Enhanced Kalman Filter using Noisy Input Gaussian Process Regression for Bridging GPS Outages in a POS. (28th November 2017)

• Record Type: Journal Article
• Authors: Ye, Wen; Liu, Zhanchao; Li, Chi; Fang, Jiancheng
• Is Part Of: Journal of navigation. Volume 71:Number 3(2018)
• Page Start: 565
• Page End: 584
• Publication Date: 2017-11-28
• Subjects: Position and Orientation System, -- Kalman Filter, -- GPS outages, -- Noisy Input Gaussian Process Regression, -- Hybrid predictor, -- Time series; Navigation -- Periodicals; 623.8905
• DOI: 10.1017/S0373463317000819
• Languages: English
• ISSNs: 0373-4633

Abstract

Abstract : A Position and Orientation System (POS) integrating an Inertial Navigation Systems (INS) and the Global Positioning System (GPS) is a key component of remote sensing motion compensation. It can provide reliable and high-frequency high-precision motion information using a Kalman Filter (KF) during GPS availability. However, the performance of a POS significantly degrades during GPS outages. To maintain reliable POS outputs, this paper proposes a new hybrid predictor based on modelling the nonlinear time-series data-driven INS-errors using Noisy Input Gaussian Process Regression (NIGPR), which takes the input noise into account. The proposed approach is used to learn the nonlinear INS-errors model when GPS signals are available. When GPS outages occur, it starts to predict the observation measurement, and then feeds it to a KF as a virtual update to estimate all the INS errors. The proposed approach is verified in a real airplane, which combines a POS and Synthetic Aperture Radar (SAR). Experimental results show that the proposed approach significantly improves the performance of the POS, with improvements more than 90% better than a KF and 10% better than a Gaussian Process Regression (GPR/KF) combination during various GPS outages.