The Probabilistic Data Association Filter (PDAF)[1][2] is a statistical approach to the problem of plot association (target-measurement assignment) in a target tracking algorithm. 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. The PDAF on its own does not confirm nor terminate tracks.
Python: The PDAF and other data association methods are implemented in Stone-Soup.[4] A tutorial demonstrates how the algorithms can be used.[5][6]
Referencesedit
^Bar-Shalom, Yaakov; Tse, Edison (1975). "Tracking in a Cluttered Environment With Probabilistic Data Association". Automatica. 11 (5): 451–460. doi:10.1016/0005-1098(75)90021-7.
^ abBar-Shalom, Yaakov; Daum, Fred; Huang, Jim (December 2009). "The probabilistic data association filter". IEEE Control Systems Magazine. 29 (6): 82–100. doi:10.1109/MCS.2009.934469. S2CID 6875122.
^"Tracker Component Library". Matlab Repository. Retrieved January 5, 2019.
probabilistic, data, association, filter, probabilistic, data, association, filter, pdaf, statistical, approach, problem, plot, association, target, measurement, assignment, target, tracking, algorithm, rather, than, choosing, most, likely, assignment, measure. The Probabilistic Data Association Filter PDAF 1 2 is a statistical approach to the problem of plot association target measurement assignment in a target tracking algorithm 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 The PDAF on its own does not confirm nor terminate tracks Whereas the PDAF is only designed to track a single target in the presence of false alarms and missed detections the Joint Probabilistic Data Association Filter JPDAF can handle multiple targets The first real world application of the PDAF was probably in the Jindalee Operational Radar Network 2 which is an Australian over the horizon radar OTHR network Implementations editMatlab The PDAF and JPDAF algorithms are implemented in the singleScanUpdate function that is part of the United States Naval Research Laboratory s free Tracker Component Library 3 Python The PDAF and other data association methods are implemented in Stone Soup 4 A tutorial demonstrates how the algorithms can be used 5 6 References edit Bar Shalom Yaakov Tse Edison 1975 Tracking in a Cluttered Environment With Probabilistic Data Association Automatica 11 5 451 460 doi 10 1016 0005 1098 75 90021 7 a b Bar Shalom Yaakov Daum Fred Huang Jim December 2009 The probabilistic data association filter IEEE Control Systems Magazine 29 6 82 100 doi 10 1109 MCS 2009 934469 S2CID 6875122 Tracker Component Library Matlab Repository Retrieved January 5 2019 Stone Soup Github Repo GitHub Stone Soup PDA Tutorial Documentation Stone Soup PDA Tutorial Code GitHub This article related to sensors is a stub You can help Wikipedia by expanding it vte Retrieved from https en wikipedia org w index php title Probabilistic data association filter amp oldid 1131500435, wikipedia, wiki, book, books, library,
