卡尔曼滤波用simulink实现_有3个例子_能运行！.zip

%% Learning the Kalman Filter in Simulink Examples % Examples to run the Simulink model kalmanfilter. % By Yi Cao at Cranfield University on 25 January 2008 %% Example 1, Automobile Voltimeter % Modified from % <http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=5377&objectType=file Michael C. Kleder's "Learning the Kalman Filter"> % % Define the system as a constant of 12 volts: % % $$x_{k+1} = 0*x_k + 12 + w, w \sim N(0,2^2)$$ % % But the Kalman filter uses different model. % % $$x_{k+1} = x_{k} + w, x_0=12, w \sim N(0,2^2)$$ % % This example shows the Kalman filter has certain robustness to model % uncertainty. A = 0; B = 1; % this is original definition x(k+1) = 12 + w A1 = 1; B1 = 0; % this is original definition for Kalman filter % Define a process noise (stdev) of 2 volts as the car operates: Q = 2^2; % variance, hence stdev^2 Q1 = Q; % Define the voltimeter to measure the voltage itself: % y(k+1) = x(k+1) + v, v ~ N(0,2^2) C = 1; D = 0; C1 = C; D1 = D; % Define a measurement error (stdev) of 2 volts: R = 2^2; R1 = R; % Define the system input (control) functions: u = 12; % for the constant % Initial state, 12 volts x0 = 12; x1 = x0; %for Kalman filter % Initial covariance as C*R*C' P1 = 2^2; % Simulation time span tspan = [0 100]; [t,x,y] = sim('kalmanfilter',tspan,[],[0 u]); figure hold on grid on % plot measurement data: hz=plot(t,y(:,2),'r.','Linewidth',2); % plot a-posteriori state estimates: hk=plot(t,y(:,3),'b-','Linewidth',2); % plot true state ht=plot(t,y(:,1),'g-','Linewidth',2); legend([hz hk ht],'observations','Kalman output','true voltage',0) title('Automobile Voltimeter Example') hold off %% Example 2, Predict the position and velocity of a moving train, % Modified from <http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=13479&objectType=file "An Intuitive Introduction to Kalman Filter"> by Alex Blekhman. % % The train is initially located at the point x = 0 and moves % along the X axis with velocity varying around a constant speed 10m/sec. % The motion of the train can be described by a set of differential % equations: % % $$ \dot{x}_1 = x_2, x_1(0)=0$$ % % $$ \dot{x}_2 = w, x_2(0)=10, w \sim N(0,0.3^2)$$ % % where x_1 is the position and x_2 is the velocity, % w the process noise due to road conditions, wind etc. % % Problem: using the position measurement to estimate actual velocity. % % Approach: We measure (sample) the position of the train every dt = 0.1 % seconds. But, because of imperfect apparature, weather etc., our % measurements are noisy, so the instantaneous velocity, derived from 2 % consecutive position measurements (remember, we measure only position) is % innacurate. We will use Kalman filter as we need an accurate and smooth % estimate for the velocity in order to predict train's position in the % future. % % Model: % The state space equation after discretization with sampling time dt % % $$x_{k+1} = \Phi x_k + w_k, \Phi = e^{A\Delta t}, A=[0\,\, 1; 0\,\, 0], w_k \sim N(0,Q), Q =[0\,\, 0; 0\,\, 1]$$ % % The measurment model is % % $$y_{k+1} = [1 \,\, 0] x_{k+1} + v_k, v_k \sim N(0,1)$$ % dt = 0.1; A = expm([0 1;0 0]*dt); B = [0;0]; Q = diag([0;1]); C = [1 0]; D = 0; R = 1; u = 0; x0 = [0;10]; % For Kalman filter, we use the same model A1 = A; B1 = B; C1 = C; D1 = D; Q1 = Q; R1 = R; x1 = [0;5]; %but with different initial state estimate P1 = eye(2); % Run the simulation for 100 smaples tspan = [0 200]; [t,x,y1,y2,y3,y4] = sim('kalmanfilter',tspan,[],[0 u]); Xtrue = y1(:,1); %actual position Vtrue = y1(:,2); %actiual velocity z = y2; %measured position X = y3; %Kalman filter output t = t*dt; % actual time %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Position analysis %%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% figure; subplot(211) plot(t,Xtrue,'g',t,z,'c',t,X(:,1),'m','linewidth',2); title('Position estimation results'); xlabel('Time (s)'); ylabel('Position (m)'); legend('True position','Measurements','Kalman estimated displacement','Location','NorthWest'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Velocity analysis %%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % The instantaneous velocity as derived from 2 consecutive position % measurements InstantV = [10;diff(z)/dt]; % The instantaneous velocity as derived from running average with a window % of 5 samples from instantaneous velocity WindowSize = 5; InstantVAverage = filter(ones(1,WindowSize)/WindowSize,1,InstantV); % figure; subplot(212) plot(t,InstantV,'g',t,InstantVAverage,'c',t,Vtrue,'m',t,X(:,2),'k','linewidth',2); title('Velocity estimation results'); xlabel('Time (s)'); ylabel('Velocity (m/s)'); legend('Estimated velocity by raw consecutive samples','Estimated velocity by running average','True velocity','Estimated velocity by Kalman filter','Location','NorthWest'); set(gcf,'Position', [100 100 600 800]); %% Example 3: A 2-input 2-output 4-state system dt = 0.1; A = [0.8110 -0.0348 0.0499 0.3313 0.0038 0.9412 0.0184 0.0399 0.1094 0.0094 0.6319 0.1080 -0.3186 -0.0254 -0.1446 0.8391]; B = [-0.0130 0.0024 -0.0011 0.0100 -0.0781 0.0009 0.0092 0.0138]; C = [0.1685 -0.9595 -0.0755 -0.3771 0.6664 0.0835 0.6260 0.6609]; D = [0 0;0 0]; % process noise variance Q=diag([0.5^2 0.2^2 0.3^2 0.5^2]); % measurment noise variance R=eye(2); % random initial state x0 = randn(4,1); % Kalman filter set up % The same model A1 =A; B1 = B; C1 = C; D1 = D; Q1 = Q; R1 = R; % However, zeros initial state x1 = zeros(4,1); % Initial state covariance P1 = 10*eye(4); % Simulation set up % time span 100 samples tspan = [0 1000]; % random input change every 100 samples u = [(0:100:1000)' randn(11,2)]; % simulation [t,x,y1,y2,y3,y4]=sim('kalmanfilter',tspan,[],u); % plot results % Display results t = t*dt; figure set(gcf,'Position',[100 100 600 800]) for k=1:4 subplot(4,1,k) plot(t,y1(:,k),'b',t,y3(:,k),'r','linewidth',2); legend('Actual state','Estimated state','Location','best') title(sprintf('state %i',k)) end xlabel('time, s')