Pedestrian localization in urban areas is often inaccurate, because GNSS signals could be blocked, attenuated or reflected by existing obstacles. The use of a combination of GNSS could reduce the drawbacks related to the scenario. Anyway, for scenarios particularly hostile, the multi-constellation approach is insufficient and the integration of multiple sensors is a possible solution. PDR is a technique based on inertial sensors, which exploits the characteristics of human gait. In PDR, the measurements from inertial sensors on a pedestrian are processed, with the aim of detecting the presence of steps, estimating their length and updating the pedestrian heading. In this paper, a PDR algorithm, processing measurements from accelerometers and gyros embedded in a smartphone, is implemented; a pedestrian carries the smartphone in texting pose. To reduce the PDR inherent drift, a loosely coupled integration architecture with GNSS is carried out; the data-fusion core is an extended Kalman filter, with position and yaw errors as inputs. The GNSS-based yaw is derived from velocity, which is usually obtained from Doppler measurements, with an accuracy, in good visibility condition, of cm/s order. In this work, a velocity derived from TDCP, with accuracy in the same condition of mm/s order, is used too. The yaw derived from TDCP allows significant improvements of PDR/GNSS performance. The adopted GNSS systems are GPS and Glonass, and a RAIM technique is applied to pseudorange, Doppler and carrier-phase measurements, in order to reduce the effect of outliers, which are very frequent in urban scenario.

Pedestrian localization with PDR supplemented by GNSS

Crocetto N.
2019

Abstract

Pedestrian localization in urban areas is often inaccurate, because GNSS signals could be blocked, attenuated or reflected by existing obstacles. The use of a combination of GNSS could reduce the drawbacks related to the scenario. Anyway, for scenarios particularly hostile, the multi-constellation approach is insufficient and the integration of multiple sensors is a possible solution. PDR is a technique based on inertial sensors, which exploits the characteristics of human gait. In PDR, the measurements from inertial sensors on a pedestrian are processed, with the aim of detecting the presence of steps, estimating their length and updating the pedestrian heading. In this paper, a PDR algorithm, processing measurements from accelerometers and gyros embedded in a smartphone, is implemented; a pedestrian carries the smartphone in texting pose. To reduce the PDR inherent drift, a loosely coupled integration architecture with GNSS is carried out; the data-fusion core is an extended Kalman filter, with position and yaw errors as inputs. The GNSS-based yaw is derived from velocity, which is usually obtained from Doppler measurements, with an accuracy, in good visibility condition, of cm/s order. In this work, a velocity derived from TDCP, with accuracy in the same condition of mm/s order, is used too. The yaw derived from TDCP allows significant improvements of PDR/GNSS performance. The adopted GNSS systems are GPS and Glonass, and a RAIM technique is applied to pseudorange, Doppler and carrier-phase measurements, in order to reduce the effect of outliers, which are very frequent in urban scenario.
2019
978-1-5386-9473-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/418675
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