Abstract
An enhanced pedestrian dead reckoning (PDR) based navigation algorithm, which uses two cascaded Kalman filters (TCKF) for the estimation of course angle and navigation errors, is proposed. The proposed algorithm uses a foot-mounted inertial measurement unit (IMU), waist-mounted magnetic sensors, and a zero velocity update (ZUPT) based inertial navigation technique with TCKF. The first stage filter estimates the course angle error of a human, which is closely related to the heading error of the IMU. In order to obtain the course measurements, the filter uses magnetic sensors and a position-trace based course angle. For preventing magnetic disturbance from contaminating the estimation, the magnetic sensors are attached to the waistband. Because the course angle error is mainly due to the heading error of the IMU, and the characteristic error of the heading angle is highly dependent on that of the course angle, the estimated course angle error is used as a measurement for estimating the heading error in the second stage filter. At the second stage, an inertial navigation system-extended Kalman filter-ZUPT (INS-EKF-ZUPT) method is adopted. As the heading error is estimated directly by using course-angle error measurements, the estimation accuracy for the heading and yaw gyro bias can be enhanced, compared with the ZUPT-only case, which eventually enhances the position accuracy more efficiently. The performance enhancements are verified via experiments, and the way-point position error for the proposed method is compared with those for the ZUPT-only case and with other cases that use ZUPT and various types of magnetic heading measurements. The results show that the position errors are reduced by a maximum of 90% compared with the conventional ZUPT based PDR algorithms.
Highlights
The personal navigation system (PNS) technology, which provides absolute or relative navigation information such as position and orientation of a user, is an emerging technology widely used for location-based services (LBS), health care systems, future soldier systems, and many other application areas [1]
Inc. consisting of three gyroscopes, three accelerometers, and three magnetic sensors, are used as the foot-mounted inertial measurement unit (IMU) and waist-mounted magnetic sensors
A pedestrian navigation algorithm based on a two cascaded Kalman filters (TCKF) was proposed
Summary
The personal navigation system (PNS) technology, which provides absolute or relative navigation information such as position and orientation of a user, is an emerging technology widely used for location-based services (LBS), health care systems, future soldier systems, and many other application areas [1]. The inertial navigation system (INS) approach, which uses an extended Kalman filter (EKF) and zero velocity updates (ZUPT) for estimating navigation and sensor error states, has been applied for indoor pedestrian navigation applications [13,14,15,16,17], and many other applications recently [18,19,20,21]. Because the zero velocity measurements are very strong and reliable measurements for estimating the velocity errors and biases of accelerometers, the travel distance is likely to be sufficiently accurate, which means that only the heading or yaw angle accuracy is a dominant factor for position accuracy in the INS-EKF-ZUPT navigation system. Cascaded Kalman Filter Architecture for Course Angle Error and Heading Estimation
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