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⛄ 内容介绍
Blind signal detection is very important in various applications such as cognitive radio, spectrum surveillance, and eavesdropping. This gets trickier when one needs to deal with burst signals (as opposed to continuously transmitted signals) in non-cooperative environments. Here, existing methods for signal detection are studied and developed to use for burst detection.For the special case of downlink time division multiple access (TDMA) burst transmission, performances of these detection methods are improved by proposing a blind synchronisation algorithm which is applied to the output result of detection algorithms. The results of Monte–Carlo simulations demonstrate the performance of the proposed detection and synchronisation algorithms.
⛄ 完整代码
clc;
clear;
close all
%% 产生噪声与信号源
code_fre = 1e6; %信号载波频率
fs = 10e6; %采样频率
sample_t = 1/fs;%采样间隔
length_all = 400;%仿真信号总长度（符号数量）
noise_std = 1;%噪声方差
%snr = 0;%信噪比
burst_length = 30; %每个突发信号的长度
Ncoh = (fs / code_fre)*burst_length;%一个积分清除时间内的采样点数
dot_length = [1:Ncoh];
burst_num = 5; %突发信号个数
burst_err = zeros(100,10);
guard_err = zeros(100,10);
for t = 1:100
for l = 1:10
snr = -1+l;
position = zeros(1,burst_num);
position(1) = 10+randperm(50,1);%返回1个[1,50]的数
for i=2:burst_num
position(i) = position(i-1)+30+randperm(50,1);%出现信号的起始位置
end
burst_code = zeros(burst_num,burst_length);
burst_code_sampled = zeros(burst_num,burst_length*fs/code_fre);
for i = 1:burst_num
burst_code(i,:) = -2*randi([0,1],[1,burst_length])+1;
sampled = repmat(burst_code(i,:),fs/code_fre,1);%将burst_code(i,:)复制
burst_code_sampled(i,:) = reshape(sampled,1,burst_length*fs/code_fre);
end
noise = noise_std*rand(1,length_all*fs/code_fre);%噪声
carrier = cos(2*pi*(code_fre)*dot_length*sample_t);%初始载波信号
err = zeros(1,13);
signal_amplitude = sqrt(10^(snr/10)*(noise_std^2*2));%根据信噪比扩大幅值
signal_burst = zeros(burst_num,burst_length*fs/code_fre);
for j=1:burst_num
signal_burst(j,:) = signal_amplitude*burst_code_sampled(j,:).* carrier;
end
receive_signal = noise;
num = length_all*fs/code_fre;
wdd = zeros(1,num); %记录正确判决结果
for i=1:burst_num
receive_signal(position(i)*fs/code_fre:position(i)*fs/code_fre+burst_length*fs/code_fre-1) = signal_burst(i,:)+receive_signal(position(i)*fs/code_fre:position(i)*fs/code_fre+burst_length*fs/code_fre-1);
wdd(position(i)*fs/code_fre:position(i)*fs/code_fre+burst_length*fs/code_fre-1) = ones(1,burst_length*fs/code_fre);
end
receive_signal = receive_signal/(max(abs(receive_signal)));%将输入数据归一化
%% ERD算法
slide_length = 20;
ERs = zeros(1,num);
ERe = zeros(1,num);
threshold = abs((1+3*10^(snr/10))*0.5);
for j = slide_length+1:num-slide_length+1
w1_pos = j;
w2_pos = j-slide_length;
w1 = sum(abs(receive_signal(w1_pos:w1_pos+slide_length-1)).^2);
w2 = sum(abs(receive_signal(w2_pos:w2_pos+slide_length-1)).^2);
ERs(j) = w1/w2;
ERe(j) = w2/w1;
end
ERs_threshold = ERs;
