An improved INS/PDR/UWB integrated positioning method for indoor foot-mounted pedestrians

Sensor Review ◽  
2019 ◽  
Vol 39 (3) ◽  
pp. 318-331 ◽  
Author(s):  
Qigao Fan ◽  
Jie Jia ◽  
Peng Pan ◽  
Hai Zhang ◽  
Yan Sun
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Integrated System ◽  
Positioning System ◽  
Adaptive Kalman Filter ◽  
Content Type ◽  
Positioning Method ◽  
Accuracy And Stability

Purpose The purpose of this paper is to relate to the real-time navigation and tracking of pedestrians in a closed environment. To restrain accumulated error of low-cost microelectromechanical system inertial navigation system and adapt to the real-time navigation of pedestrians at different speeds, the authors proposed an improved inertial navigation system (INS)/pedestrian dead reckoning (PDR)/ultra wideband (UWB) integrated positioning method for indoor foot-mounted pedestrians. Design/methodology/approach This paper proposes a self-adaptive integrated positioning algorithm that can recognize multi-gait and realize a high accurate pedestrian multi-gait indoor positioning. First, the corresponding gait method is used to detect different gaits of pedestrians at different velocities; second, the INS/PDR/UWB integrated system is used to get the positioning information. Thus, the INS/UWB integrated system is used when the pedestrian moves at normal speed; the PDR/UWB integrated system is used when the pedestrian moves at rapid speed. Finally, the adaptive Kalman filter correction method is adopted to modify system errors and improve the positioning performance of integrated system. Findings The algorithm presented in this paper improves performance of indoor pedestrian integrated positioning system from three aspects: in the view of different pedestrian gaits at different speeds, the zero velocity detection and stride frequency detection are adopted on the integrated positioning system. Further, the accuracy of inertial positioning systems can be improved; the attitude fusion filter is used to obtain the optimal quaternion and improve the accuracy of INS positioning system and PDR positioning system; because of the errors of adaptive integrated positioning system, the adaptive filter is proposed to correct errors and improve integrated positioning accuracy and stability. The adaptive filtering algorithm can effectively restrain the divergence problem caused by outliers. Compared to the KF algorithm, AKF algorithm can better improve the fault tolerance and precision of integrated positioning system. Originality/value The INS/PDR/UWB integrated system is built to track pedestrian position and attitude. Finally, an adaptive Kalman filter is used to improve the accuracy and stability of integrated positioning system.

scholarly journals A Robust Method of Vehicle Stability Accurate Measurement Using GPS and INS

2015 ◽  
Vol 15 (6) ◽  
pp. 294-303 ◽  
Author(s):  
Zhibin Miao ◽  
Hongtian Zhang ◽  
Jinzhu Zhang
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Vehicle Stability ◽  
Positioning System ◽  
Robust Method ◽  
Sideslip Angle ◽  
Yaw Rate ◽  
Global Positioning

Abstract With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) is a very practical method to get high-precision measurement data. Usually, the Kalman filter is used to fuse the data from GPS and INS. In this paper, a robust method is used to measure vehicle sideslip angle and yaw rate, which are two important parameters for vehicle stability. First, a four-wheel vehicle dynamic model is introduced, based on sideslip angle and yaw rate. Second, a double level Kalman filter is established to fuse the data from Global Positioning System and Inertial Navigation System. Then, this method is simulated on a sample vehicle, using Carsim software to test the sideslip angle and yaw rate. Finally, a real experiment is made to verify the advantage of this approach. The experimental results showed the merits of this method of measurement and estimation, and the approach can meet the design requirements of the vehicle stability controller.

