GPS World, June 2017

Checking the Accuracy of an Inertial Based Pedestrian Navigation System with a Drone By Marcin Uradziński Hang Guo and Clifford Mugnier 58 GPS WORLD WWW GPSWORLD COM JUNE 2017 WITH RICHARD B LANGLEY IM WALKING HERE S atellite navigation systems have achieved great success in personal positioning applications Nowadays GNSS is an essential tool for outdoor navigation but locating a users position in degraded and denied indoor environments is still a challenging task During the past decade methodologies have been proposed based on inertial sensors for determining a persons location to solve this problem One such solution is a personal pedestrian dead reckoning PDR system which helps in obtaining a seamless indoor outdoor position Built in sensors measure the acceleration to determine pace count and estimate the pace length to predict position with heading information coming from angular sensors such as magnetometers or gyroscopes PDR positioning solutions find many applications in security monitoring personal services navigation in shopping centers and hospitals and for guiding blind pedestrians Several dead reckoning navigation algorithms for use with inertial measurement units IMUs have been proposed However these solutions are very sensitive to the alignment of the sensor units the inherent instrumental errors and disturbances from the ambient environment problems that cause accuracy to decrease over time In such situations additional sensors are often used together with an IMU such as ZigBee radio beacons with position estimated from received signal strength In this article we present a PDR indoor positioning system we designed tested and analyzed It is based on the pace detection of a foot mounted IMU with the use of extended Kalman filter EKF algorithms to estimate the errors accumulated by the sensors PDR DESIGN AND POSITIONING METHOD Our plan in designing a pedestrian positioning system was to use a high rate IMU device strapped onto the pedestrians shoe together with an EKF based framework The main idea of this project was to use filtering algorithms to estimate the errors biases accumulated by the IMU sensors The EKF is updated with velocity and angular rate measurements by zero velocity updates ZUPTs and zero angular rate updates ZARUs separately detected when the pedestrians foot is on the ground Then the sensor biases are compensated with the estimated FIGURE 1 IMU sensor and setup errors Therefore the frequent use of ZUPT and ZARU measurements consistently bounds many of the errors and as a result even relatively low cost sensors can provide useful navigation performance The PDR framework developed in a Matlab environment consists of five algorithms Initial alignment that calculates the initial attitude with the static data of accelerometers and magnetometers during the first few minutes IMU mechanization algorithm to compute the navigation parameters position velocity and attitude Pace detection algorithm to determine when the foot is on the ground that is when the velocity and angular rates of the IMU are zero ZUPT and ZARU which feed the EKF with the measured errors when pacing is detected EFK estimation of the errors providing feedback to the IMU mechanization algorithm INITIAL ALIGNMENT OF IMU SENSOR The initial alignment of an IMU sensor is accomplished in two steps leveling and gyroscope compassing Leveling refers to getting the roll and pitch using the acceleration and gyroscope compassing refers to obtaining heading using the angular rate However the bias and noise of gyroscopes are larger than the value of the Earths rotation rate for the