EKFNet: Learning System Noise Covariance Parameters for Nonlinear Tracking
Liang Xu, Ruixin Niu
Abstract
In this paper, to reduce the time and manpower to fine-tune an extended Kalman filter (EKF), we propose a new learning framework, EKFNet, for automatically estimating the best process and measurement noise covariance parameters for an EKF from real measurement data. The EKFNet is trained end-to-end by using backpropagation through time (BPTT) over the EKF. The forward operation of EKFNet is the same as the normal EKF operation which will be used during the tracking process. During the offline training, the EKFNet uses the BPTT for passing the gradient flow to each time step and optimizing the unknown noise statistic parameters. The proposed method can choose among several optimization criteria, such as maximizing the likelihood, minimizing the measurement residual error, or minimizing the posterior state estimation error either with or without the ground truth data. The proposed method's performance is demonstrated using real GPS data, which outperforms an existing method and a manually tuned EKF.