卡尔曼算法的matlab程序
求卡尔曼算法的matlab预测程序 matlab下面的kalman滤波程序clear N=200; w(1)=0;
w=randn(1,N)
x(1)=0;
a=1;
for k=2:N;
x(k)=a*x(k-1)+w(k-1);
end
V=randn(1,N);
q1=std(V);
Rvv=q1.^2;
q2=std(x);
Rxx=q2.^2;
q3=std(w);
Rww=q3.^2;
c=0.2;
Y=c*x+V;
p(1)=0;
s(1)=0;
for t=2:N;
p1(t)=a.^2*p(t-1)+Rww;
b(t)=c*p1(t)/(c.^2*p1(t)+Rvv);
s(t)=a*s(t-1)+b(t)*(Y(t)-a*c*s(t-1));
p(t)=p1(t)-c*b(t)*p1(t);
end
t=1:N;
plot(t,s,'r',t,Y,'g',t,x,'b');
function = kalman_filter(y, A, C, Q, R, init_x, init_V, varargin)
% Kalman filter.
% = kalman_filter(y, A, C, Q, R, init_x, init_V, ...)
%
% INPUTS:
% y(:,t) - the observation at time t
% A - the system matrix
% C - the observation matrix
% Q - the system covariance
% R - the observation covariance
% init_x - the initial state (column) vector
% init_V - the initial state covariance
%
% OPTIONAL INPUTS (string/value pairs )
% 'model' - model(t)=m means use params from model m at time t
% In this case, all the above matrices take an additional final dimension,
% i.e., A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m).
% However, init_x and init_V are independent of model(1).
% 'u' - u(:,t) the control signal at time t [ [] ]
% 'B' - B(:,:,m) the input regression matrix for model m
%
% OUTPUTS (where X is the hidden state being estimated)
% x(:,t) = E
% V(:,:,t) = Cov
% VV(:,:,t) = Cov t >= 2
% loglik = sum{t=1}^T log P(y(:,t))
%
% If an input signal is specified, we also condition on it:
% e.g., x(:,t) = E
% If a model sequence is specified, we also condition on it:
% e.g., x(:,t) = E
= size(y);
ss = size(A,1); % size of state space
% set default params
model = ones(1,T);
u = [];
B = [];
ndx = [];
args = varargin;
nargs = length(args);
for i=1:2:nargs
switch args
case 'model', model = args{i+1};
case 'u', u = args{i+1};
case 'B', B = args{i+1};
case 'ndx', ndx = args{i+1};
otherwise, error(['unrecognized argument ' args])
end
end
x = zeros(ss, T);
V = zeros(ss, ss, T);
VV = zeros(ss, ss, T);
loglik = 0;
for t=1:T m = model(t);
if t==1 %prevx = init_x(:,m);
%prevV = init_V(:,:,m);
prevx = init_x;
prevV = init_V;
initial = 1;
else prevx = x(:,t-1);
prevV = V(:,:,t-1);
initial = 0;
end
if isempty(u)
= ...
kalman_update(A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m), y(:,t), prevx, prevV, 'initial', initial); else
if isempty(ndx) = ...
kalman_update(A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m), y(:,t), prevx, prevV, ... 'initial', initial, 'u', u(:,t), 'B', B(:,:,m));
else
i = ndx;
% copy over all elements; only some will get updated x(:,t) = prevx;
prevP = inv(prevV);
prevPsmall = prevP(i,i);
prevVsmall = inv(prevPsmall);
= ... kalman_update(A(i,i,m), C(:,i,m), Q(i,i,m), R(:,:,m), y(:,t), prevx(i), prevVsmall, ... 'initial', initial, 'u', u(:,t), 'B', B(i,:,m));
smallP = inv(smallV);
prevP(i,i) = smallP;
V(:,:,t) = inv(prevP);
end
end
loglik = loglik + LL;
end 厚积薄发 发表于 2011-11-28 10:48 static/image/common/back.gif
matlab下面的kalman滤波程序
clear N=200; w(1)=0;
w=randn(1,N)
强大呀，下了 太强大了{:soso_e140:} 太令人。。。。 谢谢。。。。。。。。。。。。。。。。。。。。。。。。。。 {:soso_e176:}{:soso_e163:}
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