Improved robust Kalman filter for state model errors in GNSS-PPP/MEMS-IMU double state integrated navigation

The integration of Global Navigation Satellite System (GNSS) with Inertial Navigation Systems (INS) has been actively researched and widely applied as it can provide reliable positioning information continuously. In recent years, Micro Electro Mechanical Systems (MEMS) technology achieves rapid development and Micro Electro Mechanical Systems and Inertial Measurement Unit (MEMS-IMU) has aroused wide concern due to its excellent properties in some cases. However, the observations from MEMS-IMU are easy to be influenced by motion state and location environment because of its manufacturing process. It is not easy to judge whether gross errors are in the state model or the observation model by the widely adopted robust filter based on innovation. In this contribution, we present an improved robust filter with a double state model on the basis of the chi-square distribution of the square of the Mahalanobis distance. The vehicle motion model acts as the external constraint information and can be adopted to construct robust statistic with the results from INS mechanization. And then a robust factor was determined to adjust the observation noise covariance matrix. To evaluate the performance of this method, the simulation test and the field test based on locomotive platform of Nottingham Geospatial Institute (NGI) were carried out. According to the results, in the simulation test, the position improvements are 33%, 30% in the north and east directions; in the real test, the loosely and tightly coupled was adopted and the position accuracy can be improved by about 50-60% in the horizontal direction and the improvement of the pitch and the roll accuracy was lower than the azimuth accuracy due to poor observability and experimental scene which is of the characteristics of small elevation change. Therefore, the proposed robust filter could diminish the effect of the gross error from MEMS-IMU and enhance the integrated system. (C) 2021 COSPAR. Published by Elsevier B.V. All rights reserved.

Automated summary

The integration of GNSS with INS has been widely studied and applied for continuous reliable positioning. MEMS technology and MEMS-IMU have attracted attention for their properties, but can be influenced by motion and environment. An improved robust filter using a double state model and Chi-square distribution of Mahalanobis distance was proposed, enhancing the integrated system accuracy by about 50-60% in horizontal direction. This filter mitigates the influence of gross errors from MEMS-IMU.