scholarly journals Enhanced Redundant Measurement-Based Kalman Filter for Measurement Noise Covariance Estimation in INS/GNSS Integration

2020 ◽  
Vol 12 (21) ◽  
pp. 3500
Author(s):  
Baoshuang Ge ◽  
Hai Zhang ◽  
Wenxing Fu ◽  
Jianbing Yang
Keyword(s):  
Navigation System ◽  
Autoregressive Process ◽  
Satellite System ◽  
Measurement Noise ◽  
Difference Sequence ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
The Difference

Adaptive Kalman filters (AKF) have been widely applied to the inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation system. However, the traditional AKF methods suffer from the problems of filtering instability or covariance underestimation, especially when the GNSS measurement disturbances occur. In this paper, an enhanced redundant measurement-based AKF is developed to improve the filtering performance. The scheme is based on the mutual difference sequence derived from the redundant measurement of INS. By using the mutual difference sequence, the measurement noise covariance can be estimated without being affected by the inaccuracy estimates, hence avoiding the risk of filtering divergence. In addition, the kernel density estimation is used to estimate the GNSS measurement noise’s probability density to detect whether the Gaussian properties of the measurement noise are maintained. When the noise statistics are far from Gaussian distribution, the difference sequence will be modeled as an autoregressive process using the Burg’s method. The real variance of the difference sequence can then be updated relying on the autoregressive model in order to avoid the covariance underestimation. A field experiment was carried out to evaluate the performance of the proposed method. The test results demonstrate that the proposed method can effectively mitigate the GNSS measurement disturbances and improve the accuracy of the navigation solution.

An Adaptive & Fault Tolerant SINS/GPS Integrated Navigation Scheme Robustified against Slowly Growing Errors in GPS Updates

2013 ◽  
Vol 390 ◽  
pp. 500-505 ◽  
Author(s):  
Muhammad Ushaq ◽  
Fang Jian Cheng ◽  
Jamshaid Ali
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Measurement Noise ◽  
Integrated Navigation ◽  
Random Errors ◽  
Actual Measurement ◽  
Integrated Navigation System ◽  
System Noise ◽  
Measurement Noise Covariance ◽  
Noise Covariance

The Strapdown Inertial Navigation System (SINS) renders excellent attitude, position and velocity solutions on short term basis, but when used as stand-alone navigation system, its accuracy deteriorates with the passage of time. On the other hand GPS has long-standing stability with a consistent precisiongenerally having only bounded random errors in position and velocity. Integrated navigation system is used to augment the complementary features of SINS and GPS. In integrated navigation system external fixes for position and/or velocity and/or attitude are used to contain the growing errors of SINS. Kalman filter is generally used as integration tool for integrated navigation system. Kalman filter algorithm is based on the assumptions that the system model and the measurement models are linear and the system random errors and measurement random errors are Gaussian in nature expressed with fixed covariances. But in real navigation systems these assumptions are seldom fulfilled and hence Kalman filter renders unsatisfactory results. Adaptive Kalman filter provides the solution to the problem by adjusting the system noise covariance and measurement noise covariance in real time in the light of actual measurement errors or actual dynamics of thevehicle. In this paper an innovation and residual based adaption of measurement noise covariance and system noise covariance is presented. The presented scheme has been applied on an SINS/GPS Integrated Navigation Systemand it has been validated that the scheme provide significantly better results as compared to standard Kalman filter on occurrence slowly growing errors as well as excessive random errors in GPS measurements.

Research and Design of MEMS SINS/GPS Integrated Navigation System Based on Adaptive Filter

2013 ◽  
Vol 341-342 ◽  
pp. 1048-1052
Author(s):  
Gao Wei Zhang ◽  
Xiao Yu Zhang ◽  
Chun Lei Song ◽  
Ting Ting Wang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Adaptive Estimation ◽  
Low Cost ◽  
Integrated Navigation ◽  
Variance Matrix ◽  
Integrated Navigation System ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
Research And Design

A MIMU/GPS integrated navigation system principle prototype is designed, and the structure of the system is introduced by different module. To handle the influence of Kalman filter parameters on system filtering performance (Including the system noise variance matrix Q and measurement noise covariance matrix R), adaptive estimation Kalman filter is designed. The test results show that satisfactory performance can be obtained using adaptive estimation techniques for the low-cost MIMU/GPS integrated navigation.

