5个距 离单元，即在第50~54处，另有两个点目标分别在150和154个距离单元，三个 目标的信噪比分别为75dB、10dB和20dB，虚警概率Pfa=10-6，利用CA-CFAR、GO-CFAR、SO-CFAR进行检测的结果，的matlab代码编写

以下是一个简单的Matlab代码，用于在给定条件下使用CA-CFAR、GO-CFAR和SO-CFAR进行检测：

% 假设有5个距离单元
num_cells = 5;

% 假设有3个目标，其信噪比分别为75dB、10dB和20dB，目标在第50~54处和150和154处
target_snr = [75 10 20];
target_cells = [50:54 150 154];

% 虚警概率
Pfa = 10^-6;

% 生成随机噪声数据
noise_data = randn(1, num_cells);

% 计算每个算法的阈值
CA_CFAR_threshold = cfar_ca(noise_data, Pfa);
GO_CFAR_threshold = cfar_go(noise_data, Pfa);
SO_CFAR_threshold = cfar_so(noise_data, Pfa);

% 生成包含目标和噪声的数据
data = zeros(1, num_cells);
for i = 1:length(target_cells)
    data(target_cells(i)) = 10^(target_snr(i)/10);
end
data = data + noise_data;

% 使用每个算法检测目标
CA_CFAR_result = cfar_ca(data, Pfa, CA_CFAR_threshold);
GO_CFAR_result = cfar_go(data, Pfa, GO_CFAR_threshold);
SO_CFAR_result = cfar_so(data, Pfa, SO_CFAR_threshold);

% 显示结果
disp(['CA-CFAR检测结果：' num2str(CA_CFAR_result)]);
disp(['GO-CFAR检测结果：' num2str(GO_CFAR_result)]);
disp(['SO-CFAR检测结果：' num2str(SO_CFAR_result)]);

% CFAR算法函数实现
function threshold = cfar_ca(data, Pfa)
num_cells = length(data);
num_guard_cells = 2;
num_train_cells = 2*num_guard_cells + 1;
num_target_cells = num_cells - num_train_cells - num_guard_cells;

% 计算训练区域的平均噪声功率
train_cells = sort([num_guard_cells+1:num_guard_cells+num_train_cells, num_cells-num_guard_cells-num_train_cells+1:num_cells-num_guard_cells]);
train_power = sort(data(train_cells));
train_power = train_power(2:end-1);
train_avg_power = sum(train_power) / length(train_power);

% 计算阈值
threshold = train_avg_power * (Pfa^(-1/num_target_cells) - 1);
end

function threshold = cfar_go(data, Pfa)
num_cells = length(data);
num_guard_cells = 2;
num_train_cells = 2*num_guard_cells + 1;
num_target_cells = num_cells - num_train_cells - num_guard_cells;

% 计算训练区域的平均噪声功率
train_cells = sort([num_guard_cells+1:num_guard_cells+num_train_cells, num_cells-num_guard_cells-num_train_cells+1:num_cells-num_guard_cells]);
train_power = sort(data(train_cells));
train_power = train_power(2:end-1);
train_avg_power = sum(train_power) / length(train_power);

% 计算阈值
threshold = train_avg_power * ((Pfa*num_target_cells)^(-1/num_target_cells));
end

function threshold = cfar_so(data, Pfa)
num_cells = length(data);
num_guard_cells = 2;
num_train_cells = 2*num_guard_cells + 1;
num_target_cells = num_cells - num_train_cells - num_guard_cells;

% 计算训练区域的平均噪声功率
train_cells = sort([num_guard_cells+1:num_guard_cells+num_train_cells, num_cells-num_guard_cells-num_train_cells+1:num_cells-num_guard_cells]);
train_power = sort(data(train_cells));
train_power = train_power(2:end-1);
train_avg_power = sum(train_power) / length(train_power);

% 计算阈值
threshold = train_avg_power * (Pfa^(-1/num_train_cells));
end

注意，这只是一个简单的实现，可能并不是最优的实现方式，因此在实际应用中可能需要进行优化。此外，这个实现还假设所有目标的信噪比都是已知的，这在实际应用中也不一定成立，因此需要采用更复杂的算法来估计目标的信噪比。