Abstract
How to effectively blend global navigation satellite system (GNSS) and inertial navigation system (INS) data to achieve an optimal solution in harsh environments has always been an urgent task. The main challenges for low-cost GNSS/INS integrated land vehicle navigation system are poor accuracy of GNSS observations in complex urban environments and time-growing position errors of stand-alone micro-electro mechanical system (MEMS)-based inertial sensors during GNSS outages. This paper aims to enhance the positioning accuracy and reliability of low-cost integrated system. To attain this, we propose a two-tier robust fusion scheme with different aspects: 1) The GNSS and INS information are fused through a support vector regression-based adapted Kalman filter (SVR-AKF), with which scaling factors are generated to tune the covariance parameters of KF. 2) The position errors of MEMS-INS during GNSS outages are predicted and compensated by modeling INS error characteristics utilizing an adaptive neuro fuzzy inference system (ANFIS) due to its effectiveness in dealing with the nonlinear and uncertainty problems. To verify the feasibility of the proposed methodology, experimental road tests were performed, which suggested that the proposed methodology can significantly improve the overall reliability and positioning performance of land vehicle navigation in GNSS-challenged environments.
Highlights
The integration of global navigation satellite system (GNSS) and inertial navigation system (INS) has been popular for land vehicle navigation for the last few decades, owing to their complementary error characteristics [1]
As the position accuracy of stand-alone INS is highly depended on the quality of inertial sensors, some researchers have studied the performance of integrated navigation with high-quality inertial measurement unit (IMU) [5]
The first method is a supported vector regression (SVR)-based adaptive Kalman filter, in which the status of the filter is timely assessed by monitoring the covariance of innovation sequence
Summary
The integration of global navigation satellite system (GNSS) and inertial navigation system (INS) has been popular for land vehicle navigation for the last few decades, owing to their complementary error characteristics [1]. GNSS can provide continuous, accurate positioning information with more than four available satellites [2], it is vulnerable to interferences, multipath effect of urban canyons, and block of signals [3]. With the progress of micro-electronic mechanical system (MEMS) technology, the integration of GNSS and MEMS-IMU has been successfully applied in the area of land vehicle navigation [6]–[8]. The poor accuracy of GNSS observations in urban canyons and rapidly deteriorating positioning errors of MEMS-INS in the absence of GNSS still pose threats to the conventional structure of MEMS-based integrated land vehicle navigation
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