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SampleJAH.m
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SampleJAH.m
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function [ jah_ppsl ] = SampleJAH( t, L, Set, Observs, FLAG_ns )
%SAMPLEJAH Probabilistically generates a joint association hypothesis for a
% single frame.
global Par;
jah_ppsl = zeros(Set.N, L);
R = Par.R;
Q = Par.Q;
invR = inv(Par.R);
invQ = inv(Par.Q);
A = Par.A;
% Generate a random permutation order
order = randperm(Set.N);
% List of used associations
used_ass = cell(L,1);
% Loop through targets
for j = order
d = inf;
% Re-propose point
% z = unidrnd(L);
z = L;
% % Get state
% x = Set.tracks{j}.GetState(t-L);
%%%%%%%%%%%%%%%%%%%%%%%
% How long should the KF run for?
last = t-z;
first = max(t-(z+L)+1, Set.tracks{j}.birth+1);
num = last - first + 1;
% if (num==0)%||(rand<0.5)
x = Set.tracks{j}.GetState(t-z);
% else
%
% % Draw up a list of associated hypotheses
% obs = ListAssocObservs(last, num, Set.tracks{j}, Observs);
%
% % Run a Kalman filter the target
% [KFMean, KFVar] = KalmanFilter(obs, Set.tracks{j}.GetState(first-1), Par.KFInitVar*eye(4));
%
% x = KFMean{end};
%
% end
%%%%%%%%%%%%%%%%%%%%%%%%%%
% Loop through time
for tt = t:-1:t-z+1
k = tt - (t-z);
N = Observs(tt).N;
% Calculate deterministic prediction and jacobian
p_x = (A^k) * x;
[p_bng, p_rng] = Cart2Pol(p_x(1:2));
p_rngsq = p_rng^2;
J = [-p_x(2)/p_rngsq, p_x(1)/p_rngsq, 0, 0; p_x(1)/p_rng, p_x(2)/p_rng, 0, 0];
% Calculate the mean and variance
if d>L
mu = [p_bng; p_rng];
S = R + k*J*(k*Q)*J';
else
nV = (R+next_J*(d*Q)*next_J');
invSig = J'*(R\J) + ((A^d)'*next_J'/nV)*next_J*(A^d) + invQ/k;
invS = invR - ((R\J)/invSig)*J'/R;
S = inv(invS);
mu = [p_bng; p_rng] - J*p_x + invS \ ( (((R\J)*(invSig\(A^d)')*next_J'/nV)*(next_y-next_p_pol+next_J*next_p_x)) + (((R\(J/invSig))/(k*Q))*(A^k)*x) );
end
S = (S+S')/2;
% if (d>z)&&(Set.tracks{j}.GetAssoc(t-z)==0)&&(t-z>0)
% S = 10*S;
% end
thresh1 = 5*sqrt(S(1,1));
thresh2 = 5*sqrt(S(2,2));
% Precalculate some things to speed up the next loop, which is over
% 100's of observations within a loop over 100's of particles (i.e.
