?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.relation=http%3A%2F%2Fmiis.maths.ox.ac.uk%2Fmiis%2F188%2F&rft.title=Tracking+and+Identifying+of+Multiple+Targets&rft.creator=Yewchuk%2C+Kerianne&rft.creator=Ketelsen%2C+Christian&rft.creator=Limon%2C+Alfonso&rft.creator=Mileyko%2C+Yuryi&rft.subject=Aerospace+and+defence&rft.description=There+are+many+statistical+methods+of+tracking+single+and+multiple+targets%3B+this+manuscript+will+focus+on+the+state+estimation+problem.+Ideally%2C+a+generalization+of+the+recursive+Bayes+non-linear+filter+would+track+and+resolve+the+state(s)+of+single+or+multiple+targets%2C+but+that+is+currently+computationally+intractable.+The+Probability+Hypothesis+Density+(PHD)+makes+the+tracking+problem+computationally+feasible+by+propagating+only+the+first-order+multi-target+statistical+moments+by+using+a+particle+filter+implementation+for+the+PHD.+The+problem+then+becomes+one+of+estimating+the+targets%E2%80%99+state+based+on+the+output+of+the+PHD+when+using+a+particle+filter+implementation.+%0A%0AThis+paper+describes+one+heuristic+method+for+obtaining+a+state+estimator+from+the+PHD.+The+approach+used+in+this+paper%2C+based+on+the+Expectation-Maximization+(EM)+algorithm%2C+views+the+PHD+distribution+as+a+mixture+distribution%2C+and+the+particles+as+an+i.i.d.+sampling+from+the+mixture+distribution.+Using+this%2C+a+maximum+likelihood+estimator+for+the+parameters+of+the+distribution+can+be+generated.+The+EM+seems+to+work+fairly+well%2C+particularly+when+targets+are+well+spaced.&rft.date=2003&rft.type=Study+Group+Report&rft.type=NonPeerReviewed&rft.format=application%2Fpdf&rft.language=en&rft.identifier=http%3A%2F%2Fmiis.maths.ox.ac.uk%2Fmiis%2F188%2F1%2Flockheed.pdf&rft.identifier=++Yewchuk%2C+Kerianne+and+Ketelsen%2C+Christian+and+Limon%2C+Alfonso+and+Mileyko%2C+Yuryi++(2003)+Tracking+and+Identifying+of+Multiple+Targets.++%5BStudy+Group+Report%5D+++++