moreover, to make RNLMS algorithm independent of the reweighted $l_1$-norm parameter, a modified sparsity-aware adaptive zero-attracting RNLMS (AZA-RNLMS) algorithm is introduced to ensure accurate modeling. In addition, we present a quantitative analysis of this algorithm to evaluate the convergence speed and accuracy. Furthermore, we derive an excess mean-square-error expression that proves that the AZA-RNLMS algorithm performs better for the harsh underwater channel. The measured data from the experimental channel of SPACE08 is used for simulation, and results are presented to verify the performance of the proposed algorithm. The simulation results confirm that the proposed algorithm for underwater channel estimation performs better than the earlier schemes.