A Fault Tolerant SINS/GPS/CNS Integrated Navigation Scheme Realized through Federated Kalman Filter

2013 ◽  
Vol 332 ◽  
pp. 104-110 ◽  
Author(s):  
Muhammad Ushaq ◽  
Fang Jian Cheng ◽  
Ali Jamshaid
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Research Work ◽  
Inertial Navigation ◽  
Satellite Navigation ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Satellite Navigation System ◽  
Celestial Navigation ◽  
Federated Kalman Filter

The complementary characteristics of the Strapdown Inertial Navigation System (SINS) and external non-inertial navigation aids like Global Positioning System (GPS) and Celestial Navigation System (CNS) make the integrated navigation system an appealing and cost effective solution for various applications. SINS exhibits position errors owing to errors in initialization of the inertial measurement unit (IMU) and the inherent accelerometer biases and gyroscope drifts. SINS also suffer from diverging azimuth errors and an exponentially increasing vertical channel error. Pitch and roll errors also exhibit unbounded growth with time. To mitigate this behavior of SINS, periodic corrections are opted for through measurements from external non-inertial navigation aids. These corrections can be in the form of position fixing, velocity fixing and attitude fixing from external aids like GPS, GLONASS (Russian Satellite Navigation System), BEIDU(Chinese Satellite Navigation System) and Celestial Navigation Systems (CNS) etc. In this research work GPS and CNS are used as external aids for SINS and the navigation solutions of all three systems (SINS, GPS and CNS) are fused using Federated Kalman Filter (FKF). The FKF differs from the conventional Central Kalman Filter (CKF) because each measurement is processed in Local Filters (LFs), and the results are combined in a Master Filter (MF). FKF is a partitioned estimation method that uses a two stage data processing scheme, in which the outputs of sensor related LFs are subsequently combined by a large MF. Each LF is dedicated to a separate sensor subsystem, and uses data from the common reference such as SINS. The SINS acts as a cardinal system in the combination, and its data is also available as measurement input for the master filter. In this research work, information from the GPS and the CNS are dedicated to the corresponding LFs. Each LF provides its solutions to the master filter all information is fused together forming a global solution. Simulation for the proposed architecture has validated the effectiveness of the scheme, by showing the substantial precision improvement in the solutions of position, velocity and attitude as compared to the pure SINS or any other standalone system.

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.

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.

Investigation of the positioning accuracy for the integrated navigation system at the measurement signals interruption

2021 ◽  
Author(s):  
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Forecast Accuracy ◽  
Satellite Navigation ◽  
Integrated Navigation ◽  
Positioning Accuracy ◽  
Satellite Navigation System ◽  
Integrated Navigation System ◽  
Mobile Carrier

An integrated navigation system as part of an inertial navigation system corrected by signals from a satellite navigation system is researched. The integrated navigation system is installed on a mobile carrier is arranged. The organization of the experimental study, the design of the stand used to install the equipment on a mobile carrier, and the measurement processing technique are considered. When the signals of the satellite system disappear, signal prediction algorithms are used. The results of assessing the positioning accuracy of the integrated navigation system in case of discontinuities in the reception of navigation signals, assessing the forecast accuracy by using the presented algorithms and conclusions drawn from the analysis of the results are presented. Keywords inertial navigation system; satellite navigation system; predictive model; positioning accuracy; trends; self-organization; identification

A Fault Tolerant Integrated Navigation Scheme Realized through Online Tuning of Weighting Factors for Federated Kalman Filter

2013 ◽  
Vol 446-447 ◽  
pp. 1078-1085 ◽  
Author(s):  
Muhammad Ushaq ◽  
Fang Jian Cheng
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
High Speed ◽  
Fault Tolerant ◽  
Angular Rate ◽  
Inertial Navigation ◽  
Integrated Navigation ◽  
Short Term ◽  
Weighting Factors ◽  
Federated Kalman Filter

Strapdown Inertial navigation (SINS) is a highly reliable navigation system for short term applications. SINS functions continuously, less hardware failures, renders high speed navigation solutions ranging from 50 Hz to 1000 Hz and exhibits low short-term errors. It provides efficient attitude, angular rate, acceleration, velocity and position solutions. But, the accuracy of SINS solution vitiates with time as the sensor (gyros & accelerometers) errors are integrated through the navigation equations. Average navigation grade SINS are capable of providing effective stand-alone navigation for shorter duration (few minutes) applications Stand-alone SINS capable of providing solutions for applications exceeding 10 minutes duration, are generally highly expensive ($0.1M to $2.0M). To cope with this limitation, a cost effective solution is the integrated navigation system wherein the unboundedly growing errors of SINS are contained with the help of external non-inertial navigation aids like GPS, Celestial Navigation System (CNS), Odometer, Doppler radars etc. The efficient methodology for integrated or multi-sensory navigation is the Federated Kalman Filter (FKF) scheme. In FKF architecture, a reference SINS solution is integrated independently with each of the aiding navigation systems in a bank of local Kalman filters. There are a number of different ways in which the local filter outputs may be combined to produce an integrated navigation solution. The no-reset, fusion-reset, zero-reset, and cascaded versions of federated integration have been used by different researcher and navigators over the years. All different schemes of FKF have certain pros and cons. Fusion-reset method although nearly optimal is less fault tolerant while no-resent scheme renders highly fault tolerant solutions but with sub-optimal solutions and compromised precision. To enhance the fault tolerance ability of fusion-reset scheme of FKF, additional parameters called weighting factors are introduced to tune the contribution of each local filter in the final data fusion. The presented scheme has been found nearly optimal and expressively fault tolerant.

The Analysis of Positioning Performance and DOP Value Based on BDS/GPS Integrated Navigation Satellite System

2014 ◽  
Vol 654 ◽  
pp. 181-186 ◽  
Author(s):  
Wei Lin Yuan ◽  
Yan Ma ◽  
Hua Bo Sun
Keyword(s):  
Navigation System ◽  
Satellite Navigation ◽  
Satellite System ◽  
Asia Pacific ◽  
Positioning System ◽  
Navigation Satellite ◽  
Integrated Navigation ◽  
Satellite Navigation System ◽  
Integrated Navigation System ◽  
Beidou Navigation Satellite System

The integrated positioning system increases the visible number of single satellite navigation system and improve the DOP value of single satellite navigation system. In accordance with the construction plan, BeiDou Navigation Satellite System (BDS) has started providing continuous passive positioning, navigation and timing service in the most parts of the Asia-Pacific In this paper, DOP value of GPS, BDS and the integrated navigation system are analyzed theoretically. The improvement of DOP value of GPS which resulted from present-running BDS navigation satellites is calculated by GPS/BDS observational data. The conclusions that GPS/BDS integrated navigation system will be able to improve the positioning accuracy and have useful references for the navigation and positioning application are also obtained.

Application of Improved Unscented Kalman Filter to AUV Based on Terrain-Aided Strapdown Inertial Navigation System

2013 ◽  
Vol 389 ◽  
pp. 758-764 ◽  
Author(s):  
Qi Wang ◽  
Dong Li ◽  
Zi Jia Zhang ◽  
Chang Song Yang
Keyword(s):  
Kalman Filter ◽  
Information Fusion ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Unscented Kalman Filter ◽  
Autonomous Underwater Vehicles ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Integrated Navigation ◽  
Strapdown Inertial Navigation

To improve the navigation precision of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Improved Unscented Kalman Filter (IUKF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and improved UKF 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 IUKF method is able to greatly improve the long-time navigation precision, relative to the traditional information fusion method.

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