Abstract
Smartphone-based pedestrian localization is still a challenge in deep urban canyons, where GNSS signals suffer from the degrading of signal transmission, multipath effects, and NLOS reception. This paper presents a comprehensive pedestrian localization scheme based on PDR and GNSS observations at different times, using the internal sensors equipped in the smartphone, including GNSS raw measurements (pseudo-range, carrier phase), internal MEMS sensor (including the gyroscope, accelerometer, magnetometer, and barometer). The core algorithm utilizes historical effective satellite observation and PDR to solve pedestrian position, exploiting both PDR and GNSS's complementary properties. The proposed approach can improve accuracy and continuity and solve the problem of missing data, such as without satellite coverage. Besides, we design a Kalman filter model to reduce systematic errors and correct PDR in real-time to decrease the cumulative error of PDR. To evaluate the proposed pedestrian localization scheme's performance, we perform experiments in a typical urban canyon with dense foliage and tall buildings and compare it with the different state-of-the-art approaches. The comparison and analysis of the overall positioning performance show that the method proposed in this paper can provide a better localization scheme, and the RMS value of positioning error is improved from 51.7m (GNSS only) to 9.6m.
Highlights
As Micro-Electro-Mechanical System (MEMS) has been rapidly developed, various kinds of smart-devices for consumers, such as smartphones, smart-watches, tablet computers, are equipped with Global Navigation Satellite System (GNSS) receivers, and inertial measurement unit (IMU), which provide new and cheap approaches to urban localization.Commonly the first choice for localization is the GNSS in urban because of its widest coverage
We propose a comprehensive approach for fusing GNSS observations and Pedestrian Dead Reckoning (PDR), which only uses the smartphone’s internal sensors without external assistance
We propose a pedestrian localization algorithm based on PDR and GNSS observations at different times, using the smartphone’s internal sensors
Summary
As Micro-Electro-Mechanical System (MEMS) has been rapidly developed, various kinds of smart-devices for consumers, such as smartphones, smart-watches, tablet computers, are equipped with Global Navigation Satellite System (GNSS) receivers, and inertial measurement unit (IMU), which provide new and cheap approaches to urban localization.Commonly the first choice for localization is the GNSS in urban because of its widest coverage. Due to the degrading of signal transmission, notorious multipath effects, and non-line-of-sight (NLOS) reception [1], GNSS cannot achieve highly accurate positioning performance in all areas, especially in urban canyon. Many researchers have proposed combining other positioning methods, such as Pedestrian Dead Reckoning (PDR) [2], visual localization, 3D Maps [3]. External signal-aided [4], with GNSS to improve pedestrian navigation capability and stability. According to the unique characteristics of human walking gait, PDR can achieve the user’s position by using accelerometers to detect the pedestrian’s traveling steps, estimate step length, and use gyro-meters and magnetic estimate heading between every two consecutive steps. Combining PDR and GNSS will be a highly complementary system
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