An improved approach for multi-target detection and tracking in automotive radar systems

Abstract

In this work, a multiple-target tracking problem for automotive radar applications is formulated and an improved multi-target tracking system is proposed to solve the detection and tracking problem in the presence of clutter with high accuracy and low computational cost. The proposed tracking system is based on the Unscented Kalman Filter (UKF) with Constant Turn Rate and Acceleration (CTRA) dynamic model and on the Joint Probabilistic Data Association (JPDA) algorithm, while the track management algorithm is based on M/N tests and their composite rules. The results show that the CTRA-UKF algorithm in conjunction with both the JPDA and the composite-based track management tests improve the overall performance of the tracking system over other techniques used in automotive radar applications.