Integrated PDR/GNSS at Different Times for Pedestrian Localization in Urban Canyon

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.

Multi-Phase Fusion for Pedestrian Localization Using Mass-Market GNSS and MEMS Sensors

“…Similar to constructing the double-difference pseudorange using Equation (5) and Equation (7), we use Equation ( 6) to construct the double-difference carrier phase, as follows:…”

“…It should be noted that Equation ( 2) is the expression of the un-difference pseudorange multipath error, but we need to deal with the double-difference form in RTD. Let us review Equation (7); it can be expanded as follows:…”

“…Therefore, outdoor users cannot obtain satisfactory positioning results by relying on GNSS or MARG alone. To combine the advantages of different sensors and to improve the overall performance, sensor fusion has received extensive attention from scholars [ 5 , 6 , 7 ].…”