scholarly journals Application of Updated Sage–Husa Adaptive Kalman Filter in the Navigation of a Translational Sprinkler Irrigation Machine

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1269 ◽  
Author(s):  
Kenan Liu ◽  
Wuyun Zhao ◽  
Bugong Sun ◽  
Pute Wu ◽  
Delan Zhu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Sprinkler Irrigation ◽  
The Self ◽  
Maximum Deviation ◽  
Navigation Systems ◽  
Adaptive Kalman Filter ◽  
Average Deviation ◽  
Positioning Accuracy ◽  
Promising Development

Autonomous navigation for agricultural machinery has broad and promising development prospects. Kalman filter technology, which can improve positioning accuracy, is widely used in navigation systems in different fields. However, there has not been much research performed into navigation for sprinkler irrigation machines (SIMs). In this paper, firstly, a self-developed SIM is introduced. Secondly, the kinematics model is established on the platform of the self-developed SIM, and the updated Sage–Husa adaptive Kalman filter, which is an accurate and real-time self-adaptive filtering algorithm, is applied in the navigation of the SIM with the aim of improving the positioning accuracy. Finally, experiment verifications were carried out, and the results show that the self-developed SIM has good navigation performance. Besides this, the influence of abnormal observations on the positioning accuracy of the system can be restrained by using the updated Sage–Husa adaptive Kalman filter. After using the updated Sage–Husa adaptive Kalman filter for the SIM, the maximum deviation between the SIM and the predetermined path is 0.18 m, and the average deviation is 0.08 m; these deviations are within a reasonable range. This proves that the updated Sage–Husa adaptive Kalman filter is applicable for the navigation of sprinkler irrigation machines.

scholarly journals A new adaptive Kalman filter for navigation systems of carrier-based aircraft

2021 ◽  
Author(s):  
Lifei Zhang ◽  
Shaoping Wang ◽  
Maria Sergeevna Selezneva ◽  
Konstantin Avenirovich Neysipin
Keyword(s):  
Kalman Filter ◽  
Navigation Systems ◽  
Adaptive Kalman Filter

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.

scholarly journals An Intelligent Adaptive Kalman Filter for Integrated Navigation Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 213306-213317
Author(s):  
Fei Yan ◽  
Sheng Li ◽  
Enze Zhang ◽  
Qingwei Chen
Keyword(s):  
Kalman Filter ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter

scholarly journals UAV Swarm Navigation Using Dynamic Adaptive Kalman Filter and Network Navigation

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5374
Author(s):  
Jingjuan Zhang ◽  
Wenxiang Zhou ◽  
Xueyun Wang
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Integrated System ◽  
Adaptive Kalman Filter ◽  
Positioning Accuracy ◽  
Distance Information ◽  
Noise Covariance Matrix ◽  
Position Errors ◽  
Uav Swarm ◽  
Noise Covariance

Aiming to improve the positioning accuracy of an unmanned aerial vehicle (UAV) swarm under different scenarios, a two-case navigation scheme is proposed and simulated. First, when the Global Navigation Satellite System (GNSS) is available, the inertial navigation system (INS)/GNSS-integrated system based on the Kalman Filter (KF) plays a key role for each UAV in accurate navigation. Considering that Kalman filter’s process noise covariance matrix Q and observation noise covariance matrix R affect the navigation accuracy, this paper proposes a dynamic adaptive Kalman filter (DAKF) which introduces ensemble empirical mode decomposition (EEMD) to determine R and adjust Q adaptively, avoiding the degradation and divergence caused by an unknown or inaccurate noise model. Second, a network navigation algorithm (NNA) is employed when GNSS outages happen and the INS/GNSS-integrated system is not available. Distance information among all UAVs in the swarm is adopted to compensate the INS position errors. Finally, simulations are conducted to validate the effectiveness of the proposed method, results showing that DAKF improves the positioning accuracy of a single UAV by 30–50%, and NNA increases the positioning accuracy of a swarm by 93%.

