scholarly journals Equivariant filtering framework for inertial-integrated navigation

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Yarong Luo ◽  
Chi Guo ◽  
Jingnan Liu
Keyword(s):  
Lie Group ◽  
Inertial Navigation System ◽  
Integrated Navigation ◽  
Extend Kalman Filter ◽  
Extended Kalman Filtering ◽  
The Earth ◽  
Kinematic System ◽  
The Matrix ◽  
Affine System ◽  
Global Navigation Satellite

AbstractThis paper proposes an Equivariant Filtering (EqF) framework for the inertial-integrated state estimation. As the kinematic system of the inertial-integrated navigation can be naturally modeled on the matrix Lie group SE2(3), the symmetry of the Lie group can be exploited to design an equivariant filter which extends the invariant extended Kalman filtering on the group-affine system and overcomes the inconsitency issue of the traditional extend Kalman filter. We firstly formulate the inertial-integrated dynamics as the group-affine systems. Then, we prove the left equivariant properties of the inertial-integrated dynamics. Finally, we design an equivariant filtering framework on the earth-centered earth-fixed frame and the local geodetic navigation frame. The experiments show the superiority of the proposed filters when confronting large misalignment angles in Global Navigation Satellite Navigation (GNSS)/Inertial Navigation System (INS) loosely integrated navigation experiments.

Dynamically Adjusting Filter Gain Method for Suppressing GNSS Observation Outliers in Integrated Navigation

2018 ◽  
Vol 71 (6) ◽  
pp. 1396-1412 ◽  
Author(s):  
Lihui Wang ◽  
Kangyi Zhi ◽  
Bin Li ◽  
Yuexin Zhang
Keyword(s):  
Inertial Navigation System ◽  
Normal Values ◽  
External Environment ◽  
Processing Method ◽  
Global Navigation Satellite Systems ◽  
Integrated Navigation ◽  
Satellite Systems ◽  
Loosely Coupled ◽  
Global Navigation Satellite ◽  
The Impact

Global Navigation Satellite Systems (GNSSs) are easily influenced by the external environment. Signals may be lost or become abnormal thereby causing outliers. The filter gain of the standard Kalman filter of a loosely coupled GNSS/inertial navigation system cannot change with the outliers of the GNSS, causing large deviations in the filtering results. In this paper, a method based on a χ2-test and a dynamically adjusting filter gain method are proposed to detect and separately to suppress GNSS observation outliers in integrated navigation. An indicator of an innovation vector is constructed, and a χ2-test is performed for this indicator. If it fails the test, the corresponding observation value is considered as an outlier. A scale factor is constructed according to this outlier, which is then used to lower the filter gain dynamically to decrease the influence of outliers. The simulation results demonstrate that the observation outlier processing method does not affect the normal values under normal circumstances; it can also discriminate between single and continuous outliers without errors or omissions. The impact time of outliers is greatly reduced, and the system performance is improved by more than 90%. Experimental results indicate that the proposed methods are effective in suppressing GNSS observation outliers in integrated navigation.

scholarly journals SINS/BDS Integrated Navigation for Hypersonic Boost-Glide Vehicles in the Launch-Centered Inertial Frame

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Kai Chen ◽  
Fuqiang Shen ◽  
Jun Zhou ◽  
Xiaofeng Wu
Keyword(s):  
Inertial Navigation System ◽  
Inertial Frame ◽  
Satellite System ◽  
Hypersonic Vehicles ◽  
Navigation Satellite ◽  
Hardware In The Loop ◽  
Integrated Navigation ◽  
Beidou Navigation Satellite System ◽  
The Earth ◽  
Frame System

According to the trajectory specialty of hypersonic boost-glide vehicles, a strapdown inertial navigation system/BeiDou navigation satellite system (SINS/BDS) algorithm based on the launch-centered inertial (LCI) frame for hypersonic vehicles is proposed. First, the related frame system, especially the launch earth-centered inertial (LECI) frame, and the SINS mechanization in the LCI frame are introduced. Second, SINS discrete updating algorithms in the LCI frame for the compensation of coning, sculling, and scrolling effects are deduced in the attitude, velocity, and position updating algorithms, respectively. Subsequently, the Kalman filter of the SINS/BDS integrated navigation in the LCI frame is obtained. The method of converting BDS receiver position and velocity from the Earth-centered Earth-fixed (ECEF) frame to the LCI frame is deduced through the LECI frame. Finally, taking the typical hypersonic boost-glide vehicles as the object, the SINS/BDS algorithm vehicle field test and hardware-in-the-loop simulation are performed.

