The integration of the inertial navigation system (INS) and global navigation satellite system (GNSS) is suited for localisation and navigation applications, such as aircrafts, land vehicles and ships. The primary challenge is for navigation system to achieve accurate and reliable navigation solution during GNSS outages. This paper presents an observation prediction methodology for INS/GNSS bridging GNSS outages, which combines partial least squares regression (PLSR) and Gaussian process regression (GPR) to model the INS/GNSS observations and enable a Kalman filter to estimate INS errors. The performance of proposed PLSR/GPR prediction methodology was validated through four GNSS outages taken on flight experiment data, including diverse manoeuvre conditions. The experiment results demonstrate that remarkable performance enhancements are achieved through applying the proposed PLSR/GPR prediction methodology into INS/GNSS integration.

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Current Motion Compensation (MOCO) methods using Inertial Navigation System (INS)/Global Positioning System (GPS) integrated systems have provided an important advance in Synthetic Aperture Radar (SAR) imagery, but most of these methods only work well over a short imaging period. With the development of high-resolution SAR that provides image gathering over long periods, the need for higher levels of INS/GPS performance than normally available is desired. The higher requirement of INS/GPS for SAR MOCO is two-fold: (1) the accurate knowledge of location information, and (2) the smoothness of relative change in navigation error. In this paper, we design an INS/GPS architecture with dual-filter correction to obtain accurate absolute velocity and position measurement information with smooth low relative error noise over a long image gathering period. Real SAR data experimental results show that the proposed method effectively improves the MOCO performance of INS/GPS with long SAR imaging periods, in which the SAR azimuth resolution reaches 1·45 m, which is very close to the design value of 1 m.

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