An Optimal Cooperative Navigation Algorithm based on Factor Graph for Pedestrians

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.

Download Full-text

TDMA Datalink Cooperative Navigation Algorithm Based on INS/JTIDS/BA

Electronics ◽

10.3390/electronics10070782 ◽

2021 ◽

Vol 10 (7) ◽

pp. 782

Author(s):

Shuo Cao ◽

Honglei Qin ◽

Li Cong ◽

Yingtao Huang

Keyword(s):

Distribution System ◽

Large Scale ◽

Satellite System ◽

Time Of Arrival ◽

Military Operations ◽

Position Information ◽

Cooperative Navigation ◽

Navigation Algorithm ◽

Geometric Dilution Of Precision ◽

Global Navigation Satellite

Position information is very important tactical information in large-scale joint military operations. Positioning with datalink time of arrival (TOA) measurements is a primary choice when a global navigation satellite system (GNSS) is not available, datalink members are randomly distributed, only estimates with measurements between navigation sources and positioning users may lead to a unsatisfactory accuracy, and positioning geometry of altitude is poor. A time division multiple address (TDMA) datalink cooperative navigation algorithm based on INS/JTIDS/BA is presented in this paper. The proposed algorithm is used to revise the errors of the inertial navigation system (INS), clock bias is calibrated via round-trip timing (RTT), and altitude is located with height filter. The TDMA datalink cooperative navigation algorithm estimate errors are stated with general navigation measurements, cooperative navigation measurements, and predicted states. Weighted horizontal geometric dilution of precision (WHDOP) of the proposed algorithm and the effect of the cooperative measurements on positioning accuracy is analyzed in theory. We simulate a joint tactical information distribution system (JTIDS) network with multiple members to evaluate the performance of the proposed algorithm. The simulation results show that compared to an extended Kalman filter (EKF) that processes TOA measurements sequentially and a TDMA datalink navigation algorithm without cooperative measurements, the TDMA datalink cooperative navigation algorithm performs better.

Download Full-text

Multi-UAV Cooperative Navigation Algorithm Based on Federated Filtering Structure

2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC) ◽

10.1109/gncc42960.2018.9018684 ◽

2018 ◽

Author(s):

Xiaoyang Liu ◽

Shenghong Xu

Keyword(s):

Cooperative Navigation ◽

Navigation Algorithm ◽

Federated Filtering ◽

Multi Uav

Download Full-text

Implementation of a Cooperative Navigation Algorithm on a Platoon of Autonomous Underwater Vehicles

10.1109/oceans.2007.4449404 ◽

2007 ◽

Cited By ~ 26

Author(s):

Darren K. Maczka ◽

Aditya S. Gadre ◽

Daniel J. Stilwell

Keyword(s):

Autonomous Underwater Vehicles ◽

Underwater Vehicles ◽

Cooperative Navigation ◽

Navigation Algorithm

Download Full-text

An Exact Decentralized Cooperative Navigation Algorithm for Acoustically Networked Underwater Vehicles with Robustness to Faulty Communication: Theory and Experiment

Robotics: Science and Systems IX ◽

10.15607/rss.2013.ix.010 ◽

2013 ◽

Cited By ~ 5

Author(s):

Jeffrey Walls ◽

Ryan Eustice

Keyword(s):

Communication Theory ◽

Underwater Vehicles ◽

Cooperative Navigation ◽

Navigation Algorithm ◽

Theory And Experiment

Download Full-text

A multi-AUV cooperative navigation method

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1207/1/012002 ◽

2021 ◽

Vol 1207 (1) ◽

pp. 012002

Author(s):

Yang Shao ◽

Qinghua Luo ◽

Chao Liu ◽

Xiaozhen Yan ◽

Kexin Yang

Keyword(s):

State Space ◽

Nonlinear Filtering ◽

Autonomous Underwater Vehicles ◽

State Space Model ◽

State Variables ◽

Cooperative Navigation ◽

Navigation Algorithm ◽

Cubature Kalman Filter ◽

Traditional Algorithm ◽

Navigation Method

Abstract Cooperative navigation is one of the key methods for multiple autonomous underwater vehicles (AUVs) to obtain accurate positions when performing tasks underwater. In the realistic state-space model of the multi-AUV cooperative navigation system, where the system noise does not satisfy the additivity, it is necessary to augment the dimension of the state variables before nonlinear filtering. Aiming at the problem that the error of traditional algorithms increases linearly with the dimension of state-space, a cooperative navigation method based on Augmented Embedded Cubature Kalman filter (AECKF) algorithm is proposed. The experiment results show that the AECKF cooperative navigation algorithm has better positioning accuracy and stability than the traditional algorithm.

Download Full-text