A Novel 3D Pedestrian Navigation Method for a Multiple Sensors-Based Foot-Mounted Inertial System

Abstract

In this paper, we present a novel method for 3D pedestrian navigation of foot-mounted inertial systems by integrating a MEMS-IMU, barometer, and permanent magnet. Zero-velocity update (ZUPT) is a well-known algorithm to eliminate the accumulated error of foot-mounted inertial systems. However, the ZUPT stance phase detector using acceleration and angular rate is threshold-based, which may cause incorrect stance phase estimation in the running gait pattern. A permanent magnet-based ZUPT detector is introduced to solve this problem. Peaks extracted from the magnetic field strength waveform are mid-stances of stance phases. A model of peak-peak information and stance phase duration is developed to have a quantitative calculation method of stance phase duration in different movement patterns. Height estimation using barometer is susceptible to the environment. A height difference information aided barometer (HDIB) algorithm integrating MEMS-IMU and barometer is raised to have a better height estimation. The first stage of HDIB is to distinguish level ground/upstairs/downstairs and the second stage is to calculate height using reference atmospheric pressure obtained from the first stage. At last, a ZUPT-based adaptive average window length algorithm (ZUPT-AAWL) is proposed to calculate the true total travelled distance to have a more accurate percentage error (TTDE). This proposed method is verified via multiple experiments. Numerical results show that TTDE ranges from 0.32% to 1.04% in both walking and running gait patterns, and the height estimation error is from 0 m to 2.35 m.

Indoor vehicle tracking with a smart MEMS sensor

“…However, in a sensor designed for 2D navigation in the industrial environment, the measurement error from bias change can be decreased with a technique called zero-velocity update (ZUPT [17]). This technique uses periods of time at which the vehicle is not moving to re-calibrate the sensor (update the sensor bias) and is used for enhancing the performance of automotive GPS/INS navigation systems ( [12,18,19]), as well as indoor pedestrian tracking ( [20][21][22]). Since a common movement pattern of industrial AGV includes stops and idle times either for control reliability ( [23]) or because of resource constraints ( [24,25]), the sensor can be re-calibrated during normal operation of the vehicle using this solution.…”

LSS-RM: Using Multi-Mounted Devices to Construct a Lightweight Site-Survey Radio Map for WiFi Positioning

“…The two different coordinate systems are transferred through the rotation matrix . Details of how to use accelerometer and magnetometer readings to calculate the rotation matrix can be found in Reference [ 17 ].…”

“…Steps of IEZ can be found in References [ 16 , 17 ]. An experiment was conducted to validate the IEZ-INS algorithm.…”

“…The stance-phase detector with a fixed threshold had limited performance. In Reference [ 17 ], we developed a MAG-ZUPT method to estimate the stance phase of running using magnetic-field strength. In Reference [ 51 ], we developed an adaptive-threshold method of walking and running stance-phase detection.…”

“…With the triaxial accelerometer and gyroscope readings, a zero-velocity update (ZUPT) algorithm is developed to measure velocity errors in the stance phase of a gait cycle [ 13 , 14 ]. ZUPT estimates pseudo measurements into the extended Kalman filter (EKF) navigation-error corrector, which allows the EKF to correct velocity errors during each gait cycle, breaking the cubic-in-time error growth and replacing it with an error accumulation that is linear with the number of steps [ 15 , 16 , 17 ]. This kind of foot-mounted inertial navigation system is called IEZ-INS and IEZ is short for the first letters of INS, EKF, and ZUPT.…”