【雷达通信】完全空间时域自适应处理演示（matlab实现）

荧光Matlab

已于 2024-03-24 21:45:52 修改

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文章标签： matlab 开发语言

于 2024-03-24 12:34:59 首次发布

本文链接：https://blog.youkuaiyun.com/Matlab88888/article/details/136985488

本文详细介绍了全空间时域自适应处理技术在雷达信号处理中的应用，包括原理、实现步骤，并提供了Matlab代码示例。通过使用自适应滤波器改善信号质量和准确性，适用于雷达系统中信号处理需求。

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📋📋📋 本文目录如下： 🎁🎁🎁

💥1 概述

这个演示文件展示了全空间时域自适应处理（fullySTAP）的基本原理和实现方法。全空间时域自适应处理是一种用于雷达信号处理的高级技术，它利用自适应滤波器对雷达接收到的信号进行处理，以提高信号的质量和准确性。

📚2 运行结果

主函数部分代码：

close all; clear;
lambda = 0.03; % wavelength, not neccessary in array signal processing
d = lambda/2; % interelement spacing

N = 10; % the number of elements in the ULA
M = 12; % the number of pulses per CPI

CNR = 50;   % dB Clutter to Noise Ratio  
SNR = 0;    % dB Signal to Noise Ratio 
JNR = 30;   % dB Jammer to Noise Ratio

noisePower = 1;
clutterPower = noisePower * 10^(CNR/10);
tgtPower = noisePower * 10^(SNR/10);
jammerPower = noisePower * 10^(JNR/10);

beta = 1; % Eq.(3.2) in [1],  control the way the clutter filling the angle Doppler plane


%% (1) Rc, clutter covariance
% ---the ground clutter ridge, see Eq.(3.2) in [1] ----
% the clutter Doppler shift induced on the clutter patch on the iso-range ring situated at the angle 'theta'
% fd_ClutterNormalized = beta*spatialFreq_normalized, where
% fd_ClutterNormalized = fd_Clutter/PRF;  % fd_Clutter is the Doppler frequency of clutter
% spatialFreq_normalized = d*sin(theta)/lambda;

% (debug1)--- for debug ---
No = 250;       % k-th clutter bins
sintheta = linspace(-1, 1, No);
clutterSpatialFreq_normalized = d./lambda*sintheta;
%% (debug1)--- for practice ---
%clutterAzimuth = -90:1:90; % since backlobe rejection
%clutterSpatialFreq_normalized = d./lambda*sind(clutterAzimuth); % d./lambda = 0.5

fd_ClutterNormalized = beta*clutterSpatialFreq_normalized; % fd_ClutterNormalized
Rc = complex(zeros(M*N));
V = zeros(M*N, length(clutterSpatialFreq_normalized));
for k = 1:length(clutterSpatialFreq_normalized)
	a_clutter = exp(-1j*2*pi*clutterSpatialFreq_normalized(k)*[0: N - 1].'); % spatial steering vector
	b_clutter = exp(-1j*2*pi*fd_ClutterNormalized(k)*[0:M - 1].');