Direction/Distance/Velocity Measurements Deeply Integrated Navigation for Venus Capture Period

2018 ◽  
Vol 71 (4) ◽  
pp. 861-877 ◽  
Author(s):  
Jin Liu ◽  
Xiao-lin Ning ◽  
Xin Ma ◽  
Jian-cheng Fang ◽  
Gang Liu
Keyword(s):  
Autonomous Navigation ◽  
Position Estimation ◽  
Residual Effects ◽  
Integrated Navigation ◽  
Velocity Measurements ◽  
Performance Simulation ◽  
Doppler Effects ◽  
Accurate Position ◽  
Direction Distance ◽  
Observation Period

In the Venus capture period, it is difficult for celestial autonomous navigation to satisfy the requirement of high precision. To improve autonomous navigation performance, a Direction, Distance and Velocity (DDV) measurements deeply integrated navigation method is proposed. The “deeply” integrated navigation reflects the fact that the direction and velocity measurements suppress the Doppler effects in the pulsar signals. In the pulsar observation period, the direction and velocity measurements are utilised to compensate for Doppler effects in the pulsar signals. By these means, the residual effects can be ignored. When the direction, distance or velocity measurements are obtained, they are fused to improve the navigation performance. Simulation results demonstrate that the DDV measurements deeply integrated navigation filter converges very well, and provides highly accurate position estimation without a high quality requirement on navigation sensors.

Optimising the Transfer Alignment of Weapon INS

2003 ◽  
Vol 56 (2) ◽  
pp. 323-335 ◽  
Author(s):  
Paul D. Groves
Keyword(s):  
Inertial Sensor ◽  
Integrated Navigation ◽  
Velocity Measurements ◽  
Transfer Alignment ◽  
Navigation Solution ◽  
Alignment Technique ◽  
Alignment Process ◽  
Using Data ◽  
Design Options ◽  
Better Than

Transfer alignment is the process of initialising and calibrating a weapon INS using data from the host aircraft's navigation system. To determine which transfer alignment technique performs best, different design options have been assessed, supported by simulation work. The dependence of transfer alignment performance on environmental factors, such as manoeuvres, alignment duration, lever arm and inertial sensor quality has also been studied. ‘Rapid’ alignment, using attitude as well as velocity measurements was found to perform better than ‘conventional’ techniques using only velocity. Innovative developments include the estimation of additional acceleration and gyro states and estimation of force dependent relative orientation, which has enabled robust alignment using wing rock manoeuvres, which do not require the pilot to change trajectory. Transfer alignment has been verified in real-time by flight trials on a Tornado aircraft. In addition, techniques have been developed to prevent transients in the aircraft integrated navigation solution following GPS re-acquisition after an outage of several minutes from disrupting the transfer alignment process.

scholarly journals The Method of Train Positioning Based on Digital Track Map Matching

2018 ◽  
Vol 246 ◽  
pp. 03024
Author(s):  
Pengfei Wang ◽  
Weidong Li ◽  
Xinping Wang ◽  
Xianwu Chu
Keyword(s):  
Correction Method ◽  
Position Estimation ◽  
Map Matching ◽  
Spline Curve ◽  
Positioning Method ◽  
Rail Line ◽  
Accurate Position ◽  
Train Positioning ◽  
Cubic Spline Curve ◽  
Track Line

A train positioning method based on GPS and digital rail line matching is proposed. Firstly, the digital track line is generated based on the fitting and interpolation algorithm of train track line. And then the GPS data are corrected by the track line positioning correction method, and the more accurate position estimation of the train is obtained. Finally, the data track line is simulated and analyzed with some measured data from Harbin to Qigihar track line. The analysis results show that cubic spline curve is better than cubic B-spline curve on the establishment of digital track map.

scholarly journals A Novel Neural Network-Based SINS/DVL Integrated Navigation Approach to Deal with DVL Malfunction for Underwater Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Wanli Li ◽  
Mingjian Chen ◽  
Chao Zhang ◽  
Lundong Zhang ◽  
Rui Chen
Keyword(s):  
Neural Network ◽  
Autonomous Navigation ◽  
Underwater Vehicles ◽  
The Body ◽  
Doppler Velocity ◽  
Integrated Navigation ◽  
Body Frame ◽  
Short Term ◽  
The Neural Network ◽  
Nonlinear Autoregressive

