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数学中国总编辑
TA的每日心情 | 衰 2016-11-18 10:46 |
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签到天数: 206 天 [LV.7]常住居民III 超级版主
群组: 2011年第一期数学建模 群组: 第一期sas基础实训课堂 群组: 第二届数模基础实训 群组: 2012第二期MCM/ICM优秀 群组: MCM优秀论文解析专题 |
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发表于 2011-11-28 10:48
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matlab下面的kalman滤波程序
" W" \6 N# b }5 P9 Bclear N=200; w(1)=0; ; z4 F9 M0 J+ M$ P- W2 V
w=randn(1,N) . f1 S% W0 c. S/ n- h
x(1)=0;
: x9 R, S% C/ V! J2 I* Ra=1;
* {9 b3 R* R+ k7 T$ ]for k=2:N; ! T) A4 ? A; Z! U$ f8 Y- M
x(k)=a*x(k-1)+w(k-1);
$ D/ w- C- \0 f0 Wend % [- ^2 W' e D# ^1 d: n8 Z M6 F
V=randn(1,N); 1 S# w0 m! M+ n# r P9 w" @* Z7 R+ \
q1=std(V);
" g$ Z+ C6 O1 M' gRvv=q1.^2; ! o% K- G& w8 O* [3 e5 E
q2=std(x);
- d7 @) X# n K! _. eRxx=q2.^2;
" ^2 M* w( d2 B: d) w& wq3=std(w); ) y3 i. K: J! {: c0 Z
Rww=q3.^2; 3 i+ S% S! l' z
c=0.2;
2 |( ~" l/ X TY=c*x+V; ; }: [0 ]9 d9 l* h( k+ G8 b" ]
p(1)=0; ) x; G4 ]9 i# ?3 G$ @4 F/ d
s(1)=0;, \9 I7 |) k% U/ F
for t=2:N;
+ Y" g6 a; W& y- e- Qp1(t)=a.^2*p(t-1)+Rww;
" `5 d6 n O5 C3 F3 A. x& db(t)=c*p1(t)/(c.^2*p1(t)+Rvv); L M9 r3 H+ t' b. u
s(t)=a*s(t-1)+b(t)*(Y(t)-a*c*s(t-1)); $ p5 Y& U+ Y: L* n
p(t)=p1(t)-c*b(t)*p1(t); , |6 S( w+ G; E! M _
end
4 a" c, t T* jt=1:N;
; s1 Q- t6 o- B ]( N7 ^* r4 C9 eplot(t,s,'r',t,Y,'g',t,x,'b');
( b$ W; G5 }) S5 I! u ~* ofunction [x, V, VV, loglik] = kalman_filter(y, A, C, Q, R, init_x, init_V, varargin) 4 B1 F0 d% B5 @# j1 F$ F
% Kalman filter.
]4 [" b. A1 |9 ]9 D& o5 j% [x, V, VV, loglik] = kalman_filter(y, A, C, Q, R, init_x, init_V, ...) ' N/ u1 g4 s5 ~, |
%
2 ~% ]3 m, h5 E& [6 e& Y% INPUTS:
( D& k* E$ L" U T% y(:,t) - the observation at time t
" ?, H) h, i( G4 |% A - the system matrix ' ]6 u- J4 p6 g# p3 U
% C - the observation matrix
: }3 j) g, t! z4 Z0 ?% Q - the system covariance 3 y6 |/ k8 n) f2 a4 q
% R - the observation covariance
: t9 G: V7 Q5 ]% w0 Q% init_x - the initial state (column) vector
- V9 s% n# @5 o: t0 X- w% init_V - the initial state covariance
^% S1 Y* Q' l7 c% ) D h+ s4 L. K. |
% OPTIONAL INPUTS (string/value pairs [default in brackets]) % r: w* \. |7 I4 Z* R- |, m
% 'model' - model(t)=m means use params from model m at time t [ones(1,T) ] & z, t. }0 n2 |5 q6 O
% In this case, all the above matrices take an additional final dimension, 1 g3 I; O3 Q5 }. {$ k9 q
% i.e., A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m). ) E( p; s5 ^9 ?: R4 E/ v1 o
% However, init_x and init_V are independent of model(1). ( v, I' {& |* L
% 'u' - u(:,t) the control signal at time t [ [] ] 4 @5 F, i( ^% f7 L1 y
% 'B' - B(:,:,m) the input regression matrix for model m , [* E& F) j9 _ R8 g
% , e5 Y4 j5 R" O# c
% OUTPUTS (where X is the hidden state being estimated) ! y% Z3 S. {& o% r3 |' o, _/ J
% x(:,t) = E[X(:,t) | y(:,1:t)]
' W( q- n. x7 }7 D# ] _% V(:,:,t) = Cov[X(:,t) | y(:,1:t)] 5 A- W, g! M8 ?4 B6 o5 Y% G; t
% VV(:,:,t) = Cov[X(:,t), X(:,t-1) | y(:,1:t)] t >= 2 * W: a- k* Z9 r/ B* T* y
% loglik = sum{t=1}^T log P(y(:,t)) 5 i. F m$ g8 C; a1 \# G
