A Review of Multisensor Fusion Methodologies for Aircraft Navigation Systems

2005 ◽  
Vol 58 (3) ◽  
pp. 405-417 ◽  
Author(s):  
David J. Allerton ◽  
Huamin Jia
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Navigation System ◽  
Review Paper ◽  
Fault Tolerant ◽  
Navigation Systems ◽  
System Architectures ◽  
Advantages And Disadvantages ◽  
Aircraft Navigation ◽  
Fusion Methods

This paper reviews currently existing fault-tolerant navigation system architectures and data fusion methods used in the design and development of integrated aircraft navigation systems and also compares their advantages and disadvantages. Four fault-tolerant navigation system architectures are reviewed and the associated Kalman filter architectures and algorithms are discussed. These techniques have been used in most integrated aircraft navigation systems. The aim of this review paper is to provide a guide for navigation system designers to develop future aircraft multisensor navigation systems.

Algorithm for detecting and isolating failures of the linear Kalman filter

2020 ◽  
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
High Efficiency ◽  
Detection Accuracy ◽  
Navigation Systems ◽  
Chi Square ◽  
Satellite Navigation System ◽  
Aircraft Navigation ◽  
Algorithmic Solution ◽  
Chi Square Test

The aircraft navigation complex consisting of the inertial (ANC) and satellite (SNS) navigation systems is considered. An algorithm for the detection accuracy and stability increasing at the failures of the aircraft navigation complex in the mode of joint operation of the ANS and SNS, as well as in the absence of a signal from the SNS is proposed. In the linear Kalman filter the Chi-square test is used. The results of mathematical modeling have shown the high efficiency of the algorithmic solution. Keywords aircraft; inertial navigation system; satellite navigation system; linear Kalman filter; fault tolerance; Chi-square test

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

scholarly journals Design of Accurate Navigation System by Integrating INS and GPS modules

2019 ◽  
Vol 11 (4) ◽  
pp. 139-154
Author(s):  
M. RAJA ◽  
Gaurav ASTHANA ◽  
Ajay SINGH ◽  
Ashna SINGHAL ◽  
Pallavi LAKRA
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
External Information ◽  
Navigation Systems ◽  
Final Solution ◽  
Long Term Stability ◽  
Noise Characteristics ◽  
Aircraft Navigation ◽  
Satellite Signal

Navigation has a huge application in aviation and aircraft automatic approach. Two widely used navigation systems are Global position System (GPS) and Inertial Navigation System (INS). Triangulation method used to determine the aircrafts location by GPS, speed whereas an INS, with the aid of gyroscope and accelerometer, estimates the location, velocity and alignment of an aircraft. Aircraft navigation is a complex task and using only one of the above navigation systems results in inaccurate and insufficient data. GPS stops working when satellite signal is not received, susceptible to interfere occasionally has high noise content, and has a low bandwidth, INS system requires external information for initialization has long-term drift errors. Certain errors like ionosphere interference, clock error, orbital error, position error, etc. might arise and disrupt the navigation process. In order to outrun the limitations of the above two systems and counter the errors, both INS and GPS can be integrated and used to attain more smooth, accurate and faster aircraft attitude estimates, as they have complementary strengths and limitations. GPS is stable for a long period and can act as an independent navigation system whereas INS is not susceptible to interference and signal losses has high radio bandwidth and works well for short intervals of time. In order to get accurate and precise attitude estimation, calculation of the parameters at different altitude using both systems is done; furthermore the comparison and contrast between the results is performed, measured quantities are transformed between various frames like longitudinal to rolling, calculation and elimination of errors is done producing the final solution. Because of integrated GPS and INS, the navigation system exhibits robustness, higher bandwidth, better noise characteristics, and long-term stability.

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

Increasing the Resistance of GPS Receivers by Using a Fuzzy Smart Estimator in Weak Signal Conditions

2020 ◽  
Vol 73 (5) ◽  
pp. 991-1013
Author(s):  
M. A. Farhad ◽  
M. R. Mosavi ◽  
A. A. Abedi ◽  
K. Mohammadi
Keyword(s):  
Kalman Filter ◽  
Simulated Data ◽  
Real Data ◽  
Navigation Systems ◽  
Weak Signal ◽  
Signal Tracking ◽  
Advantages And Disadvantages ◽  
Vector Tracking ◽  
Gnss Receivers ◽  
Satellite Navigation Systems

