Joint Probabilistic Data Association Filter (original) (raw)
The joint probabilistic data-association filter (JPDAF) is a statistical approach to the problem of plot association (target-measurement assignment) in a target tracking algorithm. Like the probabilistic data association filter (PDAF), rather than choosing the most likely assignment of measurements to a target (or declaring the target not detected or a measurement to be a false alarm), the PDAF takes an expected value, which is the minimum mean square error (MMSE) estimate for the state of each target. At each time, it maintains its estimate of the target state as the mean and covariance matrix of a multivariate normal distribution. However, unlike the PDAF, which is only meant for tracking a single target in the presence of false alarms and missed detections, the JPDAF can handle multipl
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| dbo:abstract | The joint probabilistic data-association filter (JPDAF) is a statistical approach to the problem of plot association (target-measurement assignment) in a target tracking algorithm. Like the probabilistic data association filter (PDAF), rather than choosing the most likely assignment of measurements to a target (or declaring the target not detected or a measurement to be a false alarm), the PDAF takes an expected value, which is the minimum mean square error (MMSE) estimate for the state of each target. At each time, it maintains its estimate of the target state as the mean and covariance matrix of a multivariate normal distribution. However, unlike the PDAF, which is only meant for tracking a single target in the presence of false alarms and missed detections, the JPDAF can handle multiple target tracking scenarios. A derivation of the JPDAF is given in. The JPDAF is one of several techniques for radar target tracking and for target tracking in the field of computer vision. (en) |
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| rdfs:comment | The joint probabilistic data-association filter (JPDAF) is a statistical approach to the problem of plot association (target-measurement assignment) in a target tracking algorithm. Like the probabilistic data association filter (PDAF), rather than choosing the most likely assignment of measurements to a target (or declaring the target not detected or a measurement to be a false alarm), the PDAF takes an expected value, which is the minimum mean square error (MMSE) estimate for the state of each target. At each time, it maintains its estimate of the target state as the mean and covariance matrix of a multivariate normal distribution. However, unlike the PDAF, which is only meant for tracking a single target in the presence of false alarms and missed detections, the JPDAF can handle multipl (en) |
| rdfs:label | Joint Probabilistic Data Association Filter (en) |
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