Add Random PRI

This commit is contained in:
Ksyer
2024-07-16 15:55:41 +08:00
parent 8c906b6618
commit 95c0eb89b8
3 changed files with 125 additions and 0 deletions
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clc; clear; close all;
parameters;
global trail_times;
[s_T_narrow, A] = narrow_signal_model(t, 1);
A = A / 10;
AH = A';
H0 = [];
for T = 1: trail_times
x = zeros(10, 1);
% x(4) = 0.8;
y = A * x;
y_noise = awgn(y, 15);
x_hat = AH * y_noise;
H0 = [H0 x_hat'];
end
H1 = [];
for T = 1: trail_times
x = zeros(10, 1);
x(4) = 0.8;
y = A * x;
y_noise = awgn(y, 15);
x_hat = AH * y_noise;
H1 = [H1 x_hat(4)];
end
% figure; histfit(real(H0));
% figure; histfit(real(H1));
figure;
plot()
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function [s_T_narrow, A_narrow] = narrow_signal_model(t, betas_narrow)
global f_c N_narrow N FIGURE N_wide numP f_s K_chirp T_r;
tt = 0: 1/f_s: numP * T_r - 1/f_s;
% 使用等效散射系数生成窄带情况信号模型
s_T_narrow = zeros(1, N * numP);
for j = 1: numP
idx = ((j-1) * N + (1:N_narrow));
tp = idx / f_s;
s_T_narrow(1, idx) = exp(1j * 2 * pi * (f_c * tp + 0.5 * K_chirp * tp .^ 2));
end
% s_T_narrow = exp(1j .* 2 .* pi .* f_c .* t);
s_R_narrows = zeros(N_narrow, N * numP);
for i = 1: N_narrow
p = i * (N_wide / N_narrow);
s_R_narrows(i, :) = betas_narrow * [zeros(1, p), s_T_narrow(1: end-p)];
end
A_narrow = s_R_narrows';
if FIGURE
subplot(2, 1, 1); plot(tt, real(s_T_narrow));
subplot(2, 1, 2); plot(tt, real(s_R_narrows(1, :)));
end
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global bandwidth T_p f_s T_s K_chirp f_c PRF T_r numP t tP c N M R_0...
delta_R_wide delta_R_narrow N_wide N_narrow ranges_wide...
ranges_narrow betas_wide FIGURE K_wide K_narrow trail_times...
method N_high TEST lambda tau iter_max noise_sigma FAR_N FAR_M noise_sigma_2 DEBUG_LEVEL...
;
f_c = 10e9; % X wave
bandwidth = 100e6;
T_p = 1e-5;
f_s = 2 * bandwidth;
T_s = 1 / f_s;
K_chirp = bandwidth / T_p;
T_r = T_p * 10;
PRF = 1 / T_r;
numP = 10;
t = 0: 1 / f_s: T_r - 1 / f_s;
tP = 0: 1 / f_s: T_r * numP - 1 / f_s;
c = 3e8; % 光速
N = length(t);
N_high = T_p * f_s;
M = round(T_p * bandwidth);
R_0 = 0;
delta_R_wide = c ./ 2 ./ bandwidth;
delta_R_narrow = T_p .* c ./ 2;
N_wide = 100;
N_narrow = round(N_wide / M);
% alert(N > N_narrow);
ranges_wide = R_0 + (0:N_wide) * delta_R_wide; % [1000, 4000]
ranges_narrow = R_0 + (0:N_narrow) * delta_R_narrow; % [1000, 4000]
betas_wide = linspace(1, 0.1, N_wide) + 1j * linspace(0.1, 1, N_wide);
K_wide = round(N_wide / 10);
K_narrow = round(K_wide / 10);
FIGURE = false;
trail_times = 5000;
% method = "debiased_LASSO";
% method = "LASSO";
% method = "BP";
method = "cVAMPro";
TEST = false;
lambda = 0.01;
tau = 1e-6;
iter_max = 1000;
noise_sigma = 0.1;
noise_sigma_2 = 0.1;
FAR_N = 480;
FAR_M = 10;
DEBUG_LEVEL = 1;