Performance evaluation of real-time tightly-coupled GNSS PPP/MEMS-based inertial integration using an improved robust adaptive Kalman filter

2020 ◽  
Vol 14 (4) ◽  
pp. 413-430
Author(s):  
Abdelsatar Elmezayen ◽  
Ahmed El-Rabbany
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Integrated System ◽  
Adaptive Kalman Filter ◽  
Integration Algorithm ◽  
Positioning Accuracy ◽  
Precise Positioning ◽  
The Real ◽  
Tightly Coupled ◽  
Robust Adaptive

AbstractTypically, the extended Kalman filter (EKF) is used for tightly-coupled (TC) integration of multi-constellation GNSS PPP and micro-electro-mechanical system (MEMS) inertial navigation system (INS) to provide precise positioning, velocity, and attitude solutions for ground vehicles. However, the obtained solution will generally be affected by both of the GNSS measurement outliers and the inaccurate modeling of the system dynamic. In this paper, an improved robust adaptive Kalman filter (IRKF) is adopted and used to overcome the effect of the measurement outliers and dynamic model errors on the obtained integrated solution. A real-time IRKF-based TC GPS+Galileo PPP/MEMS-based INS integration algorithm is developed to provide precise positioning and attitude solutions. The pre-saved real-time orbit and clock products from the Centre National d’Etudes Spatials (CNES) are used to simulate the real-time scenario. The performance of the real-time IRKF-based TC GNSS PPP/INS integrated system is assessed under open sky environment, and both of simulated partial and complete GNSS outages through two ground vehicular field trials. It is shown that the real-time TC GNSS PPP/INS integration through the IRKF achieves centimeter-level positioning accuracy under open sky environments and decimeter-level positioning accuracy under GNSS outages that range from 10 to 60 seconds. In addition, the use of IRKF improves the positioning accuracy and enhances the convergence of the integrated solution in comparison with the EKF. Furthermore, the IRKF-based integrated system achieves attitude accuracy of 0.052°, 0.048°, and 0.165° for pitch, roll, and azimuth angles, respectively. This represents improvement of 44 %, 48 %, and 36 % for the pitch, roll, and azimuth angles, respectively, in comparison with the EKF-based counterpart.

Application of Extended Kalman Filter Algorithm in SDINS/GPS Integrated Inertial Navigation System

2013 ◽  
Vol 367 ◽  
pp. 528-535
Author(s):  
Otman Ali Awin
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Integrated System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Matlab Package ◽  
Noisy Measurements

This paper deals with the integrated navigation system based on fusion of data from Strap Down Inertial Navigation System (SDINS) and from Global Position System (GPS). In order to increase the accuracy and reliability of navigation algorithms, these two different systems are combined. The navigation system that be analyzed is basically of INS type while GPS corrective data are obtained less frequently and these are treated as noisy measurements in an extended Kalman filter scheme. The simulation of whole system (SDINS/GPS integrated system with Kalman filter) was modeled using MATLAB package, SIMULINK© tool. The proper choice of Kalman filter parameters had taken to minimize navigation errors for a typical medium range flight scenario (Simulated test trajectory and real trajectory of vehicle motion). A prototype of a SDINS installed on a moving platform in the laboratory to collected data by many experiments to verification our SIMULINK models.

scholarly journals An Improved Adaptive Kalman Filter for Underwater SINS/DVL System

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Di Wang ◽  
Xiaosu Xu ◽  
Lanhua Hou
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Doppler Velocity ◽  
Integrated Navigation ◽  
Test Results ◽  
Adaptive Kalman Filter ◽  
Main Challenge ◽  
Vehicle Test ◽  
Navigation Method

The main challenge of Strap-down Inertial Navigation System (SINS)/Doppler velocity log (DVL) navigation system is the external measurement noise. Although the Sage–Husa adaptive Kalman filter (SHAKF) has been introduced in the integrated navigation field, the precision and stability of the SHAKF are still the tricky problems to be overcome. The primary aim of this paper is to improve the precision and stability of underwater SINS/DVL system. To attain this, a SINS/DVL tightly integrated model is established, where beam measurements are used without transforming them to 3D velocity. The proposed improved SHAKF algorithm is based on variable sliding window estimation and fading filter. The simulations and vehicle test results demonstrate the effectiveness of the proposed underwater SINS/DVL tightly integrated navigation method based on the improved SHAKF. In addition, the position accuracy of the designed method outperforms that of the SHAKF method.

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