scholarly journals Integration of Inertial Navigation System with EM-log Using H-infinity Filter

2019 ◽  
Vol 94 ◽  
pp. 01013
Author(s):  
Jaehyuck Cha ◽  
Hojin Ju ◽  
Chan Gook Park ◽  
Kijeong Yoo ◽  
Chanju Park
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Current Model ◽  
Inertial Navigation ◽  
Satellite System ◽  
Integrated Navigation ◽  
H Infinity ◽  
Sea Current ◽  
Underwater Navigation ◽  
Global Navigation Satellite

This paper presents the integration of inertial navigation system (INS) with electromagnetic-log (EM-log) as an underwater navigation system using H-infinity filter for robustness from the uncertainty of the sea current model. In underwater environments, the electromagnetic signals are attenuated rapidly, so that the global navigation satellite system is not available in general. Thus, INS is usually chosen for underwater navigation, and other aiding sensors are also used to complement its accumulative errors, one of which is EM-log. Since an EM-log provides the relative velocity to seawater, the integrated navigation cannot be performed accurately unless the sea current speed is compensated properly. Generally, the INS and EM-log can be integrated using extended Kalman filter (EKF). However, EKF guarantees its performance when the stochastic properties of the system’s process and measurement noises are perfectly known. In other words, in the presence of sea current modelling errors, the integration using the EKF is not expected to show good performance. On the other hand, H-infinity filter is a robust filter which can tolerate such uncertainties. In this paper, the integration of INS and EM-log using H-infinity filter is studied. The performance is compared with that of the EKF case by proper computer simulation.

An improved integrated navigation method based on RINS, GNSS and kinematics for port heavy-duty AGV

2020 ◽  
Vol 234 (8) ◽  
pp. 2135-2153
Author(s):  
Zhuang Fu ◽  
Xin Feng ◽  
Xiaoming Duan ◽  
Zeyu Fu
Keyword(s):  
Global Navigation Satellite System ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Satellite System ◽  
Automated Guided Vehicle ◽  
Navigation Satellite ◽  
Integrated Navigation ◽  
Heavy Duty ◽  
Global Navigation ◽  
Global Navigation Satellite

The current navigation methods for port heavy-duty automated guided vehicle mainly include the antenna radar-transponder navigation and the global navigation satellite system. However, the former has a huge cost and the latter will generate multi-path error easily. To avoid these problems, an improved integrated navigation method based on single-axis rotating inertial navigation system, global navigation satellite system and kinematics is proposed. First, the rotating inertial navigation system/ global navigation satellite system and rotating inertial navigation system/Kinematics integrated navigation methods generate corresponding estimates and filtering error covariances through their respective extended Kalman filter filters, and then the two sets of results are fused by the optimal weighted voting fusion method. The proposed method is applied to a heavy-duty automated guided vehicle for engineering verification. Without multi-path error, the navigation accuracy is 1.8–2.98 times higher than that of the traditional global navigation satellite system navigation. In the case of multi-path error, the improved method still has high fault tolerance and high navigation accuracy. The accuracy of this method satisfies the requirements of port heavy-duty automated guided vehicle, which can greatly reduce the number of transponders and has high practical value.

Human Rights as Land Rights in the Pacific

1993 ◽  
Vol 6 (1) ◽  
pp. 61-80 ◽  
Author(s):  
John D'Arcy May
Keyword(s):  
Human Rights ◽  
Case Studies ◽  
Papua New Guinea ◽  
Land Rights ◽  
Human Community ◽  
The Earth ◽  
The Pacific ◽  
West Papua ◽  
The Matrix ◽  
The One

Do human rights in their conventional, Western understanding really meet the needs of Pacific peoples? This article argues that land rights are a better clue to those needs. In Aboriginal Australia, Fiji, West Papua and Papua New Guinea, case studies show that people's relationship to land is religious and implicitly theological. The article therefore suggests that rights to land need to be supplemented by rights of the land extending to the earth as the home of the one human community and nature as the matrix of all life.

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.

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