Research on integrated navigation in AEB system based on multi-source fusion

2021 ◽  
Author(s):  
Y. Zhang ◽  
J. Li ◽  
X. Liu
Keyword(s):  
Integrated Navigation

Naval Energy Management System

2020 ◽  
Vol 26 (3) ◽  
pp. 20-25
Author(s):  
Laurențiu Bogdan Asalomia ◽  
Gheorghe Samoilescu
Keyword(s):  
Energy Consumption ◽  
Power Systems ◽  
New Technologies ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Efficient Management ◽  
Reduce Energy Consumption ◽  
Energy Efficiency Design Index ◽  
Green Ship

AbstractThe paper analyses the role of control and monitoring of electro-energetic equipment in order to reduce operational costs, increase profits and reduce carbon emissions. The role of SCADA and EcoStruxure Power systems is presented and analysed taking into account the energy consumption and its savings. The paper presents practical and modern solutions to reduce energy consumption by up to 53%, mass by up to 47% and increase the life of the equipment by adjusting the electrical parameters. The Integrated Navigation System has allowed an automatic control and an efficient management. For ships, the implementation of an energy efficiency design index and new technologies was required for the GREEN SHIP project.

New filtering method for GPS/INS integrated navigation

2010 ◽  
Author(s):  
Xiao-chun Zhong ◽  
Tao Song ◽  
Biao Zheng ◽  
Tingting Zhang
Keyword(s):  
Integrated Navigation ◽  
Filtering Method

A new visual/inertial integrated navigation algorithm based on sliding-window factor graph optimisation

2020 ◽  
pp. 1-17
Author(s):  
Haiying Liu ◽  
Jingqi Wang ◽  
Jianxin Feng ◽  
Xinyao Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Prior Information ◽  
Sliding Window ◽  
Factor Graph ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Sliding Windows ◽  
Navigation Algorithm ◽  
Almost All

Abstract Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

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