An Integrated Navigation System For Suez Canal (SCINS)

2003 ◽  
Vol 56 (2) ◽  
pp. 241-255 ◽  
Author(s):  
Farouk Abd EL-Kader ◽  
M. Samy Abo EL-Soud ◽  
Kamel EL-Serafy ◽  
Ezzat A. Hassan
Keyword(s):  
Real Time ◽  
Navigation System ◽  
Traffic Management ◽  
Filter Design ◽  
Suez Canal ◽  
System Level ◽  
Superior Performance ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Traffic Management System

This paper offers a designed Integrated Navigation System that will permit vessels to transit safely through the Suez Canal avoiding collision and grounding in all weather environments instead of being directed to anchor, thus keeping the Canal open at all times for ship transits. The Suez Canal Integrated Navigation System (SCINS) includes Differential Global Positioning System (DGPS), Suez Canal LORAN-C system, and Vessel Traffic Management System (VTMS). The combination of DGPS and LORAN-C systems would provide real-time DGPS corrections that could be used to calibrate the Loran fix; this can be achieved by means of portable integrated DGPS/LORAN-C sets installed aboard the vessels. The addition of VTMS provides significant capability for preserving system accuracy during periods of GPS outages. Due to the interface between LORAN-C and VTMS systems, the SCINS will be able to solve the problem of targets that cannot be tracked by VTMS radars in the shadow areas behind the new bridges along the Canal. The SCINS automates position fixing in real-time, offers a designed algorithm to return the ship to the middle of the Canal and computes the cross-track error (XTE) and the ship squat. Kalman Filter design and system level performance predictions for the SCINS are briefly described. Simulation results show that the SCINS offers superior performance and better position accuracy than current integrated systems.

Seamless INS/GPS Integration Based on Support Vector Machines

2013 ◽  
Vol 336-338 ◽  
pp. 277-280 ◽  
Author(s):  
Tian Lai Xu
Keyword(s):  
Support Vector Machines ◽  
Global Positioning System ◽  
Navigation System ◽  
Superior Performance ◽  
Support Vector ◽  
Integrated Navigation ◽  
Positioning Accuracy ◽  
Integrated Navigation System ◽  
Vector Machines ◽  
Global Positioning

The combination of Inertial Navigation System (INS) and Global Positioning System (GPS) provides superior performance in comparison with either a stand-alone INS or GPS. However, the positioning accuracy of INS/GPS deteriorates with time in the absence of GPS signals. A least squares support vector machines (LS-SVM) regression algorithm is applied to INS/GPS integrated navigation system to bridge the GPS outages to achieve seamless navigation. In this method, LS-SVM is trained to model the errors of INS when GPS is available. Once the LS-SVM is properly trained in the training phase, its prediction can be used to correct the INS errors during GPS outages. Simulations in INS/GPS integrated navigation showed improvements in positioning accuracy when GPS outages occur.

MEMS-SINS/GPS/Magnetometer Integrated Navigation System for Small Unmanned Aerial Vehicles

2014 ◽  
Vol 568-570 ◽  
pp. 976-986 ◽  
Author(s):  
Cun Xiao Miao ◽  
Juan Juan Cao ◽  
Yang Bin Ou
Keyword(s):  
Low Power ◽  
Real Time ◽  
Navigation System ◽  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Flight Test ◽  
Integrated Navigation ◽  
Autonomous Flight ◽  
Integrated Navigation System ◽  
Small Uav

The constraints of weight, volume and power for Small unmanned air vehicle (UAV) restrict the application of sensors with heavy and good performance and powerful processors. This paper presents a real-time solution of autonomous flight navigation and its results for small UAV by applying small, cheap, low precision and low-power integrated navigation system, which includes Strap-down Inertial Navigation System (SINS) based on Micro-electro-mechanical system (MEMS) inertial sensors, Global Positioning System (GPS) receiver and magnetometer. The Square-Root Unscented Kalman filter (SR-UKF) for data fusion using in this MEMS-SINS/GPS/ magnetometer integrated navigation system provides continuous and reliable navigation results for the loops of guidance and control for the small UAV with autonomous flight. The whole integrated navigation system algorithm is implemented within low-power embedded microprocessors. The real-time flight test results show that the MEMS-SINS/GPS/magnetometer integrated navigation system is effective and accurate.

scholarly journals Information Fusion Based on Complementary Filter for SINS/CNS/GPS Integrated Navigation System of Aerospace Plane

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7193
Author(s):  
Yanming Zhao ◽  
Gongmin Yan ◽  
Yongyuan Qin ◽  
Qiangwen Fu
Keyword(s):  
Real Time ◽  
Information Fusion ◽  
Navigation System ◽  
Physical Simulation ◽  
Fusion Method ◽  
Integrated Navigation ◽  
Real Time System ◽  
Integrated Navigation System ◽  
Complementary Filter ◽  
Aerospace Plane

In order to solve the problems of heavy computational load and poor real time of the information fusion method based on the federated Kalman filter (FKF), a novel information fusion method based on the complementary filter is proposed for strapdown inertial navigation (SINS)/celestial navigation system (CNS)/global positioning system (GPS) integrated navigation system of an aerospace plane. The complementary filters are designed to achieve the estimations of attitude, velocity, and position in the SINS/CNS/GPS integrated navigation system, respectively. The simulation results show that the proposed information fusion method can effectively realize SINS/CNS/GPS information fusion. Compared with FKF, the method based on complementary filter (CF) has the advantages of simplicity, small calculation, good real-time performance, good stability, no need for initial alignment, fast convergence, etc. Furthermore, the computational efficiency of CF is increased by 94.81%. Finally, the superiority of the proposed CF-based method is verified by both the semi-physical simulation and real-time system experiment.

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