基于GA-MUSIC的matlab算法代码实现

以下是基于GA-MUSIC的MATLAB算法代码实现的示例：

%% GA-MUSIC Algorithm Implementation
% Created by [Your Name]
% Reference: [Reference Paper]

clc; clear all; close all;

%% Parameters

% Signal parameters
N = 100;     % Number of samples
M = 5;      % Number of sources
P = 10;     % Number of sensors
SNR = 10;   % Signal-to-Noise Ratio

% GA-MUSIC parameters
pop_size = 50;      % Population size
num_gen = 100;      % Number of generations
mut_rate = 0.1;     % Mutation rate
elite_frac = 0.2;   % Fraction of elite individuals
sel_frac = 0.5;     % Fraction of selected individuals

% Other parameters
c = physconst('LightSpeed');    % Speed of light
f = 2.4e9;                      % Signal frequency
d = c/(2*f);                    % Sensor spacing
theta = (-90:1:90)*pi/180;      % Search range for DOA estimation

%% Generate Data

% Generate signal
A = randn(M,P);     % Mixing matrix
S = randn(M,N);     % Source signal
X = A*S;            % Mixed signal

% Add noise
noise_var = norm(X,'fro')^2/(P*N*10^(SNR/10));
N = sqrt(noise_var)*randn(P,N);
X = X + N;

%% GA-MUSIC

% Initialize population
pop = zeros(pop_size,M,P);
for i = 1:pop_size
    pop(i,:,:) = rand(M,P);
end

% Iterate over generations
for gen = 1:num_gen
    
    % Evaluate fitness
    fitness = zeros(pop_size,1);
    for i = 1:pop_size
        W = squeeze(pop(i,:,:));
        [~,~,~,~,fitness(i)] = music(X,W,d,f,theta);
    end
    
    % Sort population by fitness
    [fitness,idx] = sort(fitness,'descend');
    pop = pop(idx,:,:);
    
    % Keep elite individuals
    elite_size = round(elite_frac*pop_size);
    elite = pop(1:elite_size,:,:);
    
    % Select parents
    sel_size = round(sel_frac*pop_size);
    sel_idx = randperm(pop_size,sel_size);
    sel = pop(sel_idx,:,:);
    
    % Crossover and mutate
    new_pop = zeros(pop_size,M,P);
    for i = 1:pop_size
        % Crossover
        p1 = sel(randi(sel_size),:,:);
        p2 = sel(randi(sel_size),:,:);
        mask = rand(M,P) > 0.5;
        new_pop(i,:,:) = mask.*p1 + (1-mask).*p2;
        
        % Mutate
        if rand() < mut_rate
            new_pop(i,:,:) = new_pop(i,:,:) + randn(M,P);
        end
    end
    
    % Combine elite, new population, and random individuals
    pop = cat(1,elite,new_pop);
    pop = cat(1,pop,rand(pop_size-pop_size*elite_frac, M, P));
    
end

% Get final best individual
W = squeeze(pop(1,:,:));

% Estimate DOA using MUSIC
[power,doa] = music(X,W,d,f,theta);

%% Results

% Plot results
figure;
plot(theta*180/pi,power);
xlabel('Angle (degrees)');
ylabel('Power (dB)');
title('GA-MUSIC DOA Estimation');

% Print estimated DOA
fprintf('Estimated DOA: %.2f degrees\n',doa*180/pi);

注意：该示例中使用了一个名为 music 的函数，该函数的输入参数为混合信号 X、传感器位置矩阵 W、传感器间距 d、信号频率 f 和搜索范围 theta，输出参数为估计的信号功率和方向（即DOA）。你需要编写这个函数以完成基于GA-MUSIC的MATLAB算法的完整实现。