Title :
Particle PHD filter multiple target tracking in sonar image
Author :
Clark, Daniel ; Ruiz, I.T. ; Petillot, Yvan ; Bell, Jonathan
Author_Institution :
Dept. of Telecommun. Eng., Myongji Univ., Yongin
fDate :
1/1/2007 12:00:00 AM
Abstract :
Two contrasting approaches for tracking multiple targets in multi-beam forward-looking sonar images are considered. The first approach is based on assigning a Kalman filter to each target and managing the measurements with gating and a measurement-to-track data association technique. The second approach uses the recently developed particle implementation of the multiple-target probability hypothesis density (PHD) filter and a target state estimate-to-track data association technique. The two approaches are implemented and compared on both simulated sonar and real forward-looking sonar data obtained from an autonomous underwater vehicle (AUV) and demonstrate that the PHD filter with data association compares well with traditional approaches for multiple target tracking
Keywords :
Kalman filters; particle filtering (numerical methods); sensor fusion; sonar imaging; target tracking; underwater vehicles; AUV; Kalman filter; autonomous underwater vehicle; measurement-to-track data association technique; multibeam forward-looking sonar images; multiple target tracking; multiple-target probability hypothesis density filter; particle PHD filter; particle implementation; target state estimate-to-track data association technique; Filters; Layout; Particle tracking; Partitioning algorithms; Sonar equipment; Sonar measurements; Sonar navigation; State estimation; Target tracking; Underwater vehicles;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
DOI :
10.1109/TAES.2007.357143