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 :
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