A Robust Approach to Model Colored Noise for Low-cost High-precision Positioning

Yuting Gao, Yang Gao, Baoyu Liu, Yuan Du, Jin Wang

Abstract:	Generally, GNSS receiver’s measurement noise is assumed to be Gaussian distribution. However, be subject to various noise sources, the measurements from low-cost devices do not follow the Gaussian white noise assumption used in Kalman filter which often show strong characteristics of colored noise especially in challenging environment. Many of the involved noises such satellite clock error and multipath are presented as time-correlated. To deal with this problem, an approach based on colored Kalman filter (CKF) is presented in this paper, which considers measurement time-correlation by a first order autoregressive model and rebuilds a new measurement model for the CKF. A short baseline RTK experiment is performed with a low-cost receiver in challenging environment. The experiment results show that the state variance matrix obtained by the CKF can perfectly reflect the realistic position error, while the estimate of the standard Kalman filter is found too optimistic to reveal the realistic value. In addition, the CKF can improve around 30% of the AR (Ambiguity Resolution) convergence time, and the reliability of AR has pretty high AR ratio in challenging environment.
Published in:	Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019) September 16 - 20, 2019 Hyatt Regency Miami Miami, Florida
Pages:	3686 - 3694
Cite this article:	Gao, Yuting, Gao, Yang, Liu, Baoyu, Du, Yuan, Wang, Jin, "A Robust Approach to Model Colored Noise for Low-cost High-precision Positioning," Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), Miami, Florida, September 2019, pp. 3686-3694. https://doi.org/10.33012/2019.16976
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