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    文件類型: .m
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    發布日期: 2021-05-23
  • 語言: Matlab
  • 標簽: matlab??雷達追蹤??

資源簡介

基于卡爾曼濾波算法的雷達追蹤算法,采用matlab仿真實現

資源截圖

代碼片段和文件信息

%
%?Example?5.8
%
close?all;
clear?all;
clf;
disp(‘Covariance?analysis?of?radar?tracking?problem‘);
disp(‘given?as?Example?5.8?in‘);
disp(‘M.?S.?Grewal?and?A.?P.?Andrews‘);
disp(‘Kalman?Filtering:?Theory?and?Practice?Using?MATLAB‘);
disp(‘4th?Edition?Wiley?2014.‘);
disp(‘?‘);
disp(‘Plots?histories?of?six?mean?squared?state‘);
disp(‘uncertainties?and?six?magnitudes?of?Kalman?gains‘);
disp(‘for?intersample?intervals?of?5?10?and?15?seconds.‘);
disp(‘?‘);
disp(‘Six?state?variables:‘);
disp(‘??1.?Range?to?object?being?tracked.‘);
disp(‘??2.?Range?rate?of?object?being?tracked.‘);
disp(‘??3.?object?range?maneuvering?noise?(pseudo?state).‘);
disp(‘??4.?Bearing?to?object?being?tracked.‘);
disp(‘??5.?Bearing?rate?of?object?being?tracked.‘);
disp(‘??6.?object?bearing?maneuvering?noise?(pseudo?state).‘);
disp(‘Pseudo?states?are?used?for?modeling?correlated?noise.‘);
%
sigma1sq?=?(103/3)^2;
sigma2sq?=?1.3E-8;
sigmarsq?=?(1000)^2;
sigmatsq?=?(.017)^2;
rho??????=?0.5;
%
%?State?Transition?matrix?(the?part?not?depending?on?T)
%
Phi??????=?eye(6);
Phi(23)?=?1;
Phi(56)?=?1;
Phi(33)?=?rho;
Phi(66)?=?rho;
Q????????=?zeros(6);
Q(33)???=?sigma1sq;
Q(66)???=?sigma2sq;
R????????=?zeros(2);
R(11)???=?sigmarsq;
R(22)???=?sigmatsq;
H????????=?zeros(26);
H(11)???=?1;
H(24)???=?1;
%
%?arrays?for?saving?data?to?be?plotted
%
t????????=?zeros(332);?%?time?(for?3?plots)
rcov?????=?zeros(332);?%?Range?covariance
rrcov????=?zeros(332);?%?Range?Rate?covariance
bcov?????=?zeros(332);?%?Bearing?covariance
brcov????=?zeros(332);?%?Bearing?Rate?covariance
rrncov???=?zeros(332);?%?Range?Rate?Noise?covariance
brncov???=?zeros(332);?%?Bearing?Rate?Noise?covariance
rkg??????=?zeros(332);?%?Range?Kalman?gain
rrkg?????=?zeros(332);?%?Range?Rate?Kalman?gain
bkg??????=?zeros(332);?%?Bearing?Kalman?gain
brkg?????=?zeros(332);?%?Bearing?Rate?Kalman?gain
rrnkg????=?zeros(332);?%?Range?Rate?Noise?Kalman?gain
brnkg????=?zeros(332);?%?Bearing?Rate?Noise?Kalman?gain
N=0;
???for?T?=?5:5:15
???N=N+1;
???disp([‘Simulating?tracking?at?‘num2str(T)‘?second?intervals.‘]);
???Phi(12)?=?T;
???Phi(45)?=?T;
???P????????=?zeros(6);
???P(11)???=?sigmarsq;
???P(12)???=?sigmarsq/T;
???P(21)???=?P(12);
???P(22)???=?2*sigmarsq/T^2?+?sigma1sq;
???P(33)???=?sigma1sq;
???P(44)???=?sigmatsq;
???P(45)???=?sigmatsq/T;
???P(54)???=?P(45);
???P(55)???=?2*sigmatsq/T^2?+?sigma2sq;
???P(66)???=?sigma2sq;
??????for?cycle=0:15
%
%?Save?a?priori?values
%
??????prior??????=?2*cycle+1;
??????t(Nprior)?=?T*cycle;
??????K??????????=?P*H‘/(H*P*H‘+R);
??????rcov(Nprior)???=?P(11);?%?Range?covariance
??????rrcov(Nprior)??=?P(22);?%?Range?Rate?covariance
??????bcov(Nprior)???=?P(44);?%?Bearing?covariance
??????brcov(Nprior)??=?P(55);?%?Bearing?Rate?covariance
??????rrncov(Nprior)?=?P(33);?%?Range?Rate?Noise?covariance
??????brncov(Nprior)?=?P(66);?%?Bearing?Rate?Noise?covariance
??????rkg

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