期刊
IEEE SENSORS JOURNAL
卷 20, 期 23, 页码 14423-14435出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2020.3007892
关键词
Support vector machines; Global navigation satellite system; Sensors; Data integration; Land vehicles; Noise reduction; MEMS; wavelet transform; SVM; GNSS outages; GNSS; INS integration
资金
- Fund of the Shanxi Key Laboratory of Integrated and Intelligent Navigation [SKLIIN-20180106]
- Natural Science Foundation of Shanxi Province [2019JQ-936]
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.
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