Global satellite navigation systems (GNSS) are nowadays used in many applications. GNSS receivers experience limitations in receiving weak signals in a degraded environment. Hence, tracking weak GNSS signals is a topic of interest to researchers in this field. Different methods have been proposed to address this issue, each of which has advantages and disadvantages. In this paper, a method based on the vector tracking method is proposed for weak signal tracking. This method has been developed based on a strong Kalman filter instead of the extended Kalman filter used in conventional vector tracking methods. In order to adjust important parameters of this filter, the fuzzy method is used. The results of tests performed with both simulated data and real data demonstrate that the proposed method performs better than previous ones in weak signal tracking.

scholarly journals Robust Data Fusion of UAV Navigation Measurements with Application to the Landing System

2020 ◽  
Vol 12 (23) ◽  
pp. 3849
Author(s):  
Kirill Kolosov ◽  
Alexander Miller ◽  
Boris Miller
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Measurement Errors ◽  
High Sensitivity ◽  
False Alarm Probability ◽  
Navigation Systems ◽  
Fusion Algorithm ◽  
Chi Square ◽  
Filtering Algorithm ◽  
Anomalous Errors

To perform precise approach and landing concerning an aircraft in automatic mode, local airfield-based landing systems are used. For joint processing of measurements of the onboard inertial navigation systems (INS), altimeters and local landing systems, the Kalman filter is usually used. The application of the quadratic criterion in the Kalman filter entails the well-known problem of high sensitivity of the estimate to anomalous measurement errors. During the automatic approach phase, abnormal navigation errors can lead to disaster, so the data fusion algorithm must automatically identify and isolate abnormal measurements. This paper presents a recurrent filtering algorithm that is resistant to anomalous errors in measurements and considers its application in the data fusion problem for landing system measurements with onboard sensor measurements—INS and altimeters. The robustness of the estimate is achieved through the combined use of the least modulus method and the Kalman filter. To detect and isolate failures the chi-square criterion is used. It makes possible the customization of the algorithm in accordance with the requirements for false alarm probability and the alarm missing probability. Testing results of the robust filtering algorithm are given both for synthesized data and for real measurements.

Multisensor Integration Methods in the Development of a Fault-Tolerant Train Navigation System

2003 ◽  
Vol 56 (3) ◽  
pp. 385-398 ◽  
Author(s):  
Ahmad Mirabadi ◽  
Felix Schmid ◽  
Neil Mort
Keyword(s):  
Navigation System ◽  
Doppler Radar ◽  
Fault Tolerant ◽  
Critical Role ◽  
Fault Detection And Isolation ◽  
Sensor Data ◽  
Estimation Methods ◽  
Navigation Systems ◽  
Positioning Systems ◽  
Train Positioning

Onboard train positioning (navigation) plays a vital and safety critical role in advanced Automatic Train Control (ATC) and Automatic Train Protection (ATP) systems. Such onboard systems are also essential for moving block signalling and control systems for railways. The application of multi-sensor fusion algorithms to the vehicle navigation field has made it possible to create inexpensive and accurate positioning systems, which will satisfy the railways' requirements. The state estimation methods involved in Kalman filtering have proved to be some of the most effective techniques in multi-sensor data fusion. A multi-sensor navigation system is introduced in this paper to address the shortcomings of the existing train positioning systems. The proposed system utilizes the Global Positioning System (GPS), Doppler radar, gyroscopes, tachometers, digital maps and balises. In order to provide fault detection and isolation capabilities, a hierarchical structure is proposed for the multi-sensor integration system in which different combinations of navigation systems would function. Several data integration nodes, including DR/GPS, DR/Balise, and DR/GPS/Balise, are studied in more detail and their performances are evaluated.

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.

Enhanced Accuracy Navigation Solutions Realized through SINS/GPS Integrated Navigation System

2013 ◽  
Vol 332 ◽  
pp. 79-85
Author(s):  
Outamazirt Fariz ◽  
Muhammad Ushaq ◽  
Yan Lin ◽  
Fu Li
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Position Errors ◽  
Strapdown Inertial Navigation

Strapdown Inertial Navigation Systems (SINS) displays position errors which grow with time in an unbounded manner. This degradation is due to the errors in the initialization of the inertial measurement unit, and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Improvement to this unbounded growth in errors can be made by updating the inertial navigation system solutions periodically with external position fixes, velocity fixes, attitude fixes or any combination of these fixes. The increased accuracy is obtained through external measurements updating inertial navigation system using Kalman filter algorithm. It is the basic requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertial Navigation System (SINS), Global Positioning System (GPS) is presented using a centralized linear Kalman filter.

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