Extended Object Tracking With Automotive Radar Using B-Spline Chained Ellipses Model
Gang Yao, P. Wang, Karl Berntorp, Hassan Mansour, Petros T. Boufounos, Philip V. Orlik
Abstract
This paper introduces a B-spline chained ellipses model representation for extended object tracking (EOT) using high-resolution automotive radar measurements. With offline automotive radar training datasets, the proposed model parameters are learned using the expectation-maximization (EM) algorithm. Then the probabilistic multi-hypothesis tracking (PMHT) along with the unscented transform (UT) is proposed to deal with the nonlinear forward-warping coordinate transformation, the measurement-to-ellipsis association, and the state update step. Numerical validation is provided to verify the effectiveness of the proposed EOT framework with automotive radar measurements.