A Combinational Underwater Aided Navigation Algorithm Based on TERCOM/ICCP and Kalman Filter

2011 ◽  
Author(s):  
Gannan Yuan ◽  
Hongwei Zhang ◽  
Kefei Yuan ◽  
Chunyan Tao
Keyword(s):  
Kalman Filter ◽  
Navigation Algorithm

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.

Multi-Sensor Integrated Navigation Algorithm Using Adaptive Federated Kalman Filter for MAVs

2018 ◽  
Author(s):  
Ge-Hang Lei ◽  
Qing-Hao Meng ◽  
Ying-Jie Liu ◽  
Hui-Rang Hou ◽  
Ming Zeng
Keyword(s):  
Kalman Filter ◽  
Integrated Navigation ◽  
Navigation Algorithm ◽  
Federated Kalman Filter

Localization and Map Building of Laser Sensor AGV Based on Kalman Filter

2013 ◽  
Vol 712-715 ◽  
pp. 1938-1943
Author(s):  
Li Xiao Guo ◽  
Fan Kun ◽  
Wen Jun Yan
Keyword(s):  
Kalman Filter ◽  
Sensor Data ◽  
Laser Sensor ◽  
Map Building ◽  
Localization Accuracy ◽  
Key Technology ◽  
Navigation Algorithm ◽  
Automatic Guided Vehicle ◽  
Kalman Filter Algorithm ◽  
Navigation Method

Localization and navigation algorithm is the key technology to determine whether or not an AGV (automatic guided vehicle) can run normally. In this paper, we summarize the popular navigation technologies first and then focus on the positioning principle of Nav200 which is adopted in our AGV system. Besides that, the map building method and the layout of the reflective board is also introduced briefly. This paper introduced two navigation methods. The traditional navigation method only uses the sensor data and the electronic map to guide AGV. To improve positioning accuracy, we use the Kalman filter to minimize the error of localization sensor. At last some simulation work was done, the results shows that the localization accuracy was improved by adopting Kalman filter algorithm.

scholarly journals Performance Analysis of Computational Intelligence Correction

2021 ◽  
Author(s):  
Nalini Arasavali ◽  
Sasibhushanarao Gottapu
Keyword(s):  
Kalman Filter ◽  
Performance Analysis ◽  
Computational Intelligence ◽  
A Priori ◽  
Covariance Matrices ◽  
System Model ◽  
Process Noise ◽  
Navigation Algorithm ◽  
Modified Kalman Filter ◽  
Measurement Noises

Abstract Kalman filter (KF) is a widely used navigation algorithm, especially for precise positioning applications. However, the exact filter parameters must be defined a priori to use standard Kalman filters for coping with low error values. But for the dynamic system model, the covariance of process noise is a priori entirely undefined, which results in difficulties and challenges in the implementation of the conventional Kalman filter. Kalman Filter with recursive covariance estimation applied to solve those complicated functional issues, which can also be used in many other applications involving Kalaman filtering technology, a modified Kalman filter called MKF-RCE. While this is a better approach, KF with SAR tuned covariance has been proposed to resolve the problem of estimation for the dynamic model. The data collected at (x: 706970.9093 m, y: 6035941.0226 m, z: 1930009.5821 m) used to illustrate the performance analysis of KF with recursive covariance and KF with computational intelligence correction by means of SAR (Search and Rescue) tuned covariance, when the covariance matrices of process and measurement noises are completely unknown in advance.

Vehicle navigation algorithm based on unscented Kalman filter sensor information fusion

2013 ◽  
Vol 33 (12) ◽  
pp. 3444-3448
Author(s):  
Dingwen LIANG ◽  
Lei YUAN ◽  
Zhihua CAI ◽  
Qiong GU
Keyword(s):  
Kalman Filter ◽  
Information Fusion ◽  
Unscented Kalman Filter ◽  
Vehicle Navigation ◽  
Navigation Algorithm ◽  
Sensor Information

Fault-Tolerant Integrated Navigation Algorithm of the Federal Kalman Filter

2012 ◽  
pp. 621-627 ◽  
Author(s):  
Jing Li ◽  
Jiande Wu ◽  
Junfeng Hou ◽  
Yugang Fan ◽  
Xiaodong Wang
Keyword(s):  
Kalman Filter ◽  
Fault Tolerant ◽  
Integrated Navigation ◽  
Navigation Algorithm

scholarly journals Precise Indoor Positioning and Attitude Determination using Terrestrial Ranging Signals

2014 ◽  
Vol 68 (2) ◽  
pp. 274-290 ◽  
Author(s):  
Wei Jiang ◽  
Yong Li ◽  
Chris Rizos
Keyword(s):  
Kalman Filter ◽  
Carrier Phase ◽  
Attitude Determination ◽  
Unscented Kalman Filter ◽  
Line Of Sight ◽  
Positioning System ◽  
Indoor Environments ◽  
Navigation Algorithm ◽  
Modelling System ◽  
Better Than

This paper presents the results of a new multipath mitigating antenna “V-Ray” for use with terrestrial ranging signals in severe multipath indoor environments. The V-Ray antenna – as used in the Locata positioning system – forms tight beams that provide line-of-sight range measurements as well as azimuth measurements. To take advantage of these two types of measurements a new navigation algorithm – Position and Attitude Modelling System (PAMS) – is proposed for processing carrier phase and azimuth measurements via an unscented Kalman filter. The PAMS can output the complete navigation parameters of position, velocity, acceleration and attitude simultaneously. The indoor test was conducted in a metal warehouse and the results confirmed that the horizontal positioning solutions had an accuracy of better than four centimetres and an orientation accuracy of better than 1°.

An unscented Kalman filter based navigation algorithm for autonomous underwater vehicles

Mechatronics ◽  
2016 ◽  
Vol 39 ◽  
pp. 185-195 ◽  
Author(s):  
B. Allotta ◽  
A. Caiti ◽  
L. Chisci ◽  
R. Costanzi ◽  
F. Di Corato ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Navigation Algorithm
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