Add Random PRI
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clc; clear; close all;
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parameters;
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global trail_times;
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[s_T_narrow, A] = narrow_signal_model(t, 1);
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A = A / 10;
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AH = A';
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H0 = [];
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for T = 1: trail_times
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x = zeros(10, 1);
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% x(4) = 0.8;
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y = A * x;
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y_noise = awgn(y, 15);
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x_hat = AH * y_noise;
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H0 = [H0 x_hat'];
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end
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H1 = [];
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for T = 1: trail_times
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x = zeros(10, 1);
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x(4) = 0.8;
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y = A * x;
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y_noise = awgn(y, 15);
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x_hat = AH * y_noise;
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H1 = [H1 x_hat(4)];
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end
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% figure; histfit(real(H0));
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% figure; histfit(real(H1));
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figure;
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plot()
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@@ -0,0 +1,25 @@
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function [s_T_narrow, A_narrow] = narrow_signal_model(t, betas_narrow)
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global f_c N_narrow N FIGURE N_wide numP f_s K_chirp T_r;
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tt = 0: 1/f_s: numP * T_r - 1/f_s;
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% 使用等效散射系数生成窄带情况信号模型
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s_T_narrow = zeros(1, N * numP);
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for j = 1: numP
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idx = ((j-1) * N + (1:N_narrow));
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tp = idx / f_s;
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s_T_narrow(1, idx) = exp(1j * 2 * pi * (f_c * tp + 0.5 * K_chirp * tp .^ 2));
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end
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% s_T_narrow = exp(1j .* 2 .* pi .* f_c .* t);
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s_R_narrows = zeros(N_narrow, N * numP);
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for i = 1: N_narrow
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p = i * (N_wide / N_narrow);
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s_R_narrows(i, :) = betas_narrow * [zeros(1, p), s_T_narrow(1: end-p)];
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end
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A_narrow = s_R_narrows';
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if FIGURE
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subplot(2, 1, 1); plot(tt, real(s_T_narrow));
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subplot(2, 1, 2); plot(tt, real(s_R_narrows(1, :)));
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end
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@@ -0,0 +1,59 @@
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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...
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delta_R_wide delta_R_narrow N_wide N_narrow ranges_wide...
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ranges_narrow betas_wide FIGURE K_wide K_narrow trail_times...
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method N_high TEST lambda tau iter_max noise_sigma FAR_N FAR_M noise_sigma_2 DEBUG_LEVEL...
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;
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f_c = 10e9; % X wave
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bandwidth = 100e6;
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T_p = 1e-5;
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f_s = 2 * bandwidth;
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T_s = 1 / f_s;
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K_chirp = bandwidth / T_p;
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T_r = T_p * 10;
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PRF = 1 / T_r;
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numP = 10;
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t = 0: 1 / f_s: T_r - 1 / f_s;
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tP = 0: 1 / f_s: T_r * numP - 1 / f_s;
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c = 3e8; % 光速
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N = length(t);
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N_high = T_p * f_s;
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M = round(T_p * bandwidth);
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R_0 = 0;
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delta_R_wide = c ./ 2 ./ bandwidth;
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delta_R_narrow = T_p .* c ./ 2;
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N_wide = 100;
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N_narrow = round(N_wide / M);
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% alert(N > N_narrow);
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ranges_wide = R_0 + (0:N_wide) * delta_R_wide; % [1000, 4000]
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ranges_narrow = R_0 + (0:N_narrow) * delta_R_narrow; % [1000, 4000]
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betas_wide = linspace(1, 0.1, N_wide) + 1j * linspace(0.1, 1, N_wide);
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K_wide = round(N_wide / 10);
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K_narrow = round(K_wide / 10);
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FIGURE = false;
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trail_times = 5000;
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% method = "debiased_LASSO";
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% method = "LASSO";
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% method = "BP";
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method = "cVAMPro";
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TEST = false;
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lambda = 0.01;
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tau = 1e-6;
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iter_max = 1000;
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noise_sigma = 0.1;
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noise_sigma_2 = 0.1;
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FAR_N = 480;
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FAR_M = 10;
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DEBUG_LEVEL = 1;
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