文件名称:GM_PHD
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targets and measurements as random finite sets and applying the
probability hypothesis density (PHD) recursion to propagate the
posterior intensity, which is a first order statistic of the random
finite set of targets, in time. At present, there is no closed form
solution to the PHD recursion. This work shows that under linear,
Gaussian assumptions on the target dynamics and birth process,
the posterior intensity at any time step is a Gaussian mixture.
probability hypothesis density (PHD) recursion to propagate the
posterior intensity, which is a first order statistic of the random
finite set of targets, in time. At present, there is no closed form
solution to the PHD recursion. This work shows that under linear,
Gaussian assumptions on the target dynamics and birth process,
the posterior intensity at any time step is a Gaussian mixture.
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下载文件列表
GM_PHD/
GM_PHD/applyhatch.m
GM_PHD/CLUSTER.m
GM_PHD/demo.asv
GM_PHD/demo.m
GM_PHD/ESTIMATE.m
GM_PHD/GENRANDN.m
GM_PHD/HDISTANCE.m
GM_PHD/Hungary.m
GM_PHD/IsInMatrix.m
GM_PHD/makehatch.m
GM_PHD/MEASUREMENT.asv
GM_PHD/MEASUREMENT.m
GM_PHD/Normp.m
GM_PHD/OSPA.m
GM_PHD/PREDIC.m
GM_PHD/PREDIC_S.asv
GM_PHD/PREDIC_S.m
GM_PHD/PRUN_MERG.asv
GM_PHD/PRUN_MERG.m
GM_PHD/TARGET_FORM.asv
GM_PHD/TARGET_FORM.m
GM_PHD/TryAssign.m
GM_PHD/UPDATA.asv
GM_PHD/UPDATA.m
GM_PHD/WDISTANCE.m
GM_PHD/ZeroCover.m
GM_PHD/ZeroNumber.m
GM_PHD/applyhatch.m
GM_PHD/CLUSTER.m
GM_PHD/demo.asv
GM_PHD/demo.m
GM_PHD/ESTIMATE.m
GM_PHD/GENRANDN.m
GM_PHD/HDISTANCE.m
GM_PHD/Hungary.m
GM_PHD/IsInMatrix.m
GM_PHD/makehatch.m
GM_PHD/MEASUREMENT.asv
GM_PHD/MEASUREMENT.m
GM_PHD/Normp.m
GM_PHD/OSPA.m
GM_PHD/PREDIC.m
GM_PHD/PREDIC_S.asv
GM_PHD/PREDIC_S.m
GM_PHD/PRUN_MERG.asv
GM_PHD/PRUN_MERG.m
GM_PHD/TARGET_FORM.asv
GM_PHD/TARGET_FORM.m
GM_PHD/TryAssign.m
GM_PHD/UPDATA.asv
GM_PHD/UPDATA.m
GM_PHD/WDISTANCE.m
GM_PHD/ZeroCover.m
GM_PHD/ZeroNumber.m
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