Step Detection for ZUPT-Aided Inertial Pedestrian Navigation System Using Foot-Mounted Permanent Magnet

Norrdine, Abdelmoumen; Kasmi, Zakaria; Blankenbach, Jörg

doi:10.1109/jsen.2016.2585599

Cited by 101 publications

(50 citation statements)

References 20 publications

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“…This information is combined with the current position and velocity estimate to reset the velocity hence correct the state estimate. This technique called zero-velocity update (ZUPT) [10] helps to reduce the error growth. A similar approach adapted for the white cane can be found in [1].…”

Section: Related Workmentioning

confidence: 99%

Localization system in GPS-denied environments using radar and IMU measurements: Application to a smart white cane

Barra

Lesecq

Zarudniev

et al. 2019

2019 18th European Control Conference (ECC)

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This paper presents the development of a localization system in GPS-denied environments using an Inertial Measurement Unit (IMU) and a Pulse-Doppler radar. A ground speed estimation from radar measurements is first proposed. This estimation is combined with noisy measurements from an IMU in a Luenberger observer, allowing accurate deadreckoning. The methodology proposed provides short-term position of the sensors embedded in a white cane, the ultimate goal being obstacle detection through the computation of a model of the surroundings. The results show that this solution gives an error growth rate of the position estimation of 0.026m/s, which is a hundred times better than the one obtained with the naive double integration of the accelerometer data.

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Section: Related Workmentioning

confidence: 99%

Localization system in GPS-denied environments using radar and IMU measurements: Application to a smart white cane

Barra

Lesecq

Zarudniev

et al. 2019

2019 18th European Control Conference (ECC)

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“…en, according to the dead reckoning algorithm, the data measured can be transformed into attitude and position information [6,7]. However, the drift and integration errors of low-cost sensors greatly impact the nal positioning results [8,9]. e positioning accuracy in the pedestrian navigation and positioning system is greatly a ected by the attitude angle.…”

Section: Introductionmentioning

confidence: 99%

“…In [16], an optimal-enhanced Kalman filter is proposed, in which adaptive parameters are added to the covariance matrix to achieve accurate positioning of pedestrians within an enclosed environment. In [8,15,43,44], the EKF and zero velocity update are combined to eliminate heading angle drift, which is better than the zero velocity update only. In [45], Wang et al reconstruct the prior error covariance by mining the posterior sequence online to overcome the inaccurate calculation of the Kalman covariance.…”

Section: Introductionmentioning

confidence: 99%

Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter

Liu

et al. 2020

Complexity

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The Foot-mounted Inertial Pedestrian-Positioning System (FIPPS) based on the Micro-Inertial Measurement Unit (MIMU) is a good choice for the forest fire fighters when the Global Navigation Satellite System is unavailable. Zero Velocity Update (ZUPT) provides a solution for reducing cumulative positioning errors caused by the integral calculation of the inertial navigation. However, the performance of ZUPT is highly affected by the low accuracy and high noise of the MIMU. The accuracy of conventional ZUPT for attitude alignment is reduced by the zero offset of acceleration and the drift of a gyroscope during the standing phase. An initial alignment algorithm based on Adaptive Gradient Descent Algorithm (AGDA) is proposed. In the stepping phase, the extended Kalman filter (EKF) is often used to correct attitude and position in track estimation. However, the measurement noise of the EKF is influenced by the high-frequency acceleration and angular velocity. Thus, the accuracy of the attitude and position will decrease. A double-constrained extended Kalman filtering (DEKF) is proposed. An adaptive parameter positively correlated with the acceleration and angular velocity is set, and the measurement noise in the DEKF is adaptively adjusted. The performance of the proposed method is verified by implementing the pedestrian test trajectory using MPU-9150 MIMU manufactured by InvenSense. The results show that the attitude error of the AGDA is 33.82% less than that of the conventional GDA. The attitude error of DEKF is 21.70% less than that of the conventional EKF. The experimental results verify the effectiveness and applicability of the proposed method.

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“…Moreover these methods based on sliding windows do not allow to detect individual strides. A few methods of stance detection have been proposed in the literature by tuning thresholds to determine the start and the end of the phases [13], [14], [15], or using machine learning techniques on the frequency characteristics of the signals [16], [17]. These methods show good results when it is known that the pedestrian is walking but fail in a lot of real life situations.…”

Section: Introductionmentioning

confidence: 99%

Stride detection for pedestrian trajectory reconstruction: A machine learning approach based on geometric patterns

Beaufils

Chazal

Grelet

et al. 2017

2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Abstract-In this paper, a strides detection algorithm is proposed using inertial sensors worn on the ankle. This innovative approach based on geometric patterns can detect both normal walking strides and atypical strides such as small steps, side steps and backward walking that existing methods struggle to detect. It is also robust in critical situations, when for example the wearer is sitting and moving the ankle, while most algorithms in the literature would wrongly detect strides.

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Step Detection for ZUPT-Aided Inertial Pedestrian Navigation System Using Foot-Mounted Permanent Magnet

Cited by 101 publications

References 20 publications

Localization system in GPS-denied environments using radar and IMU measurements: Application to a smart white cane

Localization system in GPS-denied environments using radar and IMU measurements: Application to a smart white cane

Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter

Stride detection for pedestrian trajectory reconstruction: A machine learning approach based on geometric patterns

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