Neural-network-based satellite tracking for deep space applications

Farid Amoozegar; Charles Ruggier

doi:10.1117/12.488688

16 September 2003 Neural-network-based satellite tracking for deep space applications

Farid Amoozegar, Charles Ruggier

Proceedings Volume 5094, Automatic Target Recognition XIII; (2003) https://doi.org/10.1117/12.488688
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

NASA has been considering the use of Ka-band for deep space missions primarily for downlink telemetry applications. At such high frequencies, although the link will be expected to improve by a factor of four, the current Deep Space Network (DSN) antennas and transmitters would become less efficient due to higher equipment noise figures and antenna surface errors. Furthermore, the weather effect at Ka-band frequencies will dominate the degradations in link performance and tracking accuracy. At the lower frequencies, such as X-band, conventional CONSCAN or Monopulse tracking techniques can be used without much complexity, however, when utilizing Ka-band frequencies, the tracking of a spacecraft in deep space presents additional challenges. The objective of this paper is to provide a survey of neural network trends as applied to the tracking of spacecrafts in deep space at Ka-band under various weather conditions, and examine the trade-off between tracking accuracy and communication link performance.

Citation Download Citation

Farid Amoozegar and Charles Ruggier "Neural-network-based satellite tracking for deep space applications", Proc. SPIE 5094, Automatic Target Recognition XIII, (16 September 2003); https://doi.org/10.1117/12.488688

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