% 1 U `4 z3 g' |5 i T
% If an input signal is specified, we also condition on it: ; _- S6 s' k, S7 B
% e.g., x(:,t) = E[X(:,t) | y(:,1:t), u(:, 1:t)] & q \! T* O5 _) i
% If a model sequence is specified, we also condition on it: 6 p" A! g% `0 l6 q- R' [
% e.g., x(:,t) = E[X(:,t) | y(:,1:t), u(:, 1:t), m(1:t)] 5 y7 P3 ]7 ?* w$ x z! t; |) a: y
[os T] = size(y); $ d% @) O; H8 i$ ]
ss = size(A,1); % size of state space ; ^ N- P5 i* j+ Y: _4 U
% set default params 1 X8 F, S! M9 k4 p, N5 r' l" {
model = ones(1,T);
! q' U) }0 Y. \3 i0 c/ ju = [];
) O2 U' D* ?! L- q9 _, B: C: M5 xB = []; " J, A7 ?" J6 [+ }1 e2 W k% X
ndx = []; 7 C0 ^3 i0 L9 ~% j/ P) h+ v
args = varargin;
/ r6 E1 b* C. H# s; T) B7 P ynargs = length(args); # `. j! H+ N$ n' }5 E, ]% l
for i=1:2:nargs , a$ V j* I) S5 r; t
switch args # f' i1 `: h$ }6 K ]5 q1 q6 _
case 'model', model = args{i+1};
2 p# h* Y$ Z* N3 u+ s2 U, scase 'u', u = args{i+1};
& i$ o/ U# k( o% M" ]# Hcase 'B', B = args{i+1}; : L, r( @7 L% I8 ]4 ?" Q
case 'ndx', ndx = args{i+1};
( ^7 L" `# E# \* \( R$ Dotherwise, error(['unrecognized argument ' args]) 1 u2 A% b& W: K" ]
end
& D/ ] W1 X5 `7 o( W4 G send
! V1 h1 \9 \# ~: \! I4 vx = zeros(ss, T); & I( m' s4 s3 I! \9 D7 }2 Q
V = zeros(ss, ss, T); + n, W& v& O8 f6 B/ Y9 U/ G9 _
VV = zeros(ss, ss, T); 9 {% w1 R2 @& e+ {1 U
loglik = 0;
8 u. A4 V1 d3 s7 L7 Dfor t=1:T m = model(t); . k1 u: _3 x* |2 H- s5 O: Q
if t==1 %prevx = init_x(:,m); & [% D2 X/ w5 [/ r! C" E% w
%prevV = init_V(:,:,m);
7 I# b4 s6 Q4 E) Y% Sprevx = init_x; 1 o# k$ D, V6 ~6 m l: I' K- a: B% J; i
prevV = init_V; 3 t* @$ {/ d0 g6 s4 B3 e( V0 w1 _
initial = 1; 3 P5 j% F: F8 p8 X
else prevx = x(:,t-1); / t+ }5 t2 b* l4 J5 `' ~' m1 X
prevV = V(:,:,t-1); / [/ C9 {0 [. P2 ]
initial = 0;
6 O6 `! n+ b: J7 U6 j8 k; ~- A4 Tend + y5 s5 m, Y3 d
if isempty(u)
6 `) }$ S* u$ D& Z5 x9 V" |[x(:,t), V(:,:,t), LL, VV(:,:,t)] = ...
- l7 \9 }7 V2 L6 s# o1 O% Z8 `* Ukalman_update(A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m), y(:,t), prevx, prevV, 'initial', initial); else 1 U5 V$ F# a) ]- q, [+ l' p
if isempty(ndx) [x(:,t), V(:,:,t), LL, VV(:,:,t)] = ... ( E( s4 @& s+ u6 |( u2 P
kalman_update(A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m), y(:,t), prevx, prevV, ... 'initial', initial, 'u', u(:,t), 'B', B(:,:,m));
* i) J8 o7 l* f1 W1 r' `; M! V& y Xelse
) ~9 l7 ~0 ^% E1 Z& l* d% si = ndx; " j7 P/ z0 Q* h! a4 |( v
% copy over all elements; only some will get updated x(:,t) = prevx;
, f9 g5 D+ K; f1 f: LprevP = inv(prevV);
1 ?7 v8 d" H, f! y0 g/ VprevPsmall = prevP(i,i);
Z: U# r; i) \% z7 E) k* JprevVsmall = inv(prevPsmall);
* d0 M; E# ]6 ^1 ]9 O' c, ^* V3 n6 F. _[x(i,t), smallV, LL, VV(i,i,t)] = ... 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));
; O* g) S7 z! n, D. AsmallP = inv(smallV);
% N% k7 V) | E5 e7 eprevP(i,i) = smallP;
$ d( S. O& @" @0 ^/ r5 X1 T, ZV(:,:,t) = inv(prevP);
6 W7 P1 j( U: D' k2 s3 send 8 Z3 t3 G" U2 x" h0 ]/ J
end
: \3 G/ h9 o: O1 Sloglik = loglik + LL; ( f ]7 r% ^& m3 o6 b
end |
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