粒子滤波算法MATLAB实现及详解

%========== PROBLEM DEFINIATION;PARAMETER DEFINITION ============
WorkName='PFMFCS';
TimeUnit='hours';
dt=1;
measuData=y;
% measured data at every time interval
thres=3.2106;
ParamName=['x';'b';'s'];
initDisPar=[3.227 3.427;0 0.0002;0.001 0.005];
% Probability Parametes of initial distribution
% (p : num.of unknown param; q : num.probability of param)
n=2e3;  % number of particles
signiLevel=5; % significance level for C.I. and P.I.
%=================================================================
% % %============ PROGNOSTICS using Particle Filter ==============
p=size(ParamName,1);
for j=1:p;
    param(j,:)=unifrnd(initDisPar(j,1),initDisPar(j,2),1,n);
    ParamResul(j,:)=[ParamName(j,:) 'Resul'];
    eval([ParamResul(j,:) '=param(j,:);']);
end;
k1=length(measuData);k=1;
if measuData(end)-measuData(1)<0;cofec=-1; else cofec=1; end
while min(eval([ParamResul(1,:) '(k,:)'])*cofec)<thres*cofec; k=k+1;
    % step1. prediction (prior)
    paramPredi=param;
    for j=1:p;eval([ParamName(j,:) '=paramPredi(j,:);']);end
    paramPredi(1,:)=...%=== PROBLEM DEFINIATION: MODEL DEFINITION ===
                    -b*dt+x;
    % ===============================================================