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Land Vehicle Navigation Using Low-Cost Integrated Smartphone GNSS Mems and Map Matching Technique

Abstract The demand for smartphone positioning has grown rapidly due to increased positioning accuracy applications, such as land vehicle navigation systems used for vehicle tracking, emergency assistance, and intelligent transportation systems. The integration between navigation systems is necessary to maintain a reliable solution. High-end inertial sensors are not preferred due to their high cost. Smartphone microelectromechanical systems (MEMS) are attractive due to their small size and low cost; however, they suffer from long-term drift, which highlights the need for additional aiding solutions using road network that can perform efficiently for longer periods. In this research, the performance of the Xiaomi MI 8 smartphone’s single-frequency precise point positioning was tested in kinematic mode using the between-satellite single-difference (BSSD) technique. A Kalman filter algorithm was used to integrate BSSD and inertial navigation system (INS)-based smartphone MEMS. Map matching technique was proposed to assist navigation systems in global navigation satellite system (GNSS)-denied environments, based on the integration of BSSD–INS and road network models applying hidden Marcov model and Viterbi algorithm. The results showed that BSSD–INS–map performed consistently better than BSSD solution and BSSD–INS integration, irrespective of whether simulated outages were added or not. The root mean square error (RMSE) values for 2D horizontal position accuracy when applying BSSD–INS–map integration improved by 29% and 22%, compared to BSSD and BSSD–INS navigation solutions, respectively, with no simulated outages added. The overall average improvement of proposed BSSD–INS–map integration was 91%, 96%, and 98% in 2D horizontal positioning accuracy, compared to BSSD–INS algorithm for six GNSS simulated signal outages with duration of 10, 20, and 30 s, respectively.