Experimental Estimates of Angular Coordinates and Location of Radio Emission Sources in Unmanned Aircraft Monitoring Systems

2021 ◽  
Vol 64 (3) ◽  
pp. 562-570
Author(s):  
V. A. Shevtsov ◽  
V. V. Kirdyashkin ◽  
A. V. Timoshenko ◽  
S. N. Razin’kov
Keyword(s):  
Radio Emission ◽  
Unmanned Aircraft ◽  
Monitoring Systems ◽  
Emission Sources ◽  
Angular Coordinates

scholarly journals Energy budget and methods for determining coordinates for a radiomonitoring system based on a small spacecraft

2021 ◽  
Vol 21 (2) ◽  
pp. 945
Author(s):  
Altay Aitmagambetov ◽  
Yuri Butuzov ◽  
Yuri Butuzov ◽  
Valery Tikhvinskiy ◽  
Valery Tikhvinskiy ◽  
...  
Keyword(s):  
Radio Emission ◽  
Energy Budget ◽  
Radio Spectrum ◽  
Monitoring Systems ◽  
Emission Sources ◽  
Radio Lines ◽  
Spectrum Monitoring ◽  
Small Spacecraft ◽  
Radio Monitoring ◽  
The Republic

The existing ground-based radio monitoring systems do not allow performing the functions and tasks of radio spectrum monitoring in a quality manner. Therefore, to improve the efficiency of the radio spectrum monitoring systems for countries with a large territory, such as the Republic of Kazakhstan, it is proposed to use low-orbit small spacecrafts as radio monitoring stations. The analysis of the energy budget of radio lines on the basis of existing radio electronic means on the territory of the Republic of Kazakhstan, carried out in this work, showed the possibility of using low-orbit small spacecrafts for performing the functions and tasks of radio monitoring. The paper proposes and develops a method for determining the coordinates of radio emission sources based on the goniometric method using scanning antennas on board of one spacecraft. The ranges of the antenna scanning angles are substantiated, and the estimates of the coordinates determination errors are made. Algorithms have been developed and computer programs have been compiled to determine the coordinates of the radio emission sources, which will make it possible to use this method at the initial stages of developing a radio spectrum monitoring system based on one small spacecraft.

Radio Monitoring Systems and Determination of Radio Emission Sources Location

2009 ◽  
pp. 317-391
Author(s):  
Anatoly Rembovsky ◽  
Alexander Ashikhmin ◽  
Vladimir Kozmin ◽  
Sergey Smolskiy
Keyword(s):  
Radio Emission ◽  
Monitoring Systems ◽  
Emission Sources ◽  
Radio Monitoring

Nonlinear Phase Radio-Range Station for Measuring Distance to Radio Emission Sources

2007 ◽  
Vol 66 (1) ◽  
pp. 63-67
Author(s):  
N. I. Kozachek ◽  
Vladimir B. Avdeev ◽  
D. V. Senkevich ◽  
S. N. Panychev
Keyword(s):  
Radio Emission ◽  
Emission Sources ◽  
Nonlinear Phase ◽  
Radio Range

Multi-channel interference source detector

2021 ◽  
Author(s):  
V.N. Antipov ◽  
S.L. Ivanov ◽  
E.Е. Koltyshev ◽  
V.V. Mukhin ◽  
A.Yu. Frolov ◽  
...  
Keyword(s):  
Radio Emission ◽  
Radiation Pattern ◽  
Correlation Matrix ◽  
Cross Correlation ◽  
Emission Sources ◽  
Energy Correlation ◽  
Antenna Radiation Pattern ◽  
Channel Interference ◽  
Main Disadvantage ◽  
The Cross

Modern radars, along with the detection and measurement of target coordinates against the background of interference, must solve the problem of detecting radio emission sources and measuring their coordinates. Detection of interference, as well as targets, in the radar is provided in the main (total) channel based on the analysis of the rangefinder-Doppler portrait of the received signal. The main disadvantage of such a detector is that the interference coming along the side lobes of the sum antenna and falling into the dip of the antenna radiation pattern may not be detected. Therefore, the problem arises of developing and analyzing algorithms for detecting interference in a radar with several receiving channels. The article discusses the logical, energy, correlation and eigenvalues of the cross-correlation matrix of the received signals interference detectors for two receiving channels. Their characteristics are given. It is shown that two-channel interference detectors based on the analysis of the eigenvalues of the cross-correlation matrix have the highest efficiency. Energy and logical algorithms are quite a bit inferior to them. The developed algorithms make it possible to effectively detect radio emission sources even when they are in the dip of one of the antenna patterns.

