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Probability Hypothesis Density Filter
, 2011
"... Technical reports from the Automatic Control group in Linköping are available from ..."
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Technical reports from the Automatic Control group in Linköping are available from
The Gaussian mixture probability hypothesis density filter
 IEEE Trans. SP
, 2006
"... Abstract — A new recursive algorithm is proposed for jointly estimating the timevarying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise and false alarms. The approach involves modelling the respecti ..."
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Cited by 142 (19 self)
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the respective collections of 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
MultiTarget Particle Filtering for the Probability Hypothesis Density
, 2003
"... When tracking a large number of targets, it is often computationally expensive to represent the full joint distribution over target states. In cases where the targets move independently, each target can instead be tracked with a separate filter. However, this leads to a modeldata association proble ..."
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Cited by 62 (6 self)
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problem. Another approach to solve the problem with computational complexity is to track only the first moment of the joint distribution, the probability hypothesis density (PHD). The integral of this distribution over any area S is the expected number of targets within S. Since no record of object
The Gaussian Mixture Probability Hypothesis Density
"... Density Filter (GMPHD Filter) was proposed recently for jointly estimating the timevarying number of targets and their states from a noisy sequence of sets of measurements which may have missed detections and false alarms. The initial implementation of the GMPHD filter provided estimates for the ..."
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Density Filter (GMPHD Filter) was proposed recently for jointly estimating the timevarying number of targets and their states from a noisy sequence of sets of measurements which may have missed detections and false alarms. The initial implementation of the GMPHD filter provided estimates for the set of target states at each point in time but did not ensure continuity of the individual target tracks. It is shown here that the trajectories of the targets can be determined directly from the evolution of the Gaussian mixture and that single Gaussians within this mixture accurately track the correct targets. Furthermore, the technique is demonstrated to be successful in estimating the correct number of targets and their trajectories in high clutter density and shows better performance than the MHT filter.
Auxiliary Particle Implementation of the Probability Hypothesis Density Filter
"... Optimal Bayesian multitarget filtering is, in general, computationally impractical owing to the high dimensionality of the multitarget state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multitarget posterior distribution. While this reduces the dimensionalit ..."
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Cited by 15 (3 self)
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Optimal Bayesian multitarget filtering is, in general, computationally impractical owing to the high dimensionality of the multitarget state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multitarget posterior distribution. While this reduces
Gaussian Particle Implementations of Probability Hypothesis Density Filters
"... Abstract — The Probability Hypothesis Density (PHD) filter is a multipletarget filter for recursively estimating the number of targets and their state vectors from sets of observations. The filter is able to operate in environments with false alarms and missed detections. Two distinct algorithmic i ..."
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Cited by 5 (3 self)
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Abstract — The Probability Hypothesis Density (PHD) filter is a multipletarget filter for recursively estimating the number of targets and their state vectors from sets of observations. The filter is able to operate in environments with false alarms and missed detections. Two distinct algorithmic
Smoothing Algorithms for the Probability Hypothesis Density Filter
, 2010
"... The probability hypothesis density (PHD) filter is a recursive algorithm for solving multitarget tracking problems. The method consists of replacing the expectation of a random variable used in the traditional Bayesian filtering equations, by taking generalized expectations using the random sets f ..."
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Cited by 1 (1 self)
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The probability hypothesis density (PHD) filter is a recursive algorithm for solving multitarget tracking problems. The method consists of replacing the expectation of a random variable used in the traditional Bayesian filtering equations, by taking generalized expectations using the random sets
MultiTarget Particle Filtering for the Probability Hypothesis Density
"... Abstract – When tracking a large number of targets, it is often computationally expensive to represent the full joint distribution over target states. In cases where the targets move independently, each target can instead be tracked with a separate filter. However, this leads to a modeldata associa ..."
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data association problem. Another approach to solve the problem with computational complexity is to track only the first moment of the joint distribution, the probability hypothesis density (PHD). The integral of this distribution over any area S is the expected number of targets within S. Since no record
Auxiliary Particle . . .Probability Hypothesis Density Filter
, 2007
"... Optimal Bayesian multitarget filtering is, in general, computationally impractical owing to the high dimensionality of the multitarget state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multitarget posterior distribution. While this reduces the dimensionalit ..."
Abstract
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Optimal Bayesian multitarget filtering is, in general, computationally impractical owing to the high dimensionality of the multitarget state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multitarget posterior distribution. While this reduces
Results 1  10
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5,503