I have estimated my data through a fucnction that contains Kalmen filter algorthim and it estimated the data well and I have tried to use forecast function and i found those errors
Error: File: forecast.m Line: 23 Column: 33
Character vector is not terminated properly.
my data
function h = GetSonar()
persistent sonarAlt
persistent k firstRun
if isempty(firstRun)
load SonarAlt
k = 1;
firstRun = 1;
end
h = sonarAlt(k);
k = k + 1;
the full code is for kalman
function [pos vel Px] = DvKalman(z)
persistent A H Q R
persistent x P
persistent firstRun
if isempty(firstRun)
firstRun = 1;
dt = 1;
A = [ 1 dt ;
0 1 ];
H = [1 0];
Q = [ 1 0 ;
0 3 ];
R = 10;
x = [ 0 20 ]';
P = 5*eye(2);
end
xp = A*x;
Pp = A*P*A' + Q;
K = Pp*H'*inv(H*Pp*H' + R);
x = xp + K*(z - H*xp);
P = Pp - K*H*Pp;
pos = x(1);
vel = x(2);
Px = P ;
clear all
Nsamples = 1500;
Xsaved = zeros(Nsamples, 2);
Zsaved = zeros(Nsamples, 1);
Psaved = zeros(Nsamples, 2);
for k=1:Nsamples
z = GetSonar();
[pos vel P] = DvKalman1(z);
Xsaved(k, :) = [pos vel];
Zsaved(k) = z;
Psaved(k,1:2) = [P(1,1) P(2,2)];
end
dt = 0.02;
t = 0:dt:Nsamples*dt-dt;
fig = figure ;
left_color = [0 0 0];
right_color= [0 0 1];
set(fig,'defaultAxesColorOrder',[left_color; right_color]);
hold on
yyaxis left
plot(t, Zsaved(:), 'r.','markersize',20)
plot(t, Xsaved(:, 1),'k-','linewidth',4)
ylabel('position (m)')
yyaxis right
plot(t, Xsaved(:, 2),'b-','linewidth',4)
xlabel('time (s)'); ylabel('velocity (m/s)')
title('Position and Velocity from Noisy Sonar Measurements')
legend('Position, Raw Measurements','Position from Kalman Filter',...
'Velocity from Kalman Filter')
set(gca,'fontsize',18); grid on
set(gcf, 'Position', [10, 100, 1200, 500])
figure
plot(t,Psaved(:,1),'k')
figure
plot(t,Psaved(:,2),'b')
figure
t=0:dt:(Nsamples-1)*dt-dt;
vel_est = diff(Zsaved)/dt;
plot(t,vel_est,'r.','markersize',20)
hold on
vel_est2 = diff(Xsaved(:,1))/dt;
plot(t,vel_est2,'k-','linewidth',2)
plot(t,Xsaved(1:(end-1),2),'b','linewidth',4)
grid
xlabel('time (s)'); ylabel('velocity (m/s)')
legend('Finite Difference of Measurement',...
'Finite Difference of Estimated Position',...
'Kalman Filter Velocity Estimate');
set(gca,'fontsize',18); grid on
and forecast fuction
function YP = forecast(model,data,K, Init)
samples
[N, ny] = size(data);
Mss = idss(model);
ord = size(pvget(Mss,'A'),1);
if ord>N
error('Forecast:TooFewSamples','The data should contain at least %d
samples.',ord)
end
if nargin<4, Init = 'e'; end
yp = zeros(K,ny);
mp = getPredictor(Mss);
[Ap,Bp,Cp] = ssdata(mp);
if Init=='z'
xt = ltitr(Ap, Bp, data.y);
x0 = xt(end,:);
else
[A1,B1,C1,D1,K1] = ssdata(Mss);
x00 =
x0est(data.y,A1,B1,C1,D1,K1,size(C1,1),size(B1,2),250e3,eye(size(C1,1)));
x0 = ltitr(Ap,Bp,data.y,x00); x0 = x0(end,:);
end
u = [data.y(end,:); zeros(1,ny)];
for ct = 1:K
xt = ltitr(Ap, Bp, u, x0);
x0 = xt(end,:);
yp(ct,:) = (Cp*x0.').';
u = [yp(ct,:); zeros(1,ny)];
end
YP = data; YP.y = yp; YP.u = []; YP.Name = '';
YP.UserData = []; YP.Notes = '';
YP.Tstart = data.Ts*(N+1);
function mp = getPredictor(sysd)
[A,B,C,D,K] = ssdata(sysd);
Ts = sysd.Ts;
Ny = size(D,1);
mp = idss(A-K*C, [K B-K*D], C, [zeros(Ny), D], zeros(size(A,1),Ny), 'Ts',
Ts);
mp.InputDelay = [zeros(Ny,1); sysd.InputDelay];