Localization of Radio Emission Sources

2018 ◽  
pp. 297-336
Author(s):  
Anatoly M. Rembovsky ◽  
Alexander V. Ashikhmin ◽  
Vladimir A. Kozmin ◽  
Sergey M. Smolskiy
Keyword(s):  
Radio Emission ◽  
Emission Sources

Classification of OH Radio Emission Sources

1968 ◽  
Vol 153 ◽  
pp. L41 ◽  
Author(s):  
John A. Ball ◽  
David H. Staelin
Keyword(s):  
Radio Emission ◽  
Emission Sources

scholarly journals Calculation of spatial target coordinates in range-difference passive radars by the Levenberg – Marquardt method

Doklady BGUIR ◽  
2020 ◽  
Vol 18 (5) ◽  
pp. 35-43
Author(s):  
A. A. Dmitrenko ◽  
S. Y. Sedyshev ◽  
Y. У. Kuleshov ◽  
A. A. Bogatyrev
Keyword(s):  
Radio Emission ◽  
Optimal Number ◽  
Average Error ◽  
Emission Sources ◽  
Passive Radar ◽  
Marquardt Method ◽  
Modified Newton’S Method ◽  
Levenberg Marquardt ◽  
Range Difference ◽  
Spatial Coordinates

This article studies and analyzes the results of applying numerical iterative methods for solving nonlinear equation systems (Newton, modified Newton's method, gradient descent, sequential iterations, Levenberg – Marquardt), compiled and used to calculate the rectangular spatial coordinates of radio emission sources in range-difference passive radars of various configurations (incorporating from 3 to 4 receiving points). The aim of the research was to determine the optimal number of receiving points and to select the most effective algorithm for coordinate transformations of the vector of observed parameters (a set of range difference estimates from radio emission sources to the corresponding pairs of receiving points) into the vector of measured parameters (rectangular spatial coordinates). The following parameters were used as comparison criteria: passive radar working area (a part of space where the deviation of target coordinate estimates from their true values does not exceed the maximum tolerable values); average error in calculating spatial coordinates in the working area; iterations number of coordinate calculation in the analyzed part of space. Upon completing a comparative analysis of obtained characteristics and dependencies, we concluded that it is optimal to include four receiving points in a range-difference passive radar and use the Levenberg – Marquardt method to calculate the spatial coordinates of radio emission sources.

scholarly journals Use of an Unmanned Aircraft System to Quantify NO<sub>x</sub> Emissions from a Natural Gas Boiler

2020 ◽  
Author(s):  
Brian Gullett ◽  
Johanna Aurell ◽  
William Mitchell ◽  
Jennifer Richardson
Keyword(s):  
Natural Gas ◽  
Weighted Average ◽  
Unmanned Aircraft ◽  
Monitoring Systems ◽  
Sampling System ◽  
Emission Sampling ◽  
Relative Percent Difference ◽  
Aircraft System ◽  
Carbon Dioxide Co2 ◽  
Weight Sensor

Abstract. Aerial emission sampling of four natural gas boiler stack plumes was conducted using an unmanned aerial system (UAS) equipped with a light-weight sensor/sampling system (the “Kolibri”) for measurement of nitrogen oxide (NO), and nitrogen dioxide (NO2), carbon dioxide (CO2), and carbon monoxide (CO). Flights (n = 22) ranged from 11 to 24 minutes duration at two different sites. The UAS was maneuvered into the plumes with the aid of real-time CO2 telemetry to the ground operators and, at one location, a second UAS equipped with an infrared/visible camera. Concentrations were collected and recorded at 1 Hz. The maximum CO2, CO, NO, and NO2 concentrations in the plume measured were 10,000 ppm, 7 ppm, 27 ppm, and 1.5 ppm, respectively. Comparison of the NOx emissions between the stack continuous emission monitoring systems and the UAS/Kolibri for three boiler sets showed an average of 5.6 % and 3.5 % relative percent difference for the run-weighted and carbon-weighted average emissions, respectively.

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