residualr.m

Rao Blackwellised Particle Filtering for Dynamic Conditionally Gaussian Models基于高斯模型的rbpf（粒子滤波器）的mat

function outIndex = residualR(inIndex,q);
% PURPOSE : Performs the resampling stage of the SIR
%           in order(number of samples) steps. It uses Liu's
%           residual resampling algorithm and Niclas' magic line.
% INPUTS  : - inIndex = Input particle indices.
%           - q = Normalised importance ratios.
% OUTPUTS : - outIndex = Resampled indices.
% AUTHORS  : Arnaud Doucet and Nando de Freitas - Thanks for the acknowledgement.
% DATE     : 08-09-98

if nargin < 2, error('Not enough input arguments.'); end

[S,arb] = size(q);  % S = Number of particles.

% RESIDUAL RESAMPLING:
% ====================
N_babies= zeros(1,S);
% first integer part
q_res = S.*q'; %'
N_babies = fix(q_res);
% residual number of particles to sample
N_res=S-sum(N_babies);
if (N_res~=0)
  q_res=(q_res-N_babies)/N_res;
  cumDist= cumsum(q_res);   
  % generate N_res ordered random variables uniformly distributed in [0,1]
  u = fliplr(cumprod(rand(1,N_res).^(1./(N_res:-1:1))));
  j=1;
  for i=1:N_res
    while (u(1,i)>cumDist(1,j))
      j=j+1;
    end
    N_babies(1,j)=N_babies(1,j)+1;
  end;
end;

% COPY RESAMPLED TRAJECTORIES:  
% ============================
index=1;
for i=1:S
  if (N_babies(1,i)>0)
    for j=index:index+N_babies(1,i)-1
      outIndex(j) = inIndex(i);
    end;
  end;   
  index= index+N_babies(1,i);   
end