Correction of autonomous navigation systems using the Kalman filter

2019 ◽  
Author(s):  
K. A. Neusypin ◽  
M. S. Selezneva ◽  
Truong Ngoc Huong ◽  
T. Yu. Tsibizova
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Navigation Systems

scholarly journals Autonomous Navigation and Path Tracking Control on Field Roads in Hilly Areas

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Yunwu Li ◽  
Hongwei Sun ◽  
Dexiong Liu ◽  
Junjie Xu ◽  
Mingfeng Wang
Keyword(s):  
Control Strategy ◽  
Navigation System ◽  
Tracking Control ◽  
Autonomous Navigation ◽  
Path Tracking ◽  
Horizontal Direction ◽  
Maximum Deviation ◽  
Integrated Navigation ◽  
Average Deviation ◽  
Neural Network Controller

Hilly areas necessitate a field road vehicle with high automation to reduce the amount of labor required to transport agricultural products and to increase productivity. In this paper, an adaptive integrated navigation method (combining global navigation satellite system (GNSS) and inertial navigation system (INS)) and path tracking control strategy of field road vehicles are studied in view of the problems of frequent GNSS outages and high automatic control precision requirement in hilly areas. An indirect Kalman filter (KF) is designed for the GNSS/INS information fusion. A modified method for calculating the KF adaptive factor is proposed to effectively suppress the divergence of the KF and a threshold judgement method to abandon the abnormal GNSS measurement is proposed to deal with GNSS interruptions. To achieve automated driving, a five-layer fuzzy neural network controller, which takes the lateral deviation, heading deviation, and path curvature as input and the steering angle as output, is proposed to control vehicle autonomous tracking of the navigation trajectory accurately. The proposed system was evaluated through simulation and experimental tests on a field road. The simulation results showed that the adjusted KF fusion algorithm can effectively reduce the deviation of a single GNSS measurement and improve the overall accuracy. The test results showed the maximum deviation of the actual travel trajectory from the expected trajectory of the vehicle in the horizontal direction was 12.2 cm and the average deviation was 5.3 cm. During GNSS outages due to obstacles, the maximum deviation in the horizontal direction was 12.7 cm and the average deviation was 6.1 cm. The results show that the designed GNSS/INS integrated navigation system and trajectory tracking control strategy can control a vehicle automatically while driving along a field road in a hilly area.

Near Space Pseudolite Navigation System Design and High-Performance Filtering Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 931-935
Author(s):  
She Sheng Gao ◽  
Wen Hui Wei ◽  
Li Xue
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Particle Filtering ◽  
Autonomous Navigation ◽  
Unscented Kalman Filter ◽  
Navigation Systems ◽  
Filtering Algorithm ◽  
Near Space ◽  
Autonomous Navigation System

This paper analyzes the defects of satellite navigation systems that exist in positioning and precision-guided weapons and pointes out the advantages and military needs of pseudolite. The autonomous navigation nonlinear mathematical model of Near Space Pseudolite SINS/CNS/SAR autonomous navigation system is established. Based on the merits of fading filter, robust adaptive filtering and particle filter, we propose a fading adaptive Unscented Particle Filtering algorithm. The proposed filtering algorithm is applied to SINS/CNS/SAR autonomous navigation system and conducted simulation calculation with the Unscented Kalman filter and particle filter comparison. The results show that the new algorithm that is proposed meets the needs of pseudolite autonomous navigation, and the navigation accuracy is significantly higher than the Unscented Kalman filter and particle filter algorithm.

scholarly journals Performance analysis of GNSS/INS loosely coupled integration systems under GNSS signal blocking environment

2020 ◽  
Vol 206 ◽  
pp. 02013
Author(s):  
Mengke Wang ◽  
Peidong Yu ◽  
Yunzhi Li
Keyword(s):  
Autonomous Navigation ◽  
Inertial Navigation System ◽  
Satellite System ◽  
Sampling Frequency ◽  
Navigation Satellite ◽  
Navigation Systems ◽  
Post Processing ◽  
Positioning Accuracy ◽  
Loosely Coupled ◽  
Global Navigation Satellite

Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) are the most widely used navigation systems at present. Aiming at the limitations of a single system application, this paper uses kalman filter to fuse the pose information provided by GNSS and INS, respectively. GNSS has the characteristics of being easily affected by the environment but with high absolute positioning accuracy. INS has the characteristics of high sampling frequency and autonomous navigation, but the error accumulates with time. Combining the advantages of the two systems to achieve the purpose of obtaining higher-precision pose information. In addition, aiming at the problem that GNSS/INS integration cannot provide continuous, stable and reliable navigation solutions under the GNSS signal blocking environment, a smoothing post-processing algorithm for GNSS/INS integration is studied. Through experimental verification, this algorithm can effectively improve the pose accuracy under GNSS signal blocking environment.

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