Radar Update (RUPT): A Pedestrian Navigation System with Enhanced Trajectory Performance

Dai, Yuquan; Li, Kemeng; Chai, Jin; Xiao, Zhenyu; Yan, Bo; He, Yi; Peng, Wenyu

doi:10.1109/iscas51556.2021.9401236

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…The INS aiding comprises not only the ZUPT technique for each foot separately but also the utilization of the distance between the two feet. The latter aiding is implemented by a pure mathematical evaluation of distance constraints [36] or by using additional measurements of sensors such as a pair of cameras [37], radar [38], or ultrasound units [39]. An alternative variation of this dual IMU configuration is to instead wear one unit on the chest to form a local AHRS.…”

Section: Research Topics On Zupt-based Pedestrian Navigationmentioning

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: Research Topics On Zupt-based Pedestrian Navigationmentioning

confidence: 99%

Reevaluation of Algorithmic Basics for ZUPT-Based Pedestrian Navigation

2022

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“…Wang proposed an adaptive inequality foot-to-foot constraint, which determined the maximum bound of inter-foot distance [12]. Y Dai et al proposed a Radar Update Position Technology (RUPT), which used dual foot-mounted IMUs and a millimeter-wave radar [13]. Using a ranging sensor to directly measure the foot-to-foot distance is another method.…”

Section: Introductionmentioning

confidence: 99%

A tri-IMUs pedestrian positioning system based on single-lower-limb kinematic constraints

Zhang

Guo

et al. 2023

Meas. Sci. Technol.

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Single-sensor zero-velocity-update (ZUPT)-aided pedestrian inertial navigation system (PINS) is a conventional method. However, the single-sensor systematic error still causes imprecise estimation of step length and high drift of orientation. In this study, three magnetometers and inertial measurement units (MIMUs) are mounted on single-leg toe, shank, and thigh, respectively. Gaits are divided into two phases: stance and swing. Based on the biomechanical characteristics of a human lower limb, a velocity-position estimation algorithm is proposed. The positions of the three sensors are fused by dynamic geometric relationships, which are formulated through the natural lower-limb model and attitude estimation. A velocity-difference constraint is proposed to suppress the divergence of velocity, and Coriolis-based velocity correction is designed to observe velocities of the toe, shank, and thigh at the stance phase. A MAHONY-linear-Kalman framework separately estimates attitudes, velocities, and positions to reduce calculations. The proposed method is compared with foot-mounted, shank-mounted, and thigh-mounted ZUPT-aided PINS as well as a dual-sensor foot-to-foot algorithm through experiments. In the flat terrain experiment, compared with the errors of the Shank-mounted INS, the Thigh-mounted INS, the Foot-mounted INS, the Foot-to-Foot Constraint Method, the root mean squared error (RMSE) of the proposed method can be reduced by 40.34%, 71.85%, 29.53%, and 7.08%,, respectively. In the slope experiment, compared with the other four methods, the root mean squared error (RMSE) of the proposed method can be reduced by 80%, 86.74%, 67.54%, and 61.86%, respectively. In the natural terrain experiment, compared with the other four methods, the root mean squared error (RMSE) of the proposed method can be reduced by 81.63%, 96.27%, 70%, and 60.87% respectively. The results show that the proposed method greatly suppresses the positioning and orientation errors in different scenes.

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Prior Kinematic Information Fusion for Pedestrian Localization With Toe-Heel-Shank MIMUs

Wang

et al. 2023

IEEE Trans. Ind. Electron.

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Radar Update (RUPT): A Pedestrian Navigation System with Enhanced Trajectory Performance

Cited by 4 publications

References 27 publications

Reevaluation of Algorithmic Basics for ZUPT-Based Pedestrian Navigation

Reevaluation of Algorithmic Basics for ZUPT-Based Pedestrian Navigation

A tri-IMUs pedestrian positioning system based on single-lower-limb kinematic constraints

Prior Kinematic Information Fusion for Pedestrian Localization With Toe-Heel-Shank MIMUs

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