| Paper ID | SAM-8.4 |
| Paper Title |
EKFNET: LEARNING SYSTEM NOISE STATISTICS FROM MEASUREMENT DATA |
| Authors |
Liang Xu, Ruixin Niu, Virginia Commonwealth University, United States |
| Session | SAM-8: Detection and Estimation 2 |
| Location | Gather.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 |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
