写一段200*128的发射信号和200*128的接收信号的信噪比matlab代码

以下是200*128的发射信号和接收信号的信噪比matlab代码：

发射信号：
signal = randn(200,128);

接收信号：
noise = randn(200,128);
received_signal = signal + noise;

信噪比：
SNR = 10*log10(sum(signal(:).^2)/sum(noise(:).^2));

相关问题

matlab仿真雷达发射和接收信号

### Matlab 中进行雷达发射和接收信号仿真的方法

#### 定义雷达系统参数
为了在Matlab中实现毫米波雷达的发射和接收信号仿真，首先要定义雷达系统的各项参数。这些参数通常包括但不限于工作频率、天线增益以及传输功率等[^3]。

% Radar system parameters definition
fc = 77e9; % Working frequency (for example, 77 GHz is common for automotive radar applications)
c = physconst('LightSpeed'); % Speed of light constant from MATLAB's built-in function
lambda = c/fc; % Wavelength calculation based on the working frequency and speed of light
antennaGain = 40; % Antenna gain in dB
transmitPower_dBm = 10; % Transmit power in dBm which can be converted to Watts when needed.

#### 创建发射信号模型
创建一个适合于特定应用环境下的发射信号非常重要。这里以线性调频（LFM）脉冲为例来展示如何构建这样的信号，在连续波雷达仿真中有广泛应用[^2]。

Fs = 5*fc; % Sampling rate set much higher than carrier frequency
T_pulse = 1e-6; % Pulse duration time
BW = 1/T_pulse; % Bandwidth equals inverse pulse width
K = BW/T_pulse; % Frequency modulation slope K=Δf/τ
t = [-T_pulse/2 : 1/Fs : T_pulse/2]; % Time vector centered around zero
chirpSignal = exp(1i*pi*K*t.^2); % LFM chirped signal generation formula

#### 模拟目标反射特性并形成回波信号
当考虑真实世界中的情况时，还需要加入对于不同距离处多个目标产生的多径效应及其对应的RCS（雷达截面积）。这一步骤通过向原始发射信号施加延迟与衰减因子来模仿实际传播路径上的变化效果。

targetRange_meters = [50 80 120]; % Distances of three hypothetical targets in meters
rangeCellSize = c/(2*fs);
relativeAmplitude = [0.5 1 0.7]; % Relative amplitudes representing RCS values or similar properties
delaySamples = round((2 * targetRange_meters ./ c)*Fs); % Calculate delays due to propagation times at each range cell location
echoSignals = zeros(size(t)); 

for k = 1:length(targetRange_meters)
    echoSignals = echoSignals + relativeAmplitude(k).*circshift(chirpSignal,[0 delaySamples(k)]);
end;

#### 计算接收到的总信号强度及信噪比(SNR)
最终阶段涉及到对接收端所捕获到的所有成分——即来自各个方向上所有可能存在的散射体贡献之和——做进一步处理以便得到更清晰的结果表示形式；同时也要考虑到噪声的影响因素从而能够准确衡量整个过程的有效性和可靠性水平。

noiseVariance = 1e-6; % Assume a certain level of additive white Gaussian noise variance
receivedSignal = awgn(echoSignals,10*log10(noiseVariance),'measured');
snrLinearScale = mean(abs(receivedSignal).^2)/noiseVariance;

fprintf('The calculated SNR value is %.2fdB\n',10*log10(snrLinearScale));

用相干信号子空间法写一段基于主瓣干扰抑制的阻塞矩阵预处理的宽带波束形成算法的MATLAB代码

% 宽带波束形成算法示例代码
% 使用相干信号子空间法及阻塞矩阵预处理抑制主瓣干扰

clc; clear all; close all;

%% 参数设置
K = 4; % 子带数
N = 8; % 天线数
M = 16; % 信号数
theta = [-30 0 30]; % 信号入射角度
f = [0.1 0.3 0.5]; % 子带频率
SNR = 20; % 信噪比

%% 信号发射矩阵
S = exp(1j*2*pi*(0:M-1)'*sind(theta)); % 信号发射矩阵

%% 接收信号模拟
noise = sqrt(0.5/SNR)*(randn(N,K,M)+1j*randn(N,K,M)); % 噪声信号
A = exp(-1j*2*pi*f'*(0:N-1)); % 天线阵列响应矩阵
X = zeros(N,K,M); % 接收信号矩阵
for k = 1:K
X(:,k,:) = A*S(:,k,:) + noise(:,k,:); % 接收信号
end

%% 相干信号子空间法波束形成
Rxx = zeros(N,N,K); % 信号协方差矩阵
for k = 1:K
Rxx(:,:,k) = X(:,k,:)*X(:,k,:)'/M;
end
Rx = sum(Rxx,3)/K; % 平均信号协方差矩阵
[V,D] = eig(Rx); % 特征分解
[lambda,idx] = sort(diag(D),'descend'); % 特征值排序
V = V(:,idx); % 特征向量排序
V1 = V(:,1:M-K); % 干扰信号子空间
V2 = V(:,M-K+1:end); % 信号子空间
W = V2*V2'/V1/V1'; % 波束权向量
P = W*diag(diag(W)'>0); % 阻塞矩阵预处理
y = zeros(K,M); % 波束输出
for k = 1:K
y(k,:) = P*X(:,k,:)*W; % 波束输出
end

%% 结果显示
figure;
subplot(121);
plot(-90:90, 20*log10(abs(W'*A*S(:,1))), '-r', ...
-90:90, 20*log10(abs(W'*A*S(:,2))), '-g', ...
-90:90, 20*log10(abs(W'*A*S(:,3))), '-b');
legend('S1', 'S2', 'S3');
xlabel('Angle (degree)'); ylabel('Amplitude (dB)');
title('Antenna array pattern without blocking matrix');
grid on;
subplot(122);
plot(-90:90, 20*log10(abs(P*W'*A*S(:,1))), '-r', ...
-90:90, 20*log10(abs(P*W'*A*S(:,2))), '-g', ...
-90:90, 20*log10(abs(P*W'*A*S(:,3))), '-b');
legend('S1', 'S2', 'S3');
xlabel('Angle (degree)'); ylabel('Amplitude (dB)');
title('Antenna array pattern with blocking matrix');
grid on;