Improved high-degree cubature Kalman filter based on resampling-free sigma-point update framework and its application for inertial navigation system-based integrated navigation

2021 ◽  
pp. 106905
Author(s):  
Bingbo Cui ◽  
Xinhua Wei ◽  
Xiyuan Chen ◽  
Aichen Wang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Integrated Navigation ◽  
Sigma Point ◽  
Cubature Kalman Filter ◽  
High Degree

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.

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.

scholarly journals Improved Underwater Integrated Navigation System using Unscented Filtering Approach

2015 ◽  
Vol 69 (3) ◽  
pp. 561-581 ◽  
Author(s):  
Mohammad Shabani ◽  
Asghar Gholami
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Doppler Velocity ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Measurement Models ◽  
Error State ◽  
Filtering Approach

In underwater navigation, the conventional Error State Kalman Filter (ESKF) is used for combining navigation data where due to first order linearization of the nonlinear equations of the dynamics and measurements, considerable error is induced in estimated error state and covariance matrices. This paper presents an underwater integrated inertial navigation system using the unscented filter as an improved nonlinear version of the Kalman filter family. The designed system consists of a strap-down inertial navigation system accompanying Doppler velocity log and depth meter. In the proposed approach, to use the nonlinear capabilities of the unscented filtering approach the integrated navigation system is implemented in a direct approach where the nonlinear total state dynamic and and measurement models are utilised without any linearization. To our knowledge, no results have been reported in the literature on the experimental evaluation of the unscented-based integrated navigation system for underwater vehicles. The performance of the designed system is studied using real measurements. The results of the lake test show that the proposed system estimates the vehicle's position more accurately compared with the conventional ESKF structure.

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.

Online cubature Kalman filter Rauch–Tung–Striebel smoothing for indoor inertial navigation system/ultrawideband integrated pedestrian navigation

2017 ◽  
Vol 232 (4) ◽  
pp. 390-398 ◽  
Author(s):  
Yuan Xu ◽  
Xiyuan Chen
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Position Error ◽  
Smoothing Algorithm ◽  
Position Information ◽  
The North ◽  
Proposed Model ◽  
Cubature Kalman Filter

Accurate position information of the pedestrians is required in many applications such as healthcare, entertainment industries, and military field. In this work, an online Cubature Kalman filter Rauch–Tung–Striebel smoothing algorithm for people’s location in indoor environment is proposed using inertial navigation system techniques with ultrawideband technology. In this algorithm, Cubature Kalman filter is employed to improve the filtering output accuracy; then, the Rauch–Tung–Striebel smoothing is used between the ultrawideband measurements updates; finally, the average value of the corrected inertial navigation system error estimation is output to compensate the inertial navigation system position error. Moreover, a real indoor test has been done for assessing the performance of the proposed model and algorithm. Test results show that the proposed model is able to reduce the sum of the absolute position error between the east direction and the north direction by about 32% compared with only the ultrawideband model, and the performance of the online Cubature Kalman filter Rauch–Tung–Striebel smoothing algorithm is slightly better than the off-line mode.

scholarly journals Application of MCC-based Robust High-degree Cubature Kalman Filter in Integrated Navigation System

2019 ◽  
Vol 1168 (6) ◽  
pp. 062029
Author(s):  
Ming-qi Xu ◽  
Guo-rong Huang ◽  
Hang Lu ◽  
Zhi-ying Peng ◽  
Shun-yi Hao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Cubature Kalman Filter ◽  
High Degree

Application of Extended Kalman Filter Algorithm in SDINS/GPS Integrated Inertial Navigation System

2013 ◽  
Vol 367 ◽  
pp. 528-535
Author(s):  
Otman Ali Awin
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Integrated System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Matlab Package ◽  
Noisy Measurements

This paper deals with the integrated navigation system based on fusion of data from Strap Down Inertial Navigation System (SDINS) and from Global Position System (GPS). In order to increase the accuracy and reliability of navigation algorithms, these two different systems are combined. The navigation system that be analyzed is basically of INS type while GPS corrective data are obtained less frequently and these are treated as noisy measurements in an extended Kalman filter scheme. The simulation of whole system (SDINS/GPS integrated system with Kalman filter) was modeled using MATLAB package, SIMULINK© tool. The proper choice of Kalman filter parameters had taken to minimize navigation errors for a typical medium range flight scenario (Simulated test trajectory and real trajectory of vehicle motion). A prototype of a SDINS installed on a moving platform in the laboratory to collected data by many experiments to verification our SIMULINK models.

Sign in / Sign up

Export Citation Format

Share Document