Research on on-orbit SEU characterization of the signal processing platform

DOI

10.2991/aiea-16.2016.60How to use a DOI?

Keywords

Signal processing platform; FPGA; Single event upset; Solar proton event.

Abstract

Satellites have suffered many single event upset (SEU) abnormalities in the past decades. To get a detailed SEU characterization of the on-orbit signal processing platform, we installed surveillance programs on 48 signal processing platforms mounted on 24 low-orbiting satellites and monitored the Virtex-II FPGAs. We found 43,213 SEUs at 39,456 monitoring days. The observation window covers a typical rising edge of the 24th solar cycle from March 2010 to April 2015. Results show that the mean SEU rates of (500km, 700km and 1,100km) orbits are (0.122, 0.318 and 1.77)/device-day, and the average SEU rate is higher in the solar quiet period. Combined with the proton event data, it is found that a strong solar proton event (SPE) with high proton flux can cause a significant increase in the SEU rate on the 700km and 1100km orbits. Within one to two days after the maximum peak of the proton flux, the SEU rate reaches the maximum. This finding provides an important reference value for the prediction of the single event upset of signal processing platform. The reliability of the signal processing platform in the space environment can be predicted and evaluated by observing the change of the solar activity for a specific period of time, so as to avoid the space radiation risk by designing the anti - radiation reinforcement scheme.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Pengcheng Liu
AU  - Kefei Xing
AU  - Wei He
AU  - Zelong Zhang
AU  - Wei Deng
PY  - 2016/11
DA  - 2016/11
TI  - Research on on-orbit SEU characterization of the signal processing platform
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications
PB  - Atlantis Press
SP  - 335
EP  - 340
SN  - 2352-538X
UR  - https://doi.org/10.2991/aiea-16.2016.60
DO  - 10.2991/aiea-16.2016.60
ID  - Liu2016/11
ER  -