scholarly journals Kalman Filtering Algorithm for Integrated Navigation System in Unmanned Aerial Vehicle

2020 ◽  
Vol 1575 ◽  
pp. 012034
Author(s):  
Wenfa Lv
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Kalman Filtering ◽  
Navigation System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Filtering Algorithm ◽  
Aerial Vehicle ◽  
Kalman Filtering Algorithm

Application of INS/Optical Flow/Magnetometer/Barometer Integrated Navigation System in Unmanned Aerial Vehicle

2017 ◽  
Vol 54 (2) ◽  
pp. 022801
Author(s):  
李涛 Li Tao ◽  
梁建琦 Liang Jianqi ◽  
闫浩 Yan Hao ◽  
朱志飞 Zhu Zhifei ◽  
唐军 Tang Jun
Keyword(s):  
Optical Flow ◽  
Unmanned Aerial Vehicle ◽  
Navigation System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Aerial Vehicle

A Simaplied Adaptive Kalman Filtering Algorithm

2014 ◽  
Vol 1037 ◽  
pp. 378-382
Author(s):  
Lei Bo ◽  
Xin Yan Zhu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Unmanned Aerial Vehicle ◽  
Kalman Filtering ◽  
Likelihood Estimation ◽  
Simplified Model ◽  
Filtering Algorithm ◽  
Aerial Vehicle ◽  
Kalman Filtering Algorithm ◽  
The Moment

The adaptive Kalman filtering algorithm was adopted in the online estimate of navigation state of unmanned aerial vehicle (UAV) as the simplified model often used. At the moment, the alogorithms those usually applied in this territory are not perfect. Analysed the adaptive Kalman filtering based on Maximum-Likelihood Estimation and Sage-Husa Kalman filtering, take advantage the characteristics of residue, choose the estimation windows, a simplified adaptive Kalman filtering algorithm was gived.

A Measurement-Based Robust Adaptive Kalman Filtering Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 3773-3779 ◽  
Author(s):  
Yan Hong Chang ◽  
Hai Zhang ◽  
Qi Fan Zhou
Keyword(s):  
Kalman Filtering ◽  
Measurement Noise ◽  
Statistical Characteristics ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Measurement Systems ◽  
Filtering Algorithm ◽  
Gps Measurement ◽  
Kalman Filtering Algorithm ◽  
Robust Adaptive

In the case that the accuracy of standard kalman filter (SKF) declines when the noise statistical characteristics are unknown or changing, a measurement-based adaptive kalman filtering algorithm (MAKF) is presented. Based on the contrastive analysis of measurement characteristics of different measurement systems, MAKF is put forward to estimate adaptively the measurement noise variance R by co-difference measurement sequences. Simulation is performed by applying this algorithm to the GPS/INS integrated navigation system, the results show that MAKF can track the GPS measurement noise in real time on condition that the GPS measurement noise is unknown or changing, and the filtering accuracy and robustness are superior to those of SKF and an improved Sage-Husa adaptive kalman filtering algorithm.

Application of Federated Filter to AUV Based on Terrain-Aided SINS

2014 ◽  
Vol 711 ◽  
pp. 338-341 ◽  
Author(s):  
Qi Wang ◽  
Cheng Shan Qian ◽  
Zi Jia Zhang ◽  
Chang Song Yang
Keyword(s):  
Kalman Filtering ◽  
Navigation System ◽  
Autonomous Underwater Vehicles ◽  
Inertial Navigation ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Filtering Method ◽  
Long Time ◽  
Federated Filtering ◽  
Strapdown Inertial Navigation

To improve the navigation precision and reliability of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Federated Filter (FF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and Federated Filtering method is applied to the information fusion. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional Kalman filtering methods. The experiment results suggest that the Federated Filtering method is able to improve the long-time navigation precision and reliability, relative to the traditional Kalman Filtering method.

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