粒子滤波算法 matlab

%===== PROBLEM DEFINITION 1 （Required Parameters） =========================
WorkName=‘Battery‘;                    % work results are saved by WorkName
TimeUnit=‘weeks‘;                                          % time unit name
dt=5;                                          % time interval （five weeks）
measuData=[1.0000  0.9351  0.8512  0.9028  0.7754  0.7114  0.6830  ...
0.6147  0.5628  0.7090]‘;  % measured data at every time intervals （k1 x 1）
thres=0.3;                                     % threshold - critical value
ParamName=[‘x‘; ‘b‘; ‘s‘];         % model parameters‘ name to be estimated
initDisPar=[0.9 1.1; 0 0.05; 0.01 0.1];               
                    % probability parameters of initial distribution p x q
                 % （p: num. of unknown param q: num. of probability param）
n=5e3;                                                % number of particles
signiLevel=5;                        % significance level for C.I. and P.I.
%==========================================================================
% % % PROGNOSTICS using PARTICLE FILTER
p=size（ParamName1）;
for j=1:p;                                          %% Initial Distribution
param（j:）=unifrnd（initDisPar（j1）initDisPar（j2）1n）; 
ParamResul（j:）=[ParamName（j:） ‘Resul‘]; 
eval（[ParamResul（j:） ‘=param（j:）;‘]）; 
end;                                                                       
k1=length（measuData）; k=1;                   %% Update Process or Prognosis
if measuData（end）-measuData（1）<0; cofec=-1; else cofec =1; end
while min（eval（[ParamResul（1:） ‘（k:）‘]）*cofec）        % step1. prediction （prior）                                        
        paramPredi=param;                                                  
        for j=1:p