Opportunities and Challenges of Terrain Aided Navigation Systems for Aerial Surveillance by Unmanned Aerial Vehicles

2012 ◽  
pp. 163-177 ◽  
Author(s):  
Samil Temel ◽  
Numan Unaldi
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Navigation Systems ◽  
Aerial Surveillance ◽  
Aerial Vehicles

scholarly journals Relative Navigation in UAV Applications

2020 ◽  
Vol vm01 (is02) ◽  
pp. 52-65
Author(s):  
Tuncay Yunus Erkec ◽  
Chingiz Hajiyev
Keyword(s):  
State Estimation ◽  
Unmanned Aerial Vehicles ◽  
Satellite System ◽  
Formation Flight ◽  
Navigation Systems ◽  
Relative Navigation ◽  
Inertial Navigation Systems ◽  
Estimation Algorithms ◽  
Aerial Vehicles ◽  
Global Navigation Satellite

This paper is committed to the relative navigation of Unmanned Aerial Vehicles (UAVs) flying in formation flight. The concept and methods of swarm UAVs technology and architecture have been explained. The relative state estimation models of unmanned aerial vehicles which are based on separate systems as Inertial Navigation Systems (INS)&Global Navigation Satellite System (GNSS), Laser&INS and Vision based techniques have been compared via various approaches. The sensors are used individually or integrated each other via sensor integration for solving relative navigation problems. The UAV relative navigation models are varied as stated in operation area, type of platform and environment. The aim of this article is to understand the correlation between relative navigation systems and potency of state estimation algorithms as well during formation flight of UAV.

scholarly journals SAR Mocomp by machine Learning

2021 ◽  
Author(s):  
Brianna Christensen ◽  
Enson Chang ◽  
Nathaniel Tamminga
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Unmanned Aerial Vehicles ◽  
Motion Compensation ◽  
Synthetic Aperture ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Motion Error ◽  
Aerial Vehicles ◽  
Sudden Motion

All unmanned aerial vehicles that use synthetic aperture radar (SAR) systems are equipped with inertial navigation systems (INS) to reduce motion error. Additional motion compensation (MOCOMP) from the data itself is still necessary to achieve required accuracy of a SAR. An affordable method for small drones has yet to be created. We propose machine learning with deep convolutional neural network (CNN) to extract motion error such as sway (right and left) and surge (forward). Results show that the CNN is capable of recognizing gradual drone motion deviations. It has the potential to pick up sudden motion error as well, overcoming major deficiencies of traditional MOCOMP methods, and the need for INS.

scholarly journals ERROR ANALYSIS OF INS ARRANGED IN ARDUPILOT MEGA / „ARDUPILOT MEGA“ AUTOPILOTO INERCINIO NAVIGACIJOS ĮRENGINIO PAKLAIDŲ ANALIZĖ

2012 ◽  
Vol 4 (4) ◽  
pp. 408-413
Author(s):  
Ramūnas Kikutis ◽  
Darius Rudinskas
Keyword(s):  
Error Analysis ◽  
Automatic Control ◽  
Unmanned Aerial Vehicles ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Aerial Vehicles ◽  
Small Uav ◽  
Huge Impact ◽  
Uav Navigation

Inertial navigation systems (INS) are widely used for controlling piloted or unmanned aerial vehicles (UAV). Automatic control equipment with INS has error budget making a huge impact on the accuracy of UAV navigation. The paper analyzes INS errors and types of errors. Experiments have been done using small UAV. Santrauka Inerciniai navigacijos įrenginiai (INS) plačiai naudojami pilotuojamuose ir nepilotuojamuose orlaiviuose. Nepilotuojamo orlaivio skrydžio tikslumui didelę įtaką turi orlaivio automatinio valdymo sistemos įrenginių paklaidos. Tyrime nagrinėjamas nepilotuojamas orlaivis, kurio automatinio valdymo sistemos dalis yra inercinis navigacijos įrenginys. Analizuojami INS įrenginių paklaidų šaltiniai, paklaidų tipai. Eksperimentiniai tyrimai atlikti naudojant mažo nepilotuojamo orlaivio automatinio valdymo sistemą.

scholarly journals POSE ESTIMATION OF UNMANNED AERIAL VEHICLES BASED ON A VISION-AIDED MULTI-SENSOR FUSION

2016 ◽  
Vol XLI-B6 ◽  
pp. 193-199
Author(s):  
G. Abdi ◽  
F. Samadzadegan ◽  
F. Kurz
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Pose Estimation ◽  
Reference Data ◽  
Low Cost ◽  
Navigation Systems ◽  
Motion Model ◽  
Robust Solution ◽  
Working Cycle ◽  
Aerial Vehicles ◽  
Height Data

GNSS/IMU navigation systems offer low-cost and robust solution to navigate UAVs. Since redundant measurements greatly improve the reliability of navigation systems, extensive researches have been made to enhance the efficiency and robustness of GNSS/IMU by additional sensors. This paper presents a method for integrating reference data, images taken from UAVs, barometric height data and GNSS/IMU data to estimate accurate and reliable pose parameters of UAVs. We provide improved pose estimations by integrating multi-sensor observations in an EKF algorithm with IMU motion model. The implemented methodology has demonstrated to be very efficient and reliable for automatic pose estimation. The calculated position and attitude of the UAV especially when we removed the GNSS from the working cycle clearly indicate the ability of the purposed methodology.

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