浅谈压缩感知（七）：常见测量矩阵的MATLAB实现

1、随机高斯测量矩阵

function [ Phi ] = GaussMtx( M,N )
%GaussMtx Summary of this function goes here
%   Generate Bernoulli matrix
%   M -- RowNumber
%   N -- ColumnNumber
%   Phi -- The Gauss matrix

%% Generate Gauss matrix
    Phi = randn(M,N);
    %Phi = Phi/sqrt(M);
end

2、随机贝努力测量矩阵

function [ Phi ] = BernoulliMtx( M,N )
%BernoulliMtx Summary of this function goes here
%   Generate Bernoulli matrix
%   M -- RowNumber
%   N -- ColumnNumber
%   Phi -- The Bernoulli matrix

%% ()Generate Bernoulli matrix(The first kind)
% --P=0.5   ---P=0.5
    Phi = randi([,],M,N);%If your MATLAB version is too low,please use randint instead
    Phi(Phi==) = -;
    %Phi = Phi/sqrt(M);
% %% ()Generate Bernoulli matrix(The second kind)
% % --P=/   ---P=/  --/
%     Phi = randi([-,],M,N);%If your MATLAB version is too low,please use randint instead
%     Phi(Phi==) = ;%P=/
%     Phi(Phi==) = ;%P=/
%     Phi(Phi==) = ;%P=/
%     %Phi = Phi*sqrt(/M);
end

3、部分哈达玛测量矩阵

function [ Phi ] = PartHadamardMtx( M,N )
%PartHadamardMtx Summary of this function goes here
%   Generate part Hadamard matrix
%   M -- RowNumber
%   N -- ColumnNumber
%   Phi -- The part Hadamard matrix

%% parameter initialization
%Because the MATLAB function hadamard handles only the cases where n, n/,
%or n/ is a power of
    L_t = max(M,N);%Maybe L_t does not meet requirement of function hadamard
    L_t1 = ( - mod(L_t,)) + L_t;
    L_t2 = ( - mod(L_t,)) + L_t;
    L_t3 = ^ceil(log2(L_t));
    L = min([L_t1,L_t2,L_t3]);%Get the minimum L
%% Generate part Hadamard matrix
    Phi = [];
    Phi_t = hadamard(L);
    RowIndex = randperm(L);
    Phi_t_r = Phi_t(RowIndex(:M),:);
    ColIndex = randperm(L);
    Phi = Phi_t_r(:,ColIndex(:N));
end

4、部分傅里叶测量矩阵

function [ Phi ] = PartFourierMtx( M,N )
%PartFourierMtx Summary of this function goes here
%   Generate part Fourier matrix
%   M -- RowNumber
%   N -- ColumnNumber
%   Phi -- The part Fourier matrix

%% Generate part Fourier matrix
    Phi_t = fft(eye(N,N))/sqrt(N);%Fourier matrix
    RowIndex = randperm(N);
    Phi = Phi_t(RowIndex(:M),:);%Select M rows randomly
    %normalization
    for ii = :N
        Phi(:,ii) = Phi(:,ii)/norm(Phi(:,ii));
    end
end

5、稀疏随机测量矩阵

function [ Phi ] = SparseRandomMtx( M,N,d )
%SparseRandomMtx Summary of this function goes here
%   Generate SparseRandom matrix
%   M -- RowNumber
%   N -- ColumnNumber
%   d -- The number of '' in every column,d<M
%   Phi -- The SparseRandom matrix

%% Generate SparseRandom matrix
    Phi = zeros(M,N);
    for ii = :N
        ColIdx = randperm(M);
        Phi(ColIdx(:d),ii) = ;
    end
end

6、托普利兹测量矩阵与循环测量矩阵

function [ Phi ] = ToeplitzMtx( M,N )
%ToeplitzMtx Summary of this function goes here
%   Generate Toeplitz matrix
%   M -- RowNumber
%   N -- ColumnNumber
%   Phi -- The Toeplitz matrix

%% Generate a random vector
%     %()Gauss
%     u = randn(,*N-);
    %()Bernoulli
    u = randi([,],,*N-);
    u(u==) = -;
%% Generate Toeplitz matrix
    Phi_t = toeplitz(u(N:end),fliplr(u(:N)));
    Phi = Phi_t(:M,:);
end

function [ Phi ] = CirculantMtx( M,N )
%CirculantMtx Summary of this function goes here
%   Generate Circulant matrix
%   M -- RowNumber
%   N -- ColumnNumber
%   Phi -- The Circulant matrix

%% Generate a random vector
%     %()Gauss
%     u = randn(,N);
    %()Bernoulli
    u = randi([,],,N);
    u(u==) = -;
%% Generate Circulant matrix
    Phi_t = toeplitz(circshift(u,[,]),fliplr(u(:N)));
    Phi = Phi_t(:M,:);
end

参考来源：http://blog.csdn.net/jbb0523/article/details/44700735