DocumentCode
1938287
Title
SDR input power estimation algorithms
Author
Briones, J.C. ; Nappier, J.M.
Author_Institution
NASA Glenn Res. Center, Cleveland, OH, USA
fYear
2013
fDate
2-9 March 2013
Firstpage
1
Lastpage
9
Abstract
The General Dynamics (GD) S-Band software defined radio (SDR) in the Space Communications and Navigation (SCAN) Testbed on the International Space Station (ISS) provides experimenters an opportunity to develop and demonstrate experimental waveforms in space. The SDR has an analog and a digital automatic gain control (AGC) and the response of the AGCs to changes in SDR input power and temperature was characterized prior to the launch and installation of the SCAN Testbed on the ISS. The AGCs were used to estimate the SDR input power and SNR of the received signal and the characterization results showed a nonlinear response to SDR input power and temperature. In order to estimate the SDR input from the AGCs, three algorithms were developed and implemented on the ground software of the SCAN Testbed. The algorithms include a linear straight line estimator, which used the digital AGC and the temperature to estimate the SDR input power over a narrower section of the SDR input power range. There is a linear adaptive filter algorithm that uses both AGCs and the temperature to estimate the SDR input power over a wide input power range. Finally, an algorithm that uses neural networks was designed to estimate the input power over a wide range. This paper describes the algorithms in detail and their associated performance in estimating the SDR input power.
Keywords
adaptive filters; artificial satellites; automatic gain control; control engineering computing; digital control; neural nets; satellite communication; GD S-band software defined radio; ISS; SCAN testbed installation; SDR input power estimation algorithms; SNR; digital AGC; digital automatic gain control; general dynamics S-band software defined radio; ground software; linear adaptive filter algorithm; navigation testbed; neural networks; nonlinear response; space communication; Biological neural networks; Equations; Gain control; Mathematical model; Temperature distribution; Temperature measurement; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Aerospace Conference, 2013 IEEE
Conference_Location
Big Sky, MT
ISSN
1095-323X
Print_ISBN
978-1-4673-1812-9
Type
conf
DOI
10.1109/AERO.2013.6497193
Filename
6497193
Link To Document