matlab 峰值提取,Matlab2019b信号峰值检测与提取

在信号处理的时候，有的时候希望确定信号峰值的大小以及位置，并最好对信号进行分离，可以更好地进行后续处理。本人使用了集中不同的思路和方式来进行位置确定和提取，仅供参考。

注意： 部分程序仅兼容Matlab2016b以后版本

目标

提取和检测如下图类似信号的峰值及位置

方法1

调用的findpeaks函数

参考程序，通过设定合适的峰值参数，可以输出相应的峰值

%% 找到数据中的峰值

clc;

clear;

x = linspace(0,1,1000);

Pos = [1 2 3 5 7 8]/10;

Hgt = [3 4 4 2 2 3];

Wdt = [2 6 3 3 4 6]/100;

for n = 1:length(Pos)

Gauss(n,:) = Hgt(n)*exp(-((x - Pos(n))/Wdt(n)).^2);

end

Data = sum(Gauss);%产生假设信号

% NPeaks表示返回值中最大的峰值个数

[pks,locs,w,p] = findpeaks(Data,x,...pks为输出峰值，locs为峰值位置

'NPeaks',1,...输出的最大峰值个数

'SortStr','ascend',...输出峰值是否进行排序，升序或降序或不进行排序

'Threshold',0,...峰值之间的最小差值阈值，如数组为[]则未找到符合参数的阈值

'WidthReference','halfprom',...

...'MiniPeakWidth','0',...最小的峰值宽度

'MaxPeakWidth',Inf,...最大的峰值宽度

'Annotate','peaks',...在定义输出后无效

'MinPeakHeight',50);%最小峰值高度

findpeaks(Data,x,'Annotate','extents')

text(locs+.02,pks,num2str((1:numel(pks))'))

方法2

通过求取整个信号的方差和平均值，再使用逻辑与或的方式提取位置和峰值。由于方差即为随机变量和数学期望之间的偏离程度，当极度偏离方差时，信号为突变信号。首先提取位置，通过与方差比较，大于阈值的即为1，小于阈值的即为0，在于信号进行与逻辑运算，即可提取出相对准确的峰值。

clc

clear

Fs=1000;

y=xlsread('Data2','sheet1');

x=y(:,7)';%通道设置

figure('Name','原始混合信号','NumberTitle','off');

plot(x)

jun=mean(x);%求取整个信号的均值

biao=std(x);%求取整个信号的标准差

fang=var(x);%求整个信号的方差

zhong=median(x);%求整个信号的中位数

fai=1;

% x = 0:0.05:50*pi;

% x= signal+rand(1,length(signal))

L=length(x);

T=0.5*(min(x(:))+max(x(:)));

%循环判断与整个信号方差之间的关系

for i=1:L

if(x(i)

方法3

使用VPD方法

%一维峰值检测Matlab实现

clc;

clear

signal = 0:0.05:50*pi;

x = sin(signal);

row_acc=x;

plot(x)

% x = 0:0.05:50*pi;

% x= signal+rand(1,length(signal))

L=length(x);

% Y = fft(x);

% P2 = abs(Y/L);

% P1 = P2(1:L/2+1);% 实信号的功率谱是对称的,只用取一半即可

% P1(2:end-1) = 2*P1(2:end-1);

% f = Fs*(0:(L/2))/L;

%

% plot(f,P1)

% title('原始信号直接进行傅里叶分析')

% grid on

% xlabel('Frequency (Hz)')

% ylabel('Amplitude')

% [c,l] = wavedec(x,2,'db2');

% approx = appcoef(c,l,'db2');

% [cd1,cd2] = detcoef(c,l,[1 2]);

%

% subplot(3,1,1)

% plot(x)

% title('原始信号')

% subplot(3,1,2)

% plot(approx)

% title('应变信号')

% subplot(3,1,3)

% plot(cd3)

% title('冲击信号')

% subplot(4,1,3)

% plot(cd2)

% title('Level 2 Detail Coefficients')

