Title :
Addressing track coalescence in sequential K-best multiple hypothesis tracking
Author :
Palkki, Ryan D. ; Lanterman, Aaron D. ; Blair, W. Dale
Author_Institution :
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA
Abstract :
The sequential K -best multiple hypothesis tracker is implemented for a single-target, single-sensor scenario. The K-best data association approach is compared to the probabilistic data association technique to determine under what conditions it is generally the preferred method. A fundamental problem is observed in which the tracks coalesce. Several methods to prevent coalescence are presented and compared
Keywords :
Kalman filters; target tracking; K-best data association approach; probabilistic data association technique; sequential K-best multiple hypothesis tracking; single-sensor scenario; single-target scenario; track coalescence; Current measurement; Equations; Filters; Measurement errors; Measurement uncertainty; Nearest neighbor searches; Neural networks; Personal digital assistants; State estimation; Trajectory;
Conference_Titel :
System Theory, 2006. SSST '06. Proceeding of the Thirty-Eighth Southeastern Symposium on
Conference_Location :
Cookeville, TN
Print_ISBN :
0-7803-9457-7
DOI :
10.1109/SSST.2006.1619134
