Reevaluation of Algorithmic Basics for ZUPT-Based Pedestrian Navigation

Abstract

During the last 10 to 15 years, pedestrian navigation based on zero velocity updates (ZUPT) has become very popular. One of the main reasons for this is the increasing availability of small, low-cost inertial measurement units. However, the processing of the data from these units for pedestrian navigation is almost exclusively based on algorithmic features that originate from classical inertial navigation with highgrade sensors. In addition, the historical background of the ZUPT approach presupposes also sensors with high accuracy. This leads to the problem that neither the ZUPT approach nor the algorithmic features mentioned are consistent with the accuracy level of the inertial measurement units used normally for pedestrian navigation. Therefore, the question arises of whether the usual algorithmic basics and numerical procedures employed by ZUPT-based pedestrian navigation are adequate. Supported by a literature review showing the state of the art, this study investigates the effect of basics such as the system states and the usage of Runge-Kutta method on the navigation accuracy from pedestrian inertial data aided by ZUPT. To this end, a comparative processing of a well-known, published dataset of a short walk and of own data from the authors using different data fusion algorithms was employed. The important results of this study are that the oftenused omission of inertial sensor biases cannot be recommended, that a continuous-discrete Kalman filter in combination with a Runge-Kutta algorithm performs better than traditional filter configurations, and that total navigation states are an interesting alternative to classical navigation error-states.

Center-of-Rotation-Augmented Kalman Filter for Adaptive Attitude Reference System

“…The noise standard deviation for the augmented δr b is assumed as 10 −3 m/ √ Hz. Some literatures suggest that methods such as Runge-Kutta ensure a more accurate discretization than the method we chose in equation ( 21) [40]. Yet, we use equation ( 21) instead for three reasons: in the context of a low-grade IMU (such as the IMU used for our experiments in Section IV), the numerical error from the discretization is far smaller than errors from other sources; the discretization error is kept small with a small time-step, t [40]; the Runge-Kutta method is computationally heavier than the chosen method.…”

A Secure ZUPT-Aided Indoor Navigation System Using Blockchain in GNSS-Denied Environments

“…Several studies have investigated the performance of ZUPT-aided pedestrian INS and other related systems, including the characterization of foot-mounted ZUPT-aided INSs [ 5 ], pseudo-ZUPT re-detection with double-threshold ZUPT for better performance [ 6 ], reevaluation of algorithmic basics for ZUPT-based pedestrian navigation [ 7 ], scenario-dependent ZUPT-aided pedestrian INS with sensor fusion [ 8 ], and smoothing techniques for ZUPT-aided INSs [ 9 ]. Other studies have focused on specific aspects of ZUPT-aided pedestrian INS, such as stance-phase detection [ 10 ], step detection using foot-mounted permanent magnets [ 11 ], estimation errors due to IMU noises [ 12 ], and the impact of IMU mounting position on accuracy [ 13 ].…”