scholarly journals Estimation of UAV Position with Use of Smoothing Algorithms

2017 ◽  
Vol 24 (1) ◽  
pp. 127-142 ◽  
Author(s):  
Piotr Kaniewski ◽  
Rafał Gil ◽  
Stanisław Konatowski
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Position Estimation ◽  
Fixed Interval ◽  
Smoothing Algorithm ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Gnss Receiver ◽  
On Line

Abstract The paper presents methods of on-line and off-line estimation of UAV position on the basis of measurements from its integrated navigation system. The navigation system installed on board UAV contains an INS and a GNSS receiver. The UAV position, as well as its velocity and orientation are estimated with the use of smoothing algorithms. For off-line estimation, a fixed-interval smoothing algorithm has been applied. On-line estimation has been accomplished with the use of a fixed-lag smoothing algorithm. The paper includes chosen results of simulations demonstrating improvements of accuracy of UAV position estimation with the use of smoothing algorithms in comparison with the use of a Kalman filter.

Model aided airborne integrated navigation system based on an improved square-root unscented H∞ filter

2018 ◽  
Vol 41 (5) ◽  
pp. 1290-1300
Author(s):  
Jieliang Shen ◽  
Yan Su ◽  
Qing Liang ◽  
Xinhua Zhu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Statistical Characteristics ◽  
Small Scale ◽  
Strong Nonlinearity ◽  
Square Root ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System

An inertial navigation system (INS) aided with an aircraft dynamic model (ADM) is developed as a novel airborne integrated navigation system, coping with the absence of a global navigation satellite system. To overcome the shortcomings of the conventional linear integration of INS/ADM based on an extended Kalman filter, a nonlinear integration method is proposed. Fast-update ADM makes it possible to utilize a direct filtering method, which employs nonlinear INS mechanics as system equations and a nonlinear ADM as observation equations, substituting the indirect filtering based on linear error equations. The strong nonlinearity generally calls for an unscented Kalman filter to accomplish the fusion process. Dealing with the model uncertainty, the inaccurate statistical characteristics of the noise and the potential nonpositive definiteness of the covariance matrix, an improved square-root unscented H∞ filter (ISRUHF) is derived in the paper, in which the robust factor [Formula: see text] is further expanded into a diagonal matrix [Formula: see text], to improve the accuracy and robustness of the integrated navigation system. Corresponding simulations as well as real flight tests based on a small-scale fixed-wing aircraft are operated and ISRUHF shows superiority compared with the commonly used fusion algorithm.

scholarly journals The Design and Implementation of an Inertial GNSS Odometer Integrated Navigation System Based on a Federated Kalman Filter for High-Speed Railway Track Inspection

2021 ◽  
Vol 11 (11) ◽  
pp. 5244
Author(s):  
Xinchun Zhang ◽  
Ximin Cui ◽  
Bo Huang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
High Speed ◽  
Measurement Accuracy ◽  
Railway Track ◽  
Filter Method ◽  
Integrated Navigation ◽  
High Speed Railway ◽  
Track Geometry ◽  
Integrated Navigation System

The detection of track geometry parameters is essential for the safety of high-speed railway operation. To improve the accuracy and efficiency of the state detector of track geometry parameters, in this study we propose an inertial GNSS odometer integrated navigation system based on the federated Kalman, and a corresponding inertial track measurement system was also developed. This paper systematically introduces the construction process for the Kalman filter and data smoothing algorithm based on forward filtering and reverse smoothing. The engineering results show that the measurement accuracy of the track geometry parameters was better than 0.2 mm, and the detection speed was about 3 km/h. Thus, compared with the traditional Kalman filter method, the proposed design improved the measurement accuracy and met the requirements for the detection of geometric parameters of high-speed railway tracks.

Studies of the integrated navigation system correction in the absence of a satellite signal

2021 ◽  
Author(s):  
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Operation Mode ◽  
Satellite System ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Correction Signal ◽  
Satellite Signal ◽  
Navigation Information

The schemes of navigation systems correction are considered. The operation mode of the aircraft during navigation is analyzed. An adaptive modification of the linear Kalman filter is used to correct the navigation information. An algorithm for predicting a correction signal based on a neural network in the event of a loss of a SNS correction signal is formed. Experimental results show the effectiveness of the algorithm. Keywords aircraft; inertial navigation system; satellite system; Kalman filter; neural networks; genetic algorithm

scholarly journals A New Robust Kalman Filter for SINS/DVL Integrated Navigation System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 51386-51395 ◽  
Author(s):  
Li Luo ◽  
Yonggang Zhang ◽  
Tao Fang ◽  
Ning Li
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Robust Kalman Filter

scholarly journals A Fault-Tolerant Filtering Algorithm for SINS/DVL/MCP Integrated Navigation System

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaosu Xu ◽  
Peijuan Li ◽  
Jian-juan Liu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Fault Tolerant ◽  
Doppler Velocity ◽  
Observation Data ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Data Set ◽  
Integrated Navigation System ◽  
Short Period

The Kalman filter (KF), which recursively generates a relatively optimal estimate of underlying system state based upon a series of observed measurements, has been widely used in integrated navigation system. Due to its dependence on the accuracy of system model and reliability of observation data, the precision of KF will degrade or even diverge, when using inaccurate model or trustless data set. In this paper, a fault-tolerant adaptive Kalman filter (FTAKF) algorithm for the integrated navigation system composed of a strapdown inertial navigation system (SINS), a Doppler velocity log (DVL), and a magnetic compass (MCP) is proposed. The evolutionary artificial neural networks (EANN) are used in self-learning and training of the intelligent data fusion algorithm. The proposed algorithm can significantly outperform the traditional KF in providing estimation continuously with higher accuracy and smoothing the KF outputs when observation data are inaccurate or unavailable for a short period. The experiments of the prototype verify the effectiveness of the proposed method.

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