Implementation of a MEMS-Based GNSS/INS Integrated Scheme Using Supported Vector Machine for Land Vehicle Navigation

Abstract

The performance of the integrated global navigation satellite system (GNSS) and inertial navigation system (INS) has been proven to be more accurate, reliable, and continuous than stand-alone systems. The development of micro-electronic mechanical system (MEMS) enables inertial measurement unit (IMU) to meet the low-cost and small-size requirements of vehicular navigation. However, the stochastic error characteristics of the inertial sensors and the instability caused by the GNSS signal outages pose a threat to the MEMS-based GNSS/INS land vehicle navigation system. Within this context, we propose the following two-step GNSS/INS integrated architecture at two levels: 1) enhancing the signal-to-noise ratio (SNR) of MEMS-INS raw measurements utilizing a hybrid denoising algorithm with wavelet transform and support vector machine (SVM) and 2) improving the positioning accuracy by a SVM-based data fusion approach which could predict the accumulated error of the MEMS inertial sensors during the GNSS outages. A rotation platform experiment and a field test were carried out, which suggests that the proposed method can effectively eliminate the stochastic errors of MEMS-IMU, and significantly improve the overall positioning accuracy in land vehicle navigation.