%% 超参数
global c f_c B T_r N F_s M k lambda
index = 1:1:N;  %产生点向量
IF_mat = zeros(M,N);  %存储带有噪声的中频信号
t = 0:1/F_s:T_r-1/F_s;  %时间向量 确定256个点在一个Tr中的每个时刻
t = t - T_r/2; %将fc作为中心频率

dist = 10;  %目标距离雷达50m的距离
t_d = 2 * dist / c;  %目标距离雷达的延迟
velocity = -20;  %目标距雷达的相对速度为30m/s
f_d = 2 * f_c * velocity / c;  %多普勒频移
% f_d = 2 * (f_c - B/2) * velocity / c;  %多普勒频移
scat_coef = 0.8;  %回波信号衰减的比例值

s_T = chirp(t, f_c, t(end), f_c + B);
pad = round(t_d * F_s);
s_R = [zeros(1, pad), scat_coef * s_T(1: end - pad)];

for i = 1: M
    s_T = 1*exp((1i*2*pi)*(f_c*(t+i*T_r)+k/2*t.^2)); %发射信号
    s_R = scat_coef*exp((1i*2*pi)*((f_c-f_d)*(t-t_d+i*T_r)+k/2*(t-t_d).^2));  %回波信号

    IF = s_T .* conj(s_R);
    % SNR = 10;
    % IF_Noise = awgn(IF, SNR, 'measured');
    IF_Noise = IF;
    IF_mat(i, :) = IF_Noise;
end

save('Ego_vehicle_ksy.mat','IF_mat');

% figure;
% subplot(211);
% plot(t, s_T);
% ylim([-1.3, 1.3]);
% 
% subplot(212);
% plot(t, s_R);
% ylim([-1.3, 1.3]);