Joint Integrated Probabilistic Data Association - JIPDA

By D. Musicki, R. Evans. Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997), 2002

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Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member (2012, background): “The posterior tracks retained by the TOMB/P (and JIPDA, etc) are shown in (e) and (f); this yields the approximation of the posterior in (i), which is a symmetrised version of the distribution p 1,1 f 1 (x 1 t |z 1 t ) + p 1,2 f 1 (x 1 t |z 2 t ) × p 2,1 f 2 (x 2 t |z 1 t ) + p 2,2 f 2 (x 2 t |z 2 t”
Overview of Environment Perception for Intelligent Vehicles (2017, methodology): “Methods for explicit data association tracking vary widely: from the nearest neighbor (NN) algorithm [127], the multihypothesis tracking (MHT) approach [128], the probabilistic data association (PDA) approach [129], to the joint probability data association (JPDA) algorithms [130], [131].”
Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs (2015, methodology): “In addition to their wide applications in radar, sonar, electro-optical sensor networks and navigation systems [466]–[480], the PDA techniques have also been applied in the field of computer vision for solving the visual target tracking problem [481]–[484].”
Multi-Sensor Fusion in Automated Driving: A Survey (2020, methodology): “Furthermore, the exact nearest neighbor PDA (ENNPDA) [158], Joint integrated probabilistic data association (JIPDA) [159], and other improved algorithms are proposed to simplify the algorithm by sacriﬁce the precision.”