提高信号信噪比的MATLAB算法

信号信噪比的盲估计算法是一种通过信号协方差矩阵的特征值分解来分离信号与噪声并估计信噪比的算法。该算法基于信号子空间，无需借助任何信道的参数信息，直接通过特征值分解的方式来计算信噪比的估计值。下面是一个MATLAB的信噪比估计算法的示例代码：

function snr_est = func_snr_est(s)
    m = 100; % 阶数
    su1 = 0;
    su2 = 0;
    L = length(s); % 信号接收长度
    N = L;
    R = []; % 协方差矩阵
    
    % 计算协方差矩阵
    for l = 1:m
        for k = l:m
            for i = 1:L-m
                su1 = su1 + s(i,l) * conj(s(i,k));
                su2 = su2 + s(i,k) * conj(s(i,l));
            end
            R(l,k) = su1 / (L-m);
            R(k,l) = su2 / (L-m);
            su1 = 0;
            su2 = 0;
        end
    end
    
    % 特征值分解
    [V,D = eig(R);
    da = abs(diag(D));
    T = da;
    T = sort(T, 'descend');
    
    % MDL估计信号子空间维数
    for k = 1:(m-1)
        he = sum(T(k+1:m)) / (m-k);
        ji = prod(T(k+1:m)) ^ (1/(m-k));
        MDL(k) = N * (m-k) * log10(he/ji) + 0.5 * k * (2*m-k) * log10(N);
        for i = 1:k
            ai(i) = (1 - 1/(m-i+1)) / (1 - 1/(m-i)) - 1;
        end
        bi = prod(ai(1:k));
        FSIC(k) = -2*(m-k) * log10(ji/he) * bi;
    end
    
    % 计算信噪比
    for k = 1:m-2
        rr(k) = abs(MDL(k) / (MDL(k) - 2*MDL(k+1)));
    end
    d = FSIC;
    dd = diff(d);
    r = abs(diff(dd));
    lr = 99;
    En = 0.98 * sum(T(lr+1:m)) / (m - lr); % 噪声平均功率
    Es1 = sum(T(1:lr)) - lr * En; % 信号能量
    snr_est = 10 * log10(abs(Es1 / (m * En)));
end

这个算法通过计算信号协方差矩阵的特征值分解，然后利用MDL和FSIC准则来估计信号子空间的维数，并最终计算出信噪比的估计值。这个算法具有较高的估算准确度，并能适应不同的信道特性。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [【信号子空间】基于信号子空间的信噪比盲估计算法matlab仿真](https://blog.csdn.net/ccsss22/article/details/125302685)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT0_1"}}] [.reference_item style="max-width: 100%"]
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