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