RLS

使用Matlab中的递归最小二乘（RLS）算法实现均衡技术

This is the code for implementing the RLS adaptive algorithm filter. The RLS (Recursive Least Squares) algorithm is widely used in adaptive filtering applications. Below is the MATLAB implementation of the RLS adaptive filter which helps in understanding the core concepts of adaptive filtering and recursive algorithms. % MATLAB code for RLS adaptive filter N = 1000; % Number of filter coefficients M = 32; % Filter order lambda = 0.99; % Forgetting factor delta = 10; % Initialization constant % Initialize filter coefficients and variables w = zeros(M, 1); % Filter weights P = delta * eye(M); % Inverse correlation matrix % Simulate the input signal and desired output x = randn(N, 1); % Input signal d = filter([1, -0.9], 1, x); ?sired signal % RLS adaptive filtering loop for n = M+1:N x_n = x(n:-1:n-M+1); % Input vector e = d(n) - w' * x_n; % Error signal k = P * x_n / (lambda + x_n' * P * x_n); % Gain vector w = w + k * e; % Update weights P = (P - k * x_n' * P) / lambda; % Update inverse correlation matrix end % Display results figure; plot(d, 'b'); hold on; plot(filter(w, 1, x), 'r'); legend('Desired', 'Filtered Output'); This code illustrates how to apply the RLS adaptive filter to adjust its coefficients to minimize the error between the desired signal and the filter output.

本项目利用MATLAB实现了LMS和RLS两种自适应滤波算法，并通过测试绘制了学习曲线和误差曲线，以评估算法性能。

这个算法已经在MATLAB中进行了仿真，可以完全使用。

经典beamforming、自适应滤波教材MATLAB源代码。Paulo S.R. Diniz编著的《Adaptive Filtering Algorithms and Practical Implementation 4th Edition》中的源代码——Fast Transversal RLS Algorithms。

提供了使用最小均方算法(LMS)和递归最小二乘算法(RLS)进行数字信号处理系统辨识的MATLAB代码实现。这两种算法是自适应滤波领域的核心技术，被广泛应用于系统建模和参数估计。

这份资源提供了Matlab代码，涵盖了自适应滤波背景下LMS、RLS和Kalman算法的应用。内容简洁易懂，适合即拿即用。

基于Matlab实现的LMS和RLS算法在盲从多用户检测方面进行了详细比较分析。这些算法的性能特征和适用场景被深入探讨，以评估它们在实际应用中的优缺点和效果差异。

SSRLSWAM是将SSRLS和随机梯度法结合的算法。它引入了遗忘因子λ的概念，以应对模型不确定性、未知外部干扰、观测信号的时变性质或观测噪声的非平稳行为。该算法特别适用于以Chirp信号或低频信号作为输入的情况。