scholarly journals Tightly Coupled Integrated Navigation Algorithm for Hypersonic Boost-Glide Vehicles in the LCEF Frame

Aerospace ◽  
2021 ◽  
Vol 8 (5) ◽  
pp. 124
Author(s):  
Kai Chen ◽  
Sensen Pei ◽  
Fuqiang Shen ◽  
Shangbo Liu
Keyword(s):  
Inertial Navigation ◽  
State Equation ◽  
Integrated Navigation ◽  
Loosely Coupled ◽  
Navigation Algorithm ◽  
Tightly Coupled ◽  
Measurement Equations ◽  
Range Rate ◽  
Better Than ◽  
Strapdown Inertial Navigation

According to the trajectory characteristics of hypersonic boost-glide vehicles, a tightly coupled integrated navigation algorithm for hypersonic vehicles based on the launch-centered Earth-fixed (LCEF) frame is proposed. First, the strapdown inertial navigation mechanization algorithm and discrete update algorithm in the LCEF frame are introduced. Subsequently, the attitude, velocity, and position error equations of strapdown inertial navigation in the LCEF frame are introduced. The strapdown inertial navigation system/global positioning system (SINS/GPS) pseudo-range and pseudo-range rate measurement equations in the LCEF frame are derived. Further, the tightly coupled SINS/GPS integrated navigation filter state equation and the measurement equation are presented. Finally, the tightly coupled SINS/GPS integrated navigation algorithm is verified in the hardware-in-the-loop (HWIL) simulation environment. The simulation results indicate that the precision of tightly coupled integrated navigation is better than that of loosely coupled integrated navigation. Moreover, even when the number of effective satellites is less than four, tightly coupled integrated navigation functions well, thus verifying the effectiveness and feasibility of the algorithm.

scholarly journals An INS/Geomagnetic Integrated Navigation Algorithm Based on Matching Strategy and Hierarchical Filtering

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 460
Author(s):  
Xiusheng Duan ◽  
Jing Xiao ◽  
Xiaohui Qi ◽  
Yifei Liu
Keyword(s):  
Magnetic Measurements ◽  
Inertial Navigation ◽  
State Equation ◽  
Integrated Navigation ◽  
Matching Algorithm ◽  
Filtering Algorithm ◽  
Navigation Algorithm ◽  
Physical Experiments ◽  
Matching Strategy ◽  
Navigation Information

To improve the positioning accuracy of an inertial/geomagnetic integrated navigation algorithm, a combined navigation method based on matching strategy and hierarchical filtering is proposed. First, the PDA-ICCP geomagnetic matching algorithm is improved. On basis of evaluating the distribution of magnetic measurements, a number of controllable magnetic values are regenerated to participate in the geomagnetic matching algorithm (GMA). As a result, accuracy of the matching algorithm is ensured and its efficiency is improved. Secondly, the integrated navigation filter is designed based on the hierarchical filtering strategy, in which the navigation information of the geomagnetic matching module and inertial navigation module are respectively filtered and fused in the main filter. In this way, the shortcoming that GMA is unable to provide continuous and real-time navigation information is overcome. Meanwhile, precision of the inertial/geomagnetic integrated navigation algorithm is improved. Finally, the feasibility and validity of the proposed algorithm are verified by simulation and physical experiments. Compared with the integrated filtering algorithm which directly uses the error equation of inertial navigation system (INS) as the state equation, the proposed hierarchical filtering algorithm can achieve higher positioning precision.

scholarly journals Discrete EKF with pairwise Time Correlated Measurement Noise for Image-Aided Inertial Integrated Navigation

2014 ◽  
Vol II-2 ◽  
pp. 61-66 ◽  
Author(s):  
N. S. Gopaul ◽  
J. G. Wang ◽  
B. Hu
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation ◽  
Measurement Noise ◽  
Integrated Navigation ◽  
Process Noise ◽  
Shaping Filter ◽  
Egomotion Estimation ◽  
Simulation Results ◽  
The One ◽  
Better Than

An image-aided inertial navigation implies that the errors of an inertial navigator are estimated via the Kalman filter using the aiding measurements derived from images. The standard Kalman filter runs under the assumption that the process noise vector and measurement noise vector are white, i.e. independent and normally distributed with zero means. However, this does not hold in the image-aided inertial navigation. In the image-aided inertial integrated navigation, the relative positions from optic-flow egomotion estimation or visual odometry are <i>pairwise</i> correlated in terms of time. It is well-known that the solution of the standard Kalman filter becomes suboptimal if the measurements are colored or time-correlated. Usually, a shaping filter is used to model timecorrelated errors. However, the commonly used shaping filter assume that the measurement noise vector at epoch <i>k</i> is not only correlated with the one from epoch <i>k</i> &ndash; 1 but also with the ones before epoch <i>k</i> &ndash; 1 . The shaping filter presented in this paper uses Cholesky factors under the assumption that the measurement noise vector is pairwise time-correlated i.e. the measurement noise are only correlated with the ones from previous epoch. Simulation results show that the new algorithm performs better than the existing algorithms and is optimal.

Simplified Design of Dual Quaternion Strapdown Inertial Navigation Integration Algorithms

2012 ◽  
Vol 239-240 ◽  
pp. 1421-1427
Author(s):  
Yu Rong Lin ◽  
Liang Chen ◽  
Zhen Xian Fu
Keyword(s):  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Dual Quaternion ◽  
Velocity Increment ◽  
Time Performance ◽  
Navigation Algorithm ◽  
Integration Algorithms ◽  
Simplified Algorithm ◽  
The Cost ◽  
Strapdown Inertial Navigation

Dual quaternion navigation algorithm gain higher accuracy than traditional strapdown inertial navigation algorithm at the cost of real-time performance. In order to reduce tremendous computation amount of the former, a simplified design scheme for navigation integration algorithms is presented in this paper. First, based on update principle and computation rules of dual quaternion we separate rotational and translational increment information from dual quaternion increment, and deduce exact solutions defined by the spiral vector for thrust velocity increment, gravitational velocity increment and displacement increment. Then, considering characteristics of a strapdown inertial navigation system, implementation schemes of simplified integration algorithms for dual quaternion differential equations in three frames, including thrust velocity coordinates, gravitational velocity coordinates and position coordinates, are designed separately. Under the premise of ensuring the accuracy advantage of the original dual quaternion inertial navigation algorithm, the proposed simplified algorithm significantly improve the computational efficiency. This will lay favorable foundation for engineering realization of the dual quaternion strapdown inertial navigation algorithm.

Application of Federated Filter to AUV Based on Terrain-Aided SINS

2014 ◽  
Vol 711 ◽  
pp. 338-341 ◽  
Author(s):  
Qi Wang ◽  
Cheng Shan Qian ◽  
Zi Jia Zhang ◽  
Chang Song Yang
Keyword(s):  
Kalman Filtering ◽  
Navigation System ◽  
Autonomous Underwater Vehicles ◽  
Inertial Navigation ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Filtering Method ◽  
Long Time ◽  
Federated Filtering ◽  
Strapdown Inertial Navigation

To improve the navigation precision and reliability of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Federated Filter (FF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and Federated Filtering 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 Federated Filtering method is able to improve the long-time navigation precision and reliability, relative to the traditional Kalman Filtering method.

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.

Sign in / Sign up

Export Citation Format

Share Document