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Variants of the JPDAF algorithm have been made that try to avoid track coalescence. For example, the Set JPDAF uses an approximate minimum mean optimal sub pattern assignment (MMOSPA) instead of an approximate MMSE estimator. The JPDAF*, modifies how the target-measurement association probabilities
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are computed, and variants of the global nearest-neighbor JPDAF (GNN-JPDAF) (a best-hypothesis tracker) use the global nearest neighbor (GNN) estimate in place of the mean but compute the covariance matrix as in the normal JPDAF: as a mean-squared error matrix.
40:. 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.
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A common problem observed with the JPDAF is that estimates of closely spaced targets tend to coalesce over time. This is because the MMSE estimate is typically undesirable when target identity is uncertain.
28:(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
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36:(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
100:: The PDAF, JPDAF and other data association methods are implemented in Stone-Soup. A tutorial demonstrates how the algorithms can be used.
76:: The PDAF, JPDAF, Set JPDAF, JPDAF*, GNN-JPDAF and multiple other exact and approximate variants of the JPDAF are implemented in the
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Bar-Shalom, Yaakov (1986). "Comments on "Track Biases and
Coalescence with Probabilistic Data Association"".
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Bar-Shalom, Yaakov; Daum, Fred; Huang, Jim (December 2009). "The probabilistic data association filter".
308:. Proceedings of SPIE: Signal and Data Processing of Small Targets Conference. Denver. pp. 586–600.
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Fitzgerald, Robert (November 1985). "Track Biases and
Coalescence with Probabilistic Data Association".
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266:. Proceedings of the 12th International Conference of Information Fusion. Seattle. pp. 1187–1194.
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251:. Proceeding of SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII. Baltimore.
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Blom, H.A.P.; Bloem, E.A. (2000). "Probabilistic data association avoiding track coalescence".
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Multitarget-multisensor tracking : principles and techniques, 1995
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Svensson, Lennart; Svensson, Daniel; Willett, Peter (July 2009).
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demonstrates how the algorithms can be used in a simple scenario.
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Advances in displaying uncertain estimates of multiple targets
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Set JPDA algorithm for tracking unordered sets of targets
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target tracking and for target tracking in the field of
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IEEE Transactions on
Aerospace and Electronic Systems
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IEEE Transactions on
Aerospace and Electronic Systems
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220:. AES-22 (5): 661–662.
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