2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSAM-8.4
Paper Title EKFNET: LEARNING SYSTEM NOISE STATISTICS FROM MEASUREMENT DATA
Authors Liang Xu, Ruixin Niu, Virginia Commonwealth University, United States
SessionSAM-8: Detection and Estimation 2
LocationGather.Town
Session Time:Thursday, 10 June, 16:30 - 17:15
Presentation Time:Thursday, 10 June, 16:30 - 17:15
Presentation Poster
Topic Sensor Array and Multichannel Signal Processing: [RAS-TRCK] Target tracking
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In this paper, to reduce the time and manpower spent on fine-tuning an extended Kalman filter (EKF), we propose a new learning framework, EKFNet, for automatically estimating the best process and measurement noise covariance pair from the real measurement data. The EKFNet is trained by using backpropagation through time (BPTT). 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. We illustrate the proposed method’s performance using real GPS data, which outperforms existing methods and a manually tuned EKF.