Radar cross-section calculation for unmanned aerial vehicle

2014 ◽  
Author(s):  
I. Ryapolov ◽  
O. Sukharevsky ◽  
V. Vasilets
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Cross Section ◽  
Radar Cross Section ◽  
Aerial Vehicle

scholarly journals Evaluation of Radar Cross Section in Aerial Operations by using POFACETS for a standard Unmanned Aerial Vehicle (UAV)

2019 ◽  
Vol 8 (10) ◽  
pp. 4629-4633
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Cross Section ◽  
Measurement Techniques ◽  
Radar Cross Section ◽  
Security Threat ◽  
Aspect Angle ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
Surveillance Applications ◽  
Erroneous Data

In military and common applications, radar assumes an exceptionally noteworthy job in exploring the vehicle, recognizing for flying machines, ships and so on. Radar cross section is a measure of a target's ability to reflect radar signals in the direction of the receiver. It is obvious that RCS depends on the shape of the reflecting objects. In this paper, a brief overview of various radars used in surveillance applications and radar cross section (RCS) measurement techniques are presented. The objective of this paper is to illustrate methods for obtaining the Radar Cross Section (RCS) of a typical Unmanned Aerial Vehicle (UAV) for various frequencies in radar bands. UAV technologies have advanced tremendously and are being developed successfully. These features lead UAV to patrol even in civil airspace for civilian applications. These objects operate with small radar cross-section and/or on low altitude which imposes security threat. Keeping this view the RCS of a UAV which are flying in civilian airspace has to be determined drastically for security purposes. Several shapes are considered for the estimation of RCS. The results on the variations of RCS for the above objects as a function of frequency, aspect angle are presented. The POFACETS plays a crucial role and provides the user an easy–to–use GUI that allows the input of all necessary parameters, while preventing erroneous data input and other user errors. In this paper the RCS of sphere, ellipsoid and UAV are determined.

scholarly journals Radar Cross Section Analysis of Unmanned Combat Aerial Vehicle (UCAV) using FEKO Software

AVIA ◽  
2021 ◽  
Vol 2 (2) ◽  
Author(s):  
J P Sijabat ◽  
T Indriyanto
Keyword(s):  
Cross Section ◽  
Electromagnetic Waves ◽  
Technology Development ◽  
Fast Multipole Method ◽  
Radar Cross Section ◽  
Simulation Software ◽  
Factors Affecting ◽  
Aerial Vehicle ◽  
Computational Resources ◽  
Computational Calculation

Radar technology development encourages each country to develop military aircraft with small Radar Cross Section (RCS) size to bring out stealth behaviour, so that it is not easily detected by the enemy. In designing an airplane, computational methods become one of the best solutions in simulating the behaviour of an aircraft geometry when illuminated by electromagnetic waves. On this study, a calculation simulation of the RCS value was performed using FEKO (FElding bei Körn mit beliebiger Oberfläche) EM Simulation software for unmanned combat aerial vehicles (UCAV). Simulations are carried out in various conditions to find out factors affecting RCS value. These factors were analysed by varying radar frequency, material coating the plane, and methods of computational calculation. The results show that the greater the frequency, the greater the computational resources required as on higher number of mesh, more time needed to run the simulation, and required memory. However, the frequency is not directly proportional to the RCS value of the object. Methods of Momentum (MoM) and Multilevel Fast Multipole Method (MLFMM) perform computation calculations that are more detailed and more accurate comparedto Physical Optic (PO) full-ray tracing

scholarly journals Technical Note: Flow velocity and discharge measurement in rivers using terrestrial and unmanned-aerial-vehicle imagery

2020 ◽  
Vol 24 (3) ◽  
pp. 1429-1445 ◽  
Author(s):  
Anette Eltner ◽  
Hannes Sardemann ◽  
Jens Grundmann
Keyword(s):  
Flow Velocity ◽  
Unmanned Aerial Vehicle ◽  
Cross Section ◽  
Flow Characteristics ◽  
Technical Note ◽  
Surface Flow ◽  
Contactless Measurement ◽  
Aerial Vehicle ◽  
The Impact ◽  
Flow Velocities

Abstract. An automatic workflow to measure surface flow velocities in rivers is introduced, including a Python tool. The method is based on particle-tracking velocimetry (PTV) and comprises an automatic definition of the search area for particles to track. Tracking is performed in the original images. Only the final tracks are geo-referenced, intersecting the image observations with water surface in object space. Detected particles and corresponding feature tracks are filtered considering particle and flow characteristics to mitigate the impact of sun glare and outliers. The method can be applied to different perspectives, including terrestrial and aerial (i.e. unmanned-aerial-vehicle; UAV) imagery. To account for camera movements images can be co-registered in an automatic approach. In addition to velocity estimates, discharge is calculated using the surface velocities and wetted cross section derived from surface models computed with structure-from-motion (SfM) and multi-media photogrammetry. The workflow is tested at two river reaches (paved and natural) in Germany. Reference data are provided by acoustic Doppler current profiler (ADCP) measurements. At the paved river reach, the highest deviations of flow velocity and discharge reach 4 % and 5 %, respectively. At the natural river highest deviations are larger (up to 31 %) due to the irregular cross-section shapes hindering the accurate contrasting of ADCP- and image-based results. The provided tool enables the measurement of surface flow velocities independently of the perspective from which images are acquired. With the contactless measurement, spatially distributed velocity fields can be estimated and river discharge in previously ungauged and unmeasured regions can be calculated, solely requiring some scaling information.

Radar Cross Section

2004 ◽  
Author(s):  
Eugene F. Knott ◽  
John F. Shaeffer ◽  
Michael T. Tuley
Keyword(s):  
Cross Section ◽  
Radar Cross Section

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle

Results of Spatial Structure of Atmosphere Radiation in a Spectral Range (1.5–2) µm Research

2018 ◽  
pp. 7-13
Author(s):  
Anton M. Mishchenko ◽  
Sergei S. Rachkovsky ◽  
Vladimir A. Smolin ◽  
Igor V . Yakimenko
Keyword(s):  
Spatial Structure ◽  
Unmanned Aerial Vehicle ◽  
Wavelength Range ◽  
Spectral Range ◽  
Near Ir ◽  
Optoelectronic Systems ◽  
Aerial Vehicle

Results of experimental studying radiation spatial structure of atmosphere background nonuniformities and of an unmanned aerial vehicle being the detection object are presented. The question on a possibility of its detection using optoelectronic systems against the background of a cloudy field in the near IR wavelength range is also considered.

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