pf.asv

this particle filter code can be used for target tracking

clear all;
clc;
echo off;

% INITIALISATION AND PARAMETERS:
% ==============================

no_of_runs = 100;            % number of experiments to generate statistical
                            % averages
doPlot = 0;                 % 1 plot online. 0 = only plot at the end.
sigma =  1e-5;              % Variance of the Gaussian measurement noise.
g1 = 3;                     % Paramater of Gamma transition prior.
g2 = 2;                     % Parameter of Gamman transition prior.
                            % Thus mean = 3/2 and var = 3/4.
T = 60;                     % Number of time steps.
N = 200;                     % Number of particles.
resamplingScheme = 1;       % The possible choices are
                            % systematic sampling (2),
                            % residual (1)
                            % and multinomial (3). 
                            % They're all O(N) algorithms. 
                            
%% SETUP BUFFERS TO STORE PERFORMANCE RESULTS
% ==========================================
rmsError_pf   = zeros(1,no_of_runs);
time_pf       = zeros(1,no_of_runs);

%% MAIN LOOP
for j=1:no_of_runs,
  rand('state',sum(100*clock));   % Shuffle the pack!
  randn('state',sum(100*clock));   % Shuffle the pack!
  

% GENERATE THE DATA:
% ==================

x = zeros(T,1);
y = zeros(T,1);
processNoise = zeros(T,1);
measureNoise = zeros(T,1);
x(1) = 1;                         % Initial state.
for t=2:T
  processNoise(t) = gengamma(g1,g2);  
  measureNoise(t) = sqrt(sigma)*randn(1,1);    
  x(t) = feval('ffun',x(t-1),t) +processNoise(t);     % Gamma transition prior.  
  y(t) = feval('hfun',x(t),t) + measureNoise(t);      % Gaussian likelihood.
end;  

% PLOT THE GENERATED DATA:
% ========================
figure(1)
clf;
plot(1:T,x,'r',1:T,y,'b');
ylabel('Data','fontsize',15);
xlabel('Time','fontsize',15);
legend('States (x)','Observations(y)');