Zero‐Velocity Update Aided Pedestrian Inertial Navigation

Zero Velocity Update (ZUPT) is an essential method of error control in Stapdown Inertial Navigation System (SINS), which is extensively used because of its cheapness and efficiency. ZUPT uses the output of velocity error of SINS when the carrier is parking, to update the errors of other items in SINS. This method can improve the position and direction precisions of SINS. Kalman filter is chosen as the method of ZUPT to correct the velocity and position errors in SINS in this article. The method of ZUPT based on Kalman filter is applied to the vehicle experiment. The results of the vehicle experiment indicate that the ZUPT based on Kalman filter is efficient and powerful in error control, and the Kalman filter designed based on SINS is proper.

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Applications of zero-velocity detector and Kalman filter in zero velocity update for inertial navigation system

Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference ◽

10.1109/cgncc.2014.7007449 ◽

2014 ◽

Cited By ~ 3

Author(s):

Li Xiaofang ◽

Mao Yuliang ◽

Xie Ling ◽

Chen Jiabin ◽

Song Chunlei

Keyword(s):

Kalman Filter ◽

Navigation System ◽

Inertial Navigation System ◽

Inertial Navigation ◽

Zero Velocity

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An Indoor Localization Method Based on the Combination of Indoor Map Information and Inertial Navigation with Cascade Filter

Journal of Sensors ◽

10.1155/2021/7621393 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yushuai Zhang ◽

Jianxin Guo ◽

Feng Wang ◽

Rui Zhu ◽

Liping Wang

Keyword(s):

Indoor Localization ◽

Inertial Navigation ◽

Detection Algorithm ◽

Position Estimation ◽

Position Error ◽

Indoor Navigation ◽

Bayesian Filter ◽

Pedestrian Navigation ◽

Zero Velocity ◽

Average Position

The specific objective of this study is to propose a low-cost indoor navigation framework with nonbasic equipment by combining inertial sensors and indoor map messages. The proposed pedestrian navigation framework consists of a lower filter and an upper filter. In the lower filter which is designed based on the Kalman filter, the adaptive zero velocity detection algorithm is used to detect the zero velocity interval at different motion speeds, and then, zero velocity update is applied to rectify the inertial navigation solutions’ errors. In the upper filter which is designed based on the nonrecursive Bayesian filter, the map matching method with nonrecursive Bayesian filter is adopted to fuse the map prior information and the lower filter estimation results to correct the errors of navigation. The position estimation presented in this study achieves an average position error of 0.53 m compared to the ZUPT-aided inertial navigation system (INS) method under different motion states. The proposed pedestrian navigation algorithm achieves an average position error of 0.54 m as compared to the ZUPT-aided INS method among the different tested distances. The proposed framework simplifies the indoor positioning system under multiple motion speed conditions by ensuring the accuracy and stability property. The effectiveness and accuracy of the proposed framework are experimentally verified in various real-world scenarios.

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Pedestrian inertial navigation with gait phase detection assisted zero velocity updating

2009 4th International Conference on Autonomous Robots and Agents ◽

10.1109/icara.2000.4803999 ◽

2009 ◽

Cited By ~ 2

Author(s):

Young Soo Suh ◽

Sangkyung Park

Keyword(s):

Inertial Navigation ◽

Phase Detection ◽

Zero Velocity ◽

Gait Phase

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INS/Odometer Integration: Positional Approach

Giroskopiya i Navigatsiya ◽

10.17285/0869-7035.0066 ◽

2021 ◽

Vol 29 (2) ◽

pp. 110-125

Author(s):

A.A. Golovan ◽

Keyword(s):

Navigation System ◽

Inertial Navigation System ◽

Inertial Sensors ◽

Inertial Navigation ◽

Dead Reckoning ◽

Strapdown Inertial Navigation System ◽

Integrated Navigation ◽

Integrated Navigation System ◽

Zero Velocity ◽

Strapdown Inertial Navigation

The problem of a strapdown inertial navigation system (SINS) integration with an odometer as part of an integrated navigation system is considered. The odometer raw measurement is considered as an increment of the distance traveled along the odometer ‘measuring’ axis. Models of the integration solution components for the case of threedimensional navigation are presented, among which are the models of inertial autonomous and kinematic odometer dead reckoning (DR), models of relevant error equations, the model of SINS position aiding based on the odometer DR data and using GNSS position and velocity, wherever possible. The models comprise objective components, which do not depend on the type of the inertial sensors used and their accuracy grade, and variable components, which take into account the properties of the navigation sensors used. The integration does not require zero velocity updates, known as ZUPT correction, which are commonly used in navigation application.

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