ERe_threshold = ERe;
for i = 1:num
if ERs(i)<threshold
ERs_threshold(i) = 0;
end
if ERe(i)<threshold
ERe_threshold(i) = 0;
end
end
ERs_new = zeros(1,num);
ERe_new = zeros(1,num);
%% 检测输出 窗口内取 ERs和ERe的最大值
for i = slide_length+1:num+1
[ERs_max,ERs_pos] = max(ERs_threshold(i-slide_length:i-1));
ERs_threshold(i-slide_length:i-1)=0;
ERs_threshold(i-slide_length+ERs_pos-1)=ERs_max;
[ERe_max,ERe_pos] = max(ERe_threshold(i-slide_length:i-1));
ERe_threshold(i-slide_length:i-1)=0;
ERe_threshold(i-slide_length+ERe_pos-1)=ERe_max;
end
ERs_new = ERs_threshold>0;
ERe_new = ERe_threshold>0;
ERs_index = find(ERs_new == 1);
ERe_index = find(ERe_new == 1);
wd1 = zeros(1,num);
burst_data = [];
for i = 1:length(ERe_index)
k = length(ERs_index);
for j=1: k
if ERs_index(j)<ERe_index(i)
burst_data = [burst_data,ERs_index(j)];
end
end
if length(burst_data)~=0
[burst_min,burst_start] = min(ERe_index(i)-burst_data);
wd1(burst_data(burst_start):ERe_index(i))=1; % 有信号的地方 标1
ERs_index(find(ERs_index==burst_data(burst_start)))=[];% 删除
burst_data = [];
end
end
%% 增加滑动窗口长度
slide_length = 100;
num = length_all*fs/code_fre;
ERs = zeros(1,num);
ERe = zeros(1,num);
threshold = abs((1+3*10^(snr/10))*0.5);
for j = slide_length+1:num-slide_length+1
w1_pos = j;
w2_pos = j-slide_length;
w1 = sum(abs(receive_signal(w1_pos:w1_pos+slide_length-1)).^2);
w2 = sum(abs(receive_signal(w2_pos:w2_pos+slide_length-1)).^2);
ERs(j) = w1/w2;
ERe(j) = w2/w1;
end
ERs_threshold = ERs;
ERe_threshold = ERe;
for i = 1:num
if ERs(i)<threshold
ERs_threshold(i) = 0;
end
if ERe(i)<threshold
ERe_threshold(i) = 0;
end
end
ERs_new = zeros(1,num);
ERe_new = zeros(1,num);
%% 检测输出
for i = slide_length+1:num+1
[ERs_max,ERs_pos] = max(ERs_threshold(i-slide_length:i-1));
ERs_threshold(i-slide_length:i-1)=0;
ERs_threshold(i-slide_length+ERs_pos-1)=ERs_max;
[ERe_max,ERe_pos] = max(ERe_threshold(i-slide_length:i-1));
ERe_threshold(i-slide_length:i-1)=0;
ERe_threshold(i-slide_length+ERe_pos-1)=ERe_max;
end
ERs_new = ERs_threshold>0;
ERe_new = ERe_threshold>0;
ERs_index = find(ERs_new == 1);
ERe_index = find(ERe_new == 1);
wd2 = zeros(1,num);
burst_data = [];
for i = 1:length(ERe_index)
k = length(ERs_index);
for j=1: k
if ERs_index(j)<ERe_index(i)
burst_data = [burst_data,ERs_index(j)];
end
end
if length(burst_data)~=0
[burst_min,burst_start] = min(ERe_index(i)-burst_data);
wd2(burst_data(burst_start):ERe_index(i))=1;
ERs_index(find(ERs_index==burst_data(burst_start)))=[];
burst_data = [];
end
end
wd = wd1.*wd2; % 综合判决，就是都为1时，即为窗口信号
err(1,l) = sum(wd~=wdd);
burst_err(t,l) = (sum(wdd)-sum(wd&wdd))/sum(wdd);
guard_err(t,l) = (sum(~wdd)- sum(~wd&~wdd))/sum(~wdd);
end
end
X = 0:9;
%Y = mean(burst_err);
plot(X,mean(burst_err),'-O');
hold on;
plot(X,mean(guard_err),'-*')
legend('错误率','虚警率');
title('ERD算法的检测错误率和虚警率');
xlabel('SNR(dB)');
ylabel('概率p');
⛄ 运行结果
⛄ 参考文献
[1]赵丽华, 王鹏宇. 基于Matlab的非时域端点检测算法的实现与分析[J]. 科学技术与工程, 2010(35):4.
