Lidar-millimeter wave radar information fusion multi-target detection based on unscented Kalman filter and covariance intersection algorithm

Lidar-based object detection is an important method of environment perception for autonomous driving. Due to the limitation of the inherent properties of lidar, the detection accuracy of obscured vehicles and distant objects is inferior, which causes the problem of missed detection. To address this problem, a lidar-millimeter wave radar information fusion multi-target detection method based on the unscented Kalman filter (UKF) and the covariance intersection (CI) algorithm was proposed in this article. Firstly, the UKF algorithm was applied to generate state estimations on the data collected by the sensor. Subsequently, the CI algorithm was introduced to form state fusion estimates. Finally, a simulation experiment platform was built based on MATLAB, and a comparison experiment with Joint Probabilistic Data Association (JPDA) and Gaussian mixture probability hypothesis density (GMPHD) algorithms were designed. The Generalized optimal sub-pattern assignment (GOSPA) indi-cators were adopted to evaluate the detection accuracy of each algorithm, and the effectiveness of the method was verified. The experimental results showed that UKF-CI had higher detection accuracy and provided accurate infor-mation for the decision-making part of the autonomous driving system, which guaranteed the stable operation of the autonomous driving system.