Title :
Estimating polynomial structures from radar data
Author :
Lundquist, C. ; Orguner, U. ; Gustafsson, F.
Author_Institution :
Dept. of Electr. Eng., Linkoping Univ., Linköping, Sweden
Abstract :
Situation awareness for vehicular safety and autonomy functions includes knowledge of the drivable area. This area is normally constrained between stationary road-side objects as guard-rails, curbs, ditches and vegetation. We consider these as extended objects modeled by polynomials along the road, and propose an algorithm to track each polynomial based on noisy range and bearing detections, typically from a radar. A straightforward Kalman filter formulation of the problem suffers from the errors-in-variables (EIV) problem in that the noise enters the system model. We propose an EIV modification of the Kalman filter and demonstrates its usefulness using radar data from public roads.
Keywords :
Kalman filters; radar signal processing; road safety; target tracking; traffic engineering computing; Kalman filter; bearing detection; errors-in-variables problem; noisy range detection; polynomial structure estimation; radar data; stationary road-side objects; vehicular safety; Kalman filters; Noise; Noise measurement; Polynomials; Radar tracking; Target tracking; automotive radar; errors in output; errors in variables; extended object; extended target tracking; polynomial; road map;
Conference_Titel :
Information Fusion (FUSION), 2010 13th Conference on
Conference_Location :
Edinburgh
Print_ISBN :
978-0-9824438-1-1
DOI :
10.1109/ICIF.2010.5711883