Title :
Multisensor Vehicle Tracking with the Probability Hypothesis Density Filter
Author :
Maehlisch, Mirko ; Schweiger, Roland ; Ritter, Werner ; Dietmayer, Klaus
Author_Institution :
Dept. of Meas., Control & Microtechnol., Ulm Univ.
Abstract :
In this contribution we apply the probability hypothesis density (PHD) filter algorithm for joint tracking of an unknown varying number of targets to automotive environment sensing systems. We use data from a vision and a lidar sensor as well as the vehicle ESP system. After deriving a method to parametrise the algorithm systematically from detection performance statistics we proof the applicability of the method for automotive tracking based on real sensor data
Keywords :
probability; road vehicles; sensor fusion; target tracking; tracking filters; PHD filter algorithm; automotive environment sensing system; detection performance statistics; lidar sensor; multisensor vehicle tracking; probability hypothesis density; vehicle ESP system; Automotive engineering; Decision making; Filters; Radar tracking; Sensor fusion; Sensor phenomena and characterization; Sensor systems; Target tracking; Vehicles; Velocity measurement; Future Driver Assistance Systems; Joint Target Tracking; Probability Hypothesis Density; Vehicle Detection;
Conference_Titel :
Information Fusion, 2006 9th International Conference on
Conference_Location :
Florence
Print_ISBN :
1-4244-0953-5
Electronic_ISBN :
0-9721844-6-5
DOI :
10.1109/ICIF.2006.301648