A navigation grade Strapdown Inertial Navigation System (SINS) combined with a Doppler Velocity Log (DVL) is widely used for autonomous navigation of underwater vehicles. Whether the DVL is able to provide continuous velocity measurements is of crucial importance to the integrated navigation precision. Considering that the DVL may fail during the missions, a novel neural network-based SINS/DVL integrated navigation approach is proposed. The nonlinear autoregressive exogenous (NARX) neural network, which is able to provide reliable predictions, is employed. While the DVL is available, the neural network is trained by the body frame velocity and its increment from the SINS and the DVL measurements. Once the DVL fails, the well trained network is able to forecast the velocity which can be used for the subsequent navigation. From the experimental results, it is clearly shown that the neural network is able to provide reliable velocity predictions for about 200 s–300 s during DVL malfunction and hence maintain the short-term accuracy of the integrated navigation.

scholarly journals Estimation of UAV Position with Use of Smoothing Algorithms

2017 ◽  
Vol 24 (1) ◽  
pp. 127-142 ◽  
Author(s):  
Piotr Kaniewski ◽  
Rafał Gil ◽  
Stanisław Konatowski
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Position Estimation ◽  
Fixed Interval ◽  
Smoothing Algorithm ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Gnss Receiver ◽  
On Line

Abstract The paper presents methods of on-line and off-line estimation of UAV position on the basis of measurements from its integrated navigation system. The navigation system installed on board UAV contains an INS and a GNSS receiver. The UAV position, as well as its velocity and orientation are estimated with the use of smoothing algorithms. For off-line estimation, a fixed-interval smoothing algorithm has been applied. On-line estimation has been accomplished with the use of a fixed-lag smoothing algorithm. The paper includes chosen results of simulations demonstrating improvements of accuracy of UAV position estimation with the use of smoothing algorithms in comparison with the use of a Kalman filter.

Optical Flow-Based Monocular Vision/INS Integrated Navigation for Mobile Robot Indoors

2014 ◽  
Vol 538 ◽  
pp. 375-378 ◽  
Author(s):  
Xi Yuan Chen ◽  
Jing Peng Gao ◽  
Yuan Xu ◽  
Qing Hua Li
Keyword(s):  
Mobile Robot ◽  
Optical Flow ◽  
Global Positioning System ◽  
Autonomous Navigation ◽  
Image Sequences ◽  
Velocity Estimation ◽  
Monocular Vision ◽  
Integrated Navigation ◽  
Global Positioning ◽  
Flow Algorithm

This paper proposed a new algorithm for optical flow-based monocular vision (MV)/ inertial navigation system (INS) integrated navigation. In this mode, a downward-looking camera is used to get the image sequences, which is used to estimate the velocity of the mobile robot by using optical flow algorithm. INS is employed for the yaw variation. In order to evaluate the performance of the proposed method, a real indoor test has done. The result shows that the proposed method has good performance for velocity estimation. It can be applied to the autonomous navigation of mobile robots when the Global Positioning System (GPS) and code wheel is unavailable.

scholarly journals Scalability Optimization of Seamless Positioning Service

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Juraj Machaj ◽  
Peter Brida ◽  
Jozef Benikovsky
Keyword(s):  
Service Providers ◽  
Research Community ◽  
Position Estimation ◽  
Point Of View ◽  
Modular System ◽  
Positioning Error ◽  
Real World Application ◽  
Radio Signals ◽  
Accurate Position ◽  
Seamless Positioning

Recently positioning services are getting more attention not only within research community but also from service providers. From the service providers point of view positioning service that will be able to work seamlessly in all environments, for example, indoor, dense urban, and rural, has a huge potential to open new markets. However, such system does not only need to provide accurate position estimates but have to be scalable and resistant to fake positioning requests. In the previous works we have proposed a modular system, which is able to provide seamless positioning in various environments. The system automatically selects optimal positioning module based on available radio signals. The system currently consists of three positioning modules—GPS, GSM based positioning, and Wi-Fi based positioning. In this paper we will propose algorithm which will reduce time needed for position estimation and thus allow higher scalability of the modular system and thus allow providing positioning services to higher amount of users. Such improvement is extremely important, for real world application where large number of users will require position estimates, since positioning error is affected by response time of the positioning server.

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