% the bottleneck of the whole program)
innov = bsxfun(@minus, Observs(tt).r, mu')';
wrap_around = innov(1,:)>pi;
innov(1, wrap_around) = 2*pi - innov(1, wrap_around);
test1 = abs(innov(1, :)) < thresh1;
test2 = abs(innov(2, :)) < thresh2;
indexes = find(test1&test2);
test = zeros(1, Observs(tt).N);
for i = indexes
test(i) = ((innov(:,i)'/S)*innov(:,i) < 16);
end
validated = find(test);
% disp(length(validated));
% Calculate weights
ppsl_weights = zeros(N+1, 1);
for i = validated
ppsl_weights(i) = (Par.PDetect) * mvnpdf(Observs(tt).r(i, :), mu', S);
end
% % Calculate weights
% ppsl_weights = zeros(N+1, 1);
% for i = 1:N
%
% if (BngDist(Observs(tt).r(i, 1), mu(1))<thresh1)&&(abs(Observs(tt).r(i, 2)-mu(2))<thresh2)
% ppsl_weights(i) = (Par.PDetect) * mvnpdf(Observs(tt).r(i, :), mu', S);%/Observs(tt).N
%
% % obs_cart = Pol2Cart(Observs(tt).r(i, 1), Observs(tt).r(i, 2));
% % if Dist(obs_cart, p_x)<k*Par.Vmax
% % innov = Observs(tt).r(i, :)' - mu;
% % if innov(1)>pi
% % innov(1) = innov(1) - 2*pi;
% % end
% % if innov(1)<-pi
% % innov(1) = innov(1) + 2*pi;
% % end
% % if (abs(innov(1))<thresh1)&&(abs(innov(2))<thresh2)&&((innov'/S)*innov < 16)
% % ppsl_weights(i) = (Par.PDetect) * mvnpdf(Observs(tt).r(i, :), mu', S);
% else
% ppsl_weights(i) = 0;
% end
% end
% Clutter
ppsl_weights(N+1) = Par.ClutDens * (1-Par.PDetect);
% Remove used ones
ppsl_weights(used_ass{k}) = 0;
% Normalise
ppsl_weights = ppsl_weights/sum(ppsl_weights);
% Set minimum value for clutter as 1-PDetect
if (d>L)&&(ppsl_weights(N+1)<(1-Par.PDetect))
ppsl_weights(1:N) = Par.PDetect*ppsl_weights(1:N)/sum(ppsl_weights(1:N));
ppsl_weights(N+1) = 1-Par.PDetect;
end
if ~FLAG_ns
% Sample
ass = randsample(N+1, 1, true, ppsl_weights);
else
ass = Set.tracks{j}.GetAssoc(tt);
if ass == 0
ass = N+1;
end
end
% Probability
jah_ppsl(j, k) = log(ppsl_weights(ass));
% Assign it
if ass==N+1
ass = 0;
else
used_ass{k} = [used_ass{k}; ass];
end
if ~FLAG_ns
Set.tracks{j}.SetAssoc(tt, ass);
end
% Store numbers for next step
if ass==0
d=d+1;
else
next_y = Observs(tt).r(ass, :)';
next_p_pol = [p_bng; p_rng];
next_p_x = p_x;
next_J = J;
d=1;
end
end
end
end
% range_squ = range^2;
% range = rng;
% jac = [-x(2)/range_squ, x(1)/range_squ, 0, 0; x(1)/range, x(2)/range, 0, 0];
% mean_obs = [bng; rng];
% var_obs = Par.R + jac * Par.Q * jac';
%
% % Find the marginal likelihood of each observation, given the previous state
% for i = 1:N
%
% obs_cart = Pol2Cart(Observs(t).r(i, 1), Observs(t).r(i, 2));
% mean_cart = Pol2Cart(mean_obs(1), mean_obs(2));
% if Dist(obs_cart, mean_cart)<Par.Vmax
% ppsl_weights(i) = (Par.PDetect/Observs(t).N) * mvnpdf(Observs(t).r(i, :), mean_obs', var_obs);
% % ppsl_weights(i) = mvnpdfFastSymm(obs_cart, mean_cart, Par.AuctionVar);
% else
% ppsl_weights(i) = 0;
% end
%
% end
%
% % Clutter
% % ppsl_weights(N+1) = Par.UnifPosDens;
% ppsl_weights(N+1) = Par.ClutDens * (1-Par.PDetect);
%
% % Remove used ones
% ppsl_weights(used_ass) = 0;
%
% % Normalise
% ppsl_weights = ppsl_weights/sum(ppsl_weights);
%
% % Sample
% ass = randsample(N+1, 1, true, ppsl_weights);
%
% % Probability
% jah_ppsl(j) = log(ppsl_weights(ass));
%
% % Assign it
% if ass==N+1
% ass = 0;
% else
% used_ass = [used_ass; ass];
% end
% Set.tracks{j}.SetAssoc(t, ass);
%
% end
%
% end
% function d = Dist(x1, x2)
% d = sqrt((x1(1)-x2(1))^2+(x1(2)-x2(2))^2);
% end
%
function diff = BngDist(b1, b2)
diff=abs(b1-b2);
if diff>pi
diff = 2*pi - diff;
end
end