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monsterskyy
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2021-4-26 09:58
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% kalman filtering
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load ini
ti
al_track s; % y:initial data,s:data with noise
/ y$ p2 |8 i/ o( B& ] a* Z
T=0.1;
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% yp denotes the sample value of position
4 j" N8 e/ }3 P" A* m& C
% yv denotes the sample value of velocity
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% Y=[yp(n);yv(n)];
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% error deviation caused by the random acceleration
% B- a2 {, b( p: ?3 n
% known data
% k) f" J! h! R4 a& B0 W6 b
Y=zeros(2,200);
2 Z/ E) q, R$ [+ E# ~; V
Y0=[0;1];
$ E( A4 C: b# n4 P3 ? `& H
Y(:,1)=Y0;
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A=[1 T
- w+ T8 [, @- [8 a6 j9 V
0 1];
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B=[1/2*(T)^2 T]';
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H=[1 0];
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C0=[0 0
+ Z u7 c- M+ m! `# D
0 1];
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C=[C0 zeros(2,2*199)];
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Q=(0.25)^2;
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R=(0.25)^2;
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% kalman algorithm ieration
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for n=1:200
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i=(n-1)*2+1;
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K=C(:,i:i+1)*H'*inv(H*C(:,i:i+1)*H'+R);
6 Q, U8 D0 h& ]
Y(:,n)=Y(:,n)+K*(s(:,n)-H*Y(:,n));
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Y(:,n+1)=A*Y(:,n);
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C(:,i:i+1)=(eye(2,2)-K*H)*C(:,i:i+1);
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C(:,i+2:i+3)=A*C(:,i:i+1)*A'+B*Q*B';
% R9 T+ M: J- X( F2 T
end
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% the diagram of position after filtering
* z2 P! }( }! V* [
t=0:0.1:20;
+ ? p) w0 [8 {( [0 a5 B) \
figure(2);
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yp=Y(1,£º);
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plot(t,yp,'r.-');
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axis([0 20 0 20]);
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xlabel('time');
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ylabel('yp position');
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title('the track after kalman filtering');
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hold on;
: ^$ u% n; C, G
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% the diagram of velocity after filtering
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figure(3);
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yv=Y(2,£º);
+ {; l! T7 K9 B6 t
plot(t,yv,'k.-');
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xlabel('time');
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ylabel('yv velocity');
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title('the velocity caused by random acceleration');
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×÷Õß:
nevadaooo
ʱ¼ä:
2021-4-26 11:19
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