Drift Reduction in Pedestrian Navigation System by Exploiting Motion Constraints and Magnetic Field

Abstract

Pedestrian navigation systems (PNS) using foot-mounted MEMS inertial sensors use zero-velocity updates (ZUPTs) to reduce drift in navigation solutions and estimate inertial sensor errors. However, it is well known that ZUPTs cannot reduce all errors, especially as heading error is not observable. Hence, the position estimates tend to drift and even cyclic ZUPTs are applied in updated steps of the Extended Kalman Filter (EKF). This urges the use of other motion constraints for pedestrian gait and any other valuable heading reduction information that is available. In this paper, we exploit two more motion constraints scenarios of pedestrian gait: (1) walking along straight paths; (2) standing still for a long time. It is observed that these motion constraints (called “virtual sensor”), though considerably reducing drift in PNS, still need an absolute heading reference. One common absolute heading estimation sensor is the magnetometer, which senses the Earth’s magnetic field and, hence, the true heading angle can be calculated. However, magnetometers are susceptible to magnetic distortions, especially in indoor environments. In this work, an algorithm, called magnetic anomaly detection (MAD) and compensation is designed by incorporating only healthy magnetometer data in the EKF updating step, to reduce drift in zero-velocity updated INS. Experiments are conducted in GPS-denied and magnetically distorted environments to validate the proposed algorithms.

Real-Time Calibration of Magnetometers Using the RLS/ML Algorithm

“…Currently, most magnetic and inertial measurement units (MIMU) based on the MEMS technology integrate three accelerometers, gyroscopes, and magnetometers each, assembled in three orthogonal directions. Due to the weak yaw observation of the conventional navigation systems, magnetometers are widely utilized for heading estimation based on the principle that the local magnetic field points to the north [1][2][3][4].…”

“…The magnetic disturbances can be classified into hard iron and soft iron disturbances. The hard iron disturbances are caused by ferromagnetic materials with a permanent magnetic field, e.g., magnets and speakers, which are time-invariant, whereas the soft iron disturbances are deflections or alterations in the existing magnetic field, which are caused by magnetized materials such as steel shield and batteries [2]. Moreover, magnetometers inherently possess non-orthogonality, scaling, and bias errors.…”

Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning

“…Some scholars also established the fourth error model of stride length estimation on the basis of predecessors, and discussed the systematic error and random error of Taylor expansion [19]. In order to reduce the drift error of inertial devices in indoor environment, researchers proposed a scheme based on acceleration and gyroscope output to detect pedestrian attitude [20]. In addition, the scheme can also correct the heading of walking in real time and suppress the accumulated error of inertial position to a certain extent.…”

An Infrastructure-Free Indoor Localization Algorithm for Smartphones

“…The Earth’s magnetic field [23] is omnipresent and susceptible to be affected by many factors, such as steel-reinforced concrete, metallic door frames, pillars, furniture, electronic equipment, appliances. These factors generate stable indoor magnetic anomalies that differ across locations.…”