On the Influence of Sample Rate, Calibration, and Allan Variance Parameters on the Accuracy of ZUPT-Based Pedestrian Navigation with MEMS IMUs

Köhl, Michael; Gyorfi, Benedikt; Wagner, Jörg

doi:10.1109/iss55898.2022.9926342

Cited by 3 publications

(1 citation statement)

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“…In both cases, the results did not change the assessment of the current literature: ZARU is applicable only in pronounced resting phases of the IMU, and a significant advantage of the Gauss-Markov modelling is not visible. Further research should address the IMU sampling rate and IMU calibration (as already done by the authors [75]) the usage of other data fusion techniques like an unscented Kalman filter or like smoothing, advanced detection of ZUPT phases, dual IMU configurations etc.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Reevaluation of Algorithmic Basics for ZUPT-Based Pedestrian Navigation

2022

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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.

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Section: Conclusion and Recommendationsmentioning

confidence: 99%