000007747 001__ 7747
000007747 005__ 20240626123055.0
000007747 02470 $$2doi$$a10.24868/issn.2631-8741.2020.008
000007747 035__ $$a4468319
000007747 037__ $$aGENERAL
000007747 245__ $$aPlane Partitions in Batch Track-Track Associations
000007747 269__ $$a2020-10-05
000007747 336__ $$aConference Proceedings
000007747 520__ $$aThe most difficult multiple target tracking problem includes multiple sensors with different viewing angles, measurement geometries, fields of view, accuracies, resolutions and scan rates. Such variations in sensor output characteristics as well as channel delays, countermeasures, inherent target features and maneuvers have solidified the consensus that an effective fusion system must handle several levels of “tracklets” from distributed sources in order to produce the desired long tracks as described in Waltz and Llinas (1990). In view of the increased attention given to hypersonics as well as the increased need for low-level signal processing, the computational complexity of track association is a vital factor in determining an autonomous vehicles’ ability to complete its<br>
objectives quickly. We are given a set of tracklets where the particular methods used to make the detections are taken for granted. Following joint probability density association filters, we assume short tracklets are completed (i.e, detections are correctly correlated with state estimates) and take a computational geometric approach to associating tracklets. If N is the number of short term tracklets, this method fuses them in O(N2). Using covariance as a distance, this report suggests the applicability of a class of sweep-line algorithms developed in computational geometry in data fusion.
000007747 542__ $$fCC-BY-4.0
000007747 6531_ $$aMultiple target tracking
000007747 6531_ $$adata fusion
000007747 6531_ $$atrack association
000007747 6531_ $$aradar
000007747 7001_ $$aNelatury, Charles$$uUniversity of Colorado, Boulder, USA
000007747 773__ $$tConference Proceedings of iSCSS
000007747 773__ $$jiSCSS 2020
000007747 789__ $$whttps://zenodo.org/record/4468319$$2URL$$eIsIdenticalTo
000007747 85641 $$uhttps://www.imarest.org/events/inec-2020/iscss-2020$$yConference website
000007747 8564_ $$9dbeb345f-0eea-46b9-8efe-bf991df8ee79$$s635981$$uhttps://library.imarest.org/record/7747/files/iSCSS_2020_Paper_17.pdf