scholarly journals SAR Mocomp by machine Learning

Author(s):  
Brianna Christensen ◽  
Enson Chang ◽  
Nathaniel Tamminga

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.

Author(s):  
Tuncay Yunus Erkec ◽  
Chingiz Hajiyev

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.


2012 ◽  
Vol 4 (4) ◽  
pp. 408-413
Author(s):  
Ramūnas Kikutis ◽  
Darius Rudinskas

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.


2020 ◽  
pp. 1-19
Author(s):  
E. I. Starovoytov

Currently, unmanned aerial vehicles (UAVs) can be used in topographic works, condition monitoring and diagnostics of extended engineering structures, delivering goods to hard-to-reach places, etc. To provide the widespread UAVs applications and raise the number of tasks to be solved through their using, it is necessary to increase their autonomy degree in terms of navigation support, in particular. Unmanned aerial vehicles (UAV) control systems for autonomous navigation use the strap-down inertial navigation systems (SINS) based on various types of gyroscopes. SINS based on the laser gyroscopes, which have a large mass, have the best accuracy. UAVs with a payload mass that is commensurable with the mass of navigation equipment require optimization of SINS characteristics. An optimization method has been developed to enable obtaining a Pareto set for the mass and accuracy of SINS based on laser gyroscopes. A comprehensive assessment of the characteristics of SINS and UAV carrier with different payload mass has been performed. Various SINS correction methods are considered when satellite navigation is unavailable.For overland flights, the correlation-extreme navigation systems (CENS) and SLAM methods (for simultaneous localisation and mapping) can be used. CENS require a reference lay-of-the-land description and a sufficient density of landmarks. In navigation based on SLAM algorithms, there is no need in the reference lay-of-the-land description, and the initial data can be obtained through the optical sensors under appropriate condition of the atmospheric path.Regardless of the condition of the atmospheric path, type of the underlying surface and its information available in detail, the UAV coordinates can be determined by Doppler dead reckoning using a Doppler system (DISS). At low and medium altitudes SINS correction is possible, only heading sensor data are needed to calculate the path angle.In combining with DISS and 3D Flash Ladar sensors (for implementing SLAM algorithms), it is more optimal to use low-accuracy SINS based on fibre-optic gyroscopes rather than laser gyro-based systems.The results obtained can be used when developing navigation systems for medium, light and heavy-medium UAVs.


2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations


2012 ◽  
Author(s):  
H. Zargarzadeh ◽  
David Nodland ◽  
V. Thotla ◽  
S. Jagannathan ◽  
S. Agarwal

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