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 Multi-Radio Integrated Navigation System M&S Software Design for GNSS Backup under Navigation Warfare

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 188 ◽  
Author(s):  
Heyone Kim ◽  
Junhak Lee ◽  
Sang Heon Oh ◽  
Hyoungmin So ◽  
Dong-Hwan Hwang
Keyword(s):  
Software Design ◽  
Navigation System ◽  
Design Method ◽  
Satellite System ◽  
Navigation Satellite ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Very High Frequency ◽  
Integrated Navigation System ◽  
Function Modules

To avoid degradation of navigation performance in the navigation warfare environment, the multi-radio integrated navigation system can be used, in which all available radio navigation systems are integrated to back up Global Navigation Satellite System (GNSS) when the GNSS is not available. Before real-time multi-radio integrated navigation systems are deployed, time and cost can be saved when the modeling and simulation (M&S) software is used in the performance evaluation. When the multi-radio integrated navigation system M&S is comprised of independent function modules, it is easy to modify and/or to replace the function modules. In this paper, the M&S software design method was proposed for multi-radio integrated navigation systems as a GNSS backup under the navigation warfare. The M&S software in the proposed design method consists of a message broker and function modules. All the messages were transferred through the message broker in order to be exchanged between the function modules. The function modules in the M&S software were independently operated due to the message broker. A message broker-based M&S software was designed for a multi-radio integrated navigation system. In order to show the feasibility of the proposed design method, the M&S software was implemented for Global Positioning System (GPS), Korean Navigation Satellite System (KNSS), enhanced Long range navigation (eLoran), Loran-C, and Distance Measuring Equipment/Very high-frequency Omnidirectional Radio range (DME/VOR). The usefulness of the proposed design method was shown by checking the accuracy and availability of the GPS only navigation and the multi-radio integrated navigation system under the attack of jamming to GPS.

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 Kalman Filter Technology to Integrated INS/DR Navigation System for Land Vehicle

2013 ◽  
Vol 411-414 ◽  
pp. 912-916 ◽  
Author(s):  
Ying Chen ◽  
Xia Jiang Zhang ◽  
Yuan Yuan Xue ◽  
Zhen Kang ◽  
Ting Shang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Dead Reckoning ◽  
Position Error ◽  
Integrated System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Attitude Error ◽  
Result Show ◽  
The Difference

Strap-down INS is composed of fiber gyroscope. Position error propagation equation and position update algorithm of dead reckoning is deduced in this paper. The Kalman filter is proposed for compensation error of integrated system. The difference of velocity between INS and DR is used as the input of Kalman filter, attitude error, velocity error, position error and scale factor error are to be estimated which compensate and rectify the errors of integrated navigation system. By carrying out experiment upon vehicular navigation system in use of Kalman filter, the errors of integrated navigation system are estimated accurately. Experiment result show that the method not only can effectively improve precision of the system, but also is simple and convenient, so it is more suitable for practical application.

Using Artificial Neural Networks to Compensate for the Error in an Integrated Navigation System

2020 ◽  
pp. 4-26
Author(s):  
N. Al Bitar ◽  
A.I. Gavrilov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Navigation System ◽  
Satellite Navigation ◽  
Satellite System ◽  
Integrated Navigation ◽  
Satellite Navigation System ◽  
Integrated Navigation System ◽  
Artificial Neural

The paper presents a new method for improving the accuracy of an integrated navigation system in terms of coordinate and velocity when there is no signal received from the global navigation satellite system. We used artificial neural networks to simulate the error occurring in an integrated navigation system in the absence of the satellite navigation system signal. We propose a method for selecting the inputs for the artificial neural networks based on the mutual information (MI) criterion and lag-space estimation. The artificial neural network employed is a non-linear autoregressive neural network with external inputs. We estimated the efficiency of using our method to solve the problem of compensating for the error in an integrated navigation system in the absence of the satellite navigation system signal

scholarly journals A Novel Adaptive Two-Stage Information Filter Approach for Deep-Sea USBL/DVL Integrated Navigation

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6029
Author(s):  
Kaifei He ◽  
Huimin Liu ◽  
Zhenjie Wang
Keyword(s):  
Deep Sea ◽  
Underwater Vehicle ◽  
Measurement Noise ◽  
Doppler Velocity ◽  
Time Varying ◽  
Integrated Navigation ◽  
Two Stage ◽  
Information Filter ◽  
Measurement Noise Covariance ◽  
Noise Covariance

An accurate observation model and statistical model are critical in underwater integrated navigation. However, it is often the case that the statistical characteristics of noise are unknown through the ultra-short baseline (USBL) system/Doppler velocity log (DVL) integrated navigation in the deep-sea. Additionally, the velocity of underwater vehicles relative to the bottom of the sea or the currents is commonly provided by the DVL, and an adaptive filtering solution is needed to correctly estimate the velocity with unknown currents. This paper focuses on the estimation of unknown currents and measurement noise covariance for an underwater vehicle based on the USBL, DVL, and a pressure gauge (PG), and proposes a novel unbiased adaptive two-stage information filter (ATSIF) for the underwater vehicle (UV) with an unknown time-varying currents velocity. In the proposed algorithm, the adaptive filter is decomposed into a standard information filter and an unknown currents velocity information filter with interconnections, and the time-varying unknown ocean currents and measurement noise covariance are estimated. The simulation and experimental results illustrate that the proposed algorithm can make full use of high-precision observation information and has better robustness and navigation accuracy to deal with time-varying currents and measurement outliers than existing state-of-the-art algorithms.

Sign in / Sign up

Export Citation Format

Share Document