A modified adaptive Kalman filter for real-time applications

1991 ◽  
Vol 27 (1) ◽  
pp. 149-154 ◽  
Author(s):  
G. Chen ◽  
C.K. Chui
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Adaptive Kalman Filter ◽  
Real Time Applications

An adaptive Kalman filter based on variance component estimation for a real-time ZTD solution

2019 ◽  
Vol 54 (1) ◽  
pp. 89-121 ◽  
Author(s):  
Xu Yang ◽  
Guobin Chang ◽  
Qianxin Wang ◽  
Shubi Zhang ◽  
Ya Mao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Variance Component ◽  
Variance Component Estimation ◽  
Adaptive Kalman Filter ◽  
Component Estimation

The Adaptive Kalman Filter Based on Improved Variable Oblivion Factor Least Square Arithmetic

2011 ◽  
Vol 181-182 ◽  
pp. 124-129
Author(s):  
Yu Song
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Transfer Matrix ◽  
Least Square ◽  
Flood Forecast ◽  
Adaptive Kalman Filter ◽  
Dam Reservoir ◽  
State Transfer

Adopting improved variable oblivion factor least square arithmetic to real-time amend Kalman’s state transfer matrix, we put Maple Dam reservoir flood forecast real-time adjustment for example, then apply and compare with other ways. The result shows this arithmetic is preferable.

scholarly journals DANAE++: A Smart Approach for Denoising Underwater Attitude Estimation

2021 ◽  
Author(s):  
Paolo Russo ◽  
Fabiana Di Ciaccio ◽  
Salvatore Troisi
Keyword(s):  
Deep Learning ◽  
Kalman Filter ◽  
Real Time ◽  
Denoising Autoencoder ◽  
Orientation Estimation ◽  
Underwater Robots ◽  
Underwater Environment ◽  
Real Time Applications ◽  
And Performance ◽  
Vehicle Positioning

One of the main issues for underwater robots navigation is represented by the accurate vehicle positioning, which heavily depends on the orientation estimation phase. The systems employed to this scope are affected by different noise typologies, mainly related to the sensors and to the irregular noise of the underwater environment. Filtering algorithms can reduce their effect if opportunely configured, but this process usually requires fine techniques and time. This paper presents DANAE++, an improved denoising autoencoder based on DANAE, which is able to recover Kalman Filter IMU/AHRS orientation estimations from any kind of noise, independently of its nature. This deep learning-based architecture already proved to be robust and reliable, but in its enhanced implementation significant improvements are obtained both in terms of results and performance. In fact, DANAE++is able to denoise the three angles describing the attitude at the same time, and that is verified also on the estimations provided by the more performing Extended KF. Further tests could make this method suitable for real-time applications on navigation tasks.

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.

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.

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