mx = 8; my = 8; % x轴和y轴阵元个数 sn = 4; % 信号个数 dw = 0.5; % 半径波长比 snr = 10; % 信噪比 N = 1000; % 采样点数 fangwei = [10, 25, 135, 170]; % 信号方位角 yangjiao = [60 80 20 10]; % 信号俯仰角 for i = 1:sn for m = 1:mx daoxiang1(m, i) = exp(-j * 2 * pi * dw * (m - 1) * cos(fangwei(i) * pi / 180) * cos(yangjiao(i) * pi / 180)); end for mm = 1:my daoxiang2(mm, i) = exp(-j * 2 * pi * dw * mm * sin(fangwei(i) * pi / 180) * cos(yangjiao(i) * pi / 180)); end ss(i, :) = randn(1, N); % 生成高斯白噪声 end daoxiang = [daoxiang1; daoxiang2]; Signal = daoxiang * ss; x = awgn(Signal, snr, 'measured'); % 加入高斯白噪声 R = x * x' / N; [tzxiangliang, tzzhi] = eig(R); Nspace = tzxiangliang(:, 1:mx + my - sn); % 噪声子空间对应小的特征值（从小到大排列） for azi = 1:180 for ele = 1:90 for m = 1:mx daoxiang3(m, 1) = exp(-j * 2 * pi * dw * (m - 1) * cos(azi * pi / 180) * cos(ele * pi / 180)); end for mm = 1:my daoxiang4(mm, 1) = exp(-j * 2 * pi * dw * mm * sin(azi * pi / 180) * cos(ele * pi / 180)); end AQ1 = [daoxiang3; daoxiang4]; Power = AQ1' * Nspace * Nspace' * AQ1; % 在1-180度范围内进行计算 P(ele, azi) = -10 * log10(abs(Power)); end end [ele_grid, azi_grid] = meshgrid(1:90, 1:180); [x, y, z] = sph2cart(azi_grid / 180 * pi, (90 - ele_grid) / 180 * pi, P); mesh(x, y, z); title('九元L阵;信噪比：[50,50,50,50]；距离波长比：0.5'); xlabel('x'); ylabel('y'); zlabel('z'); 把这段代码改成经过10次蒙特卡洛实验后，求出方位角和俯仰角的角度均值

以下是修改后的代码：

mx = 8; my = 8; % x轴和y轴阵元个数 
sn = 4; % 信号个数 
dw = 0.5; % 半径波长比 
snr = 10; % 信噪比 
N = 1000; % 采样点数 
fangwei = [10, 25, 135, 170]; % 信号方位角 
yangjiao = [60 80 20 10]; % 信号俯仰角 
num_monte_carlo = 10; % 蒙特卡洛实验次数 

azimuth_mean = zeros(num_monte_carlo, sn); % 存储方位角均值 
elevation_mean = zeros(num_monte_carlo, sn); % 存储俯仰角均值 

for monte_carlo_idx = 1:num_monte_carlo
    for i = 1:sn
        for m = 1:mx
            daoxiang1(m, i) = exp(-j * 2 * pi * dw * (m - 1) * cos(fangwei(i) * pi / 180) * cos(yangjiao(i) * pi / 180));
        end
        for mm = 1:my
            daoxiang2(mm, i) = exp(-j * 2 * pi * dw * mm * sin(fangwei(i) * pi / 180) * cos(yangjiao(i) * pi / 180));
        end
        ss(i, :) = randn(1, N); % 生成高斯白噪声 
    end
    daoxiang = [daoxiang1; daoxiang2];
    Signal = daoxiang * ss;
    x = awgn(Signal, snr, 'measured'); % 加入高斯白噪声 
    R = x * x' / N;
    [tzxiangliang, tzzhi] = eig(R);
    Nspace = tzxiangliang(:, 1:mx + my - sn); % 噪声子空间对应小的特征值（从小到大排列）

    P = zeros(90, 180); % 存储每个方位角和俯仰角的功率值
    for azi = 1:180
        for ele = 1:90
            for m = 1:mx
                daoxiang3(m, 1) = exp(-j * 2 * pi * dw * (m - 1) * cos(azi * pi / 180) * cos(ele * pi / 180));
            end
            for mm = 1:my
                daoxiang4(mm, 1) = exp(-j * 2 * pi * dw * mm * sin(azi * pi / 180) * cos(ele * pi / 180));
            end
            AQ1 = [daoxiang3; daoxiang4];
            Power = AQ1' * Nspace * Nspace' * AQ1; % 在1-180度范围内进行计算 
            P(ele, azi) = -10 * log10(abs(Power));
        end
    end

    % 求出方位角和俯仰角的均值
    for i = 1:sn
        [ele_idx, azi_idx] = find(P == max(P(:)));
        azimuth_mean(monte_carlo_idx, i) = mean(azi_idx);
        elevation_mean(monte_carlo_idx, i) = mean(ele_idx);
        P(ele_idx, azi_idx) = -100; % 将已经找到的最大值置为极小值，以便寻找下一个最大值
    end
end

% 显示结果
disp(['方位角均值：', num2str(mean(azimuth_mean)), ' 度']);
disp(['俯仰角均值：', num2str(mean(elevation_mean)), ' 度']);

以上代码进行了10次蒙特卡洛实验，每次实验都会生成不同的随机信号和高斯白噪声，以求得方位角和俯仰角的角度均值。最终输出结果显示方位角和俯仰角的角度均值。