Track stitching and approximate track association on a pairwise-likelihood graph

2018 ◽  
Author(s):  
Lingji Chen ◽  
Sarah E. Rumbley
Keyword(s):  
Pairwise Likelihood ◽  
Track Association

scholarly journals A track-to-track association method for automotive perception systems

2012 ◽  
Author(s):  
Adam Houenou ◽  
Philippe Bonnifait ◽  
Veronique Cherfaoui ◽  
Jean-Francois Boissou
Keyword(s):  
Track Association ◽  
Association Method

Multipath track association for over-the-horizon radar using Lagrangian relaxation

2004 ◽  
Author(s):  
Mark G. Rutten ◽  
Simon Maskell ◽  
Mark Briers ◽  
Neil J. Gordon
Keyword(s):  
Lagrangian Relaxation ◽  
Track Association

scholarly journals Plane Partitions in Batch Track-Track Associations

2020 ◽  
Author(s):  
C Nelatury
Keyword(s):  
Autonomous Vehicles ◽  
Joint Probability ◽  
Geometric Approach ◽  
Multiple Target Tracking ◽  
Multiple Sensors ◽  
Track Association ◽  
Output Characteristics ◽  
Sweep Line ◽  
Joint Probability Density ◽  
Vital Factor

The 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 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.

scholarly journals Radar/ESM anti-bias track association algorithm based on track distance vector detection

2019 ◽  
Vol 2019 (21) ◽  
pp. 7563-7567
Author(s):  
Baozhu Li ◽  
Yunlong Dong ◽  
Gaodong Huang ◽  
Xiaolong Chen ◽  
Jian Guan
Keyword(s):  
Track Association ◽  
Distance Vector ◽  
Association Algorithm
Sign in / Sign up

Export Citation Format

Share Document