scholarly journals Collaborative Autonomous Vehicles with Signals of Opportunity Aided Inertial Navigation Systems

2017 ◽  
Author(s):  
Joshua J. Morales ◽  
Joe Khalife ◽  
Zaher M. Kassas
Keyword(s):  
Autonomous Vehicles ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Signals Of Opportunity

scholarly journals Learning to Localise Automated Vehicles in Challenging Environments Using Inertial Navigation Systems (INS)

2021 ◽  
Vol 11 (3) ◽  
pp. 1270
Author(s):  
Uche Onyekpe ◽  
Vasile Palade ◽  
Stratis Kanarachos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Autonomous Vehicles ◽  
Short Term Memory ◽  
Inertial Navigation ◽  
Satellite System ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Displacement Estimation ◽  
Global Navigation Satellite

An approach based on Artificial Neural Networks is proposed in this paper to improve the localisation accuracy of Inertial Navigation Systems (INS)/Global Navigation Satellite System (GNSS) based aided navigation during the absence of GNSS signals. The INS can be used to continuously position autonomous vehicles during GNSS signal losses around urban canyons, bridges, tunnels and trees, however, it suffers from unbounded exponential error drifts cascaded over time during the multiple integrations of the accelerometer and gyroscope measurements to position. More so, the error drift is characterised by a pattern dependent on time. This paper proposes several efficient neural network-based solutions to estimate the error drifts using Recurrent Neural Networks, such as the Input Delay Neural Network (IDNN), Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (vRNN), and Gated Recurrent Unit (GRU). In contrast to previous papers published in literature, which focused on travel routes that do not take complex driving scenarios into consideration, this paper investigates the performance of the proposed methods on challenging scenarios, such as hard brake, roundabouts, sharp cornering, successive left and right turns and quick changes in vehicular acceleration across numerous test sequences. The results obtained show that the Neural Network-based approaches are able to provide up to 89.55% improvement on the INS displacement estimation and 93.35% on the INS orientation rate estimation.

scholarly journals A Dynamic Calibration Method of Installation Misalignment Angles between Two Inertial Navigation Systems

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2947
Author(s):  
Ming Hua ◽  
Kui Li ◽  
Yanhong Lv ◽  
Qi Wu
Keyword(s):  
Autonomous Navigation ◽  
Inertial Navigation ◽  
Calibration Method ◽  
Measurement Information ◽  
Dynamic Calibration ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Additional Information ◽  
Two Systems ◽  
Dynamic Calibration Method

Generally, in order to ensure the reliability of Navigation system, vehicles are usually equipped with two or more sets of inertial navigation systems (INSs). Fusion of navigation measurement information from different sets of INSs can improve the accuracy of autonomous navigation effectively. However, due to the existence of misalignment angles, the coordinate axes of different systems are usually not in coincidence with each other absolutely, which would lead to serious problems when integrating the attitudes information. Therefore, it is necessary to precisely calibrate and compensate the misalignment angles between different systems. In this paper, a dynamic calibration method of misalignment angles between two systems was proposed. This method uses the speed and attitude information of two sets of INSs during the movement of the vehicle as measurements to dynamically calibrate the misalignment angles of two systems without additional information sources or other external measuring equipment, such as turntable. A mathematical model of misalignment angles between two INSs was established. The simulation experiment and the INSs vehicle experiments were conducted to verify the effectiveness of the method. The results show that the calibration accuracy of misalignment angles between the two sets of systems can reach to 1″ while using the proposed method.

Analysis of Three Velocity Plus Attitude Matching Methods for Transfer Alignment

2012 ◽  
Vol 433-440 ◽  
pp. 2802-2807
Author(s):  
Ying Hong Han ◽  
Wan Chun Chen
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Misalignment Angle ◽  
Inertial Navigation Systems ◽  
Excellent Performance ◽  
Transfer Alignment ◽  
Matching Methods ◽  
Moving Base

For inertial navigation systems (INS) on moving base, transfer alignment is widely applied to initialize it. Three velocity plus attitude matching methods are compared. And Kalman filter is employed to evaluate the misalignment angle. Simulations under the same conditions show which scheme has excellent performance in precision and rapidness of estimations.

Initial Alignment of a Moving Inertial Navigation System

1960 ◽  
Vol 13 (3) ◽  
pp. 301-315
Author(s):  
Richard B. Seeley ◽  
Roy Dale Cole
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Local Level ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Velocity Measurements ◽  
Initial Alignment ◽  
Inertial Navigation Systems ◽  
Error Sources ◽  
China Lake

This paper describes and discusses some of the techniques by which a moving inertial platform may be aligned by using external velocity measurements and also presents some of the major problems and error sources affecting such alignment. It is based upon the results of a 3-year study, of inertial and doppler-inertial navigation at the Naval Ordnance Test Station, China Lake, California, and, in general, applies to inertial navigation systems which erect to either the local level or the mass-attraction vertical. Although rudimentary derivations are made of the alignment techniques, the paper is largely nonmathematical for ease of reading. Emphasis is placed upon the major errors affecting the alignment. This paper describes and discusses some of the techniques by which a moving inertial platform may be aligned by using external velocity measurements and also presents some of the major problems and error sources affecting such alignment. It is based upon the results of a 3-year study, of inertial and doppler-inertial navigation at the Naval Ordnance Test Station, China Lake, California, and, in general, applies to inertial navigation systems which erect to either the local level or the mass-attraction vertical. Although rudimentary derivations are made of the alignment techniques, the paper is largely nonmathematical for ease of reading. Emphasis is placed upon the major errors affecting the alignment.

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