% subplot(4,1,4)

% plot(cd1)

% title('Level 1 Detail Coefficients')

%

% Hd=LowPass_Filter;

% output=filter(Hd,x);

% figure

% plot(output)

%

% blo = fir1(34,0.48,chebwin(35,30));

% outlo = filter(blo,1,x);

%

% 使用峰值检测

% % [pks,locs]=findpeaks(x,'Npeaks',1,'MinPeakHeight',250);

% % jieyue=x(:,[locs-200:L]);

% % figure

% % plot(jieyue)

% % yingbian=x(:,[1:locs-200]);

% % figure

% % plot(yingbian)

% % ylim([-50 300])

% % figure

% % plot(locs,pks)

%

%% VPD方法

%%前三点均值滤波

row_acc = x;

m = length(row_acc);

row_acc1 = linspace(0,0,m);

row_acc1(1) = row_acc(1);

row_acc1(m) = row_acc(m);

for i=2:m-1

row_acc1(i)=(row_acc(i-1) + row_acc(i)+row_acc(i+1))/3;

end

% figure;

% plot(row_acc1);

for i=m-1:-1:2

row_acc(i) = (row_acc1(i-1) + row_acc1(i)+row_acc1(i+1))/3;

end

%%找到局部最小值和局部最大值及其对应的位置，波峰点、波谷点满足：

peaks = linspace(0,0,m);

valleys = linspace(0,0,m);

peakindexs = linspace(0,0,m);

valleyindexs = linspace(0,0,m);

peakindex = 1;

valleyindex = 1;

for i = 2:m-1

if row_acc(i) >row_acc(i-1) && row_acc(i)>=row_acc(i+1)

peaks(peakindex)=row_acc(i);

peakindexs(peakindex)=i;

peakindex = peakindex+1;

end

if row_acc(i) < row_acc(i-1) && row_acc(i)2 && vcount>2

if peakindexs(1) < valleyindexs(1)

peakindex=2;

else

peakindex=1;

end

vindex=1;

end

if peakindex == 2

for i = 1:m-1

peaks(i)=peaks(i+1);

end

pcount = pcount-1;

pindex=1;

end

vpd = linspace(0,0,m);

vpd1 = linspace(0,0,m);

for i=1:pcount

vpd(i) = peaks(i) - valleys(i);

end

dels = linspace(0,0, pcount);

peakindexs1 = linspace(0,0,pcount);

if pcount > 2

lastcount=pcount;

curcount = 1;

while lastcount ~= curcount

lastcount = curcount;

del_count = 0;

for i = 2:pcount-1

if vpd(i) <= 0.7 * (vpd(i-1) + vpd(i)+vpd(i+1)) / 3

dels(i)=1;

end

end

count = 1;

for i = 1:pcount

if dels(i) ~= 1

vpd1(count) = vpd(i);

peakindexs1(count) = peakindexs(i);

count = count+1;

else

del_count = del_count + 1;

dels(i) = 0;

end

end

pcount = pcount - del_count;

for i = 1:pcount

vpd(i) = vpd1(i);

peakindexs(i) = peakindexs1(i);

end

peakindexs(pcount+1) = 0;

vpd(pcount+1) = 0;

indices = linspace(0,0,pcount);

for i = 1:pcount

indices(i) = peakindexs1(i);

end

plot(row_acc,'-o', 'MarkerIndices',indices,'MarkerFaceColor','red','MarkerSize',10);

curcount = pcount;

end

end

% [pks,locs]=findpeaks(row_acc,'Npeaks',1,'MinPeakHeight',20);

% jieyue=row_acc(:,[locs-200:L]);%从触发峰值点左边200处分离

% figure

% plot(jieyue)

% yingbian=x(:,[1:locs-200]);

% figure

% plot(yingbian)

% ylim([-50 300])

%

% % figure

% % plot(locs,pks)

%

Copyright © 2020 by RichardYang. All rights reserved.

仅供参考，严禁转载，感谢。