Model Based Data Transmission: Analysis of Link Budget Requirement Reduction

DOI: 10.4236/cn.2012.44032   PDF   HTML     4,299 Downloads   6,794 Views   Citations

Abstract

Communications capability can be a significant constraint on the utility of a spacecraft. While conventionally enhanced through the use of a larger transmitting or receiving antenna or through augmenting transmission power, communications capability can also be enhanced via incorporating more data in every unit of transmission. Model Based Transmission Reduction (MBTR) increases the mission utility of spacecraft via sending higher-level messages which rely on preshared (or, in some cases, co-transmitted) data. Because of this a priori knowledge, the amount of information contained in a MBTR message significantly exceeds the amount the amount of information in a conventional message. MBTR has multiple levels of operation; the lowest, Model Based Data Transmission (MBDT), utilizes a pre-shared lower-resolution data frame, which is augmented in areas of significant discrepancy with data from the higher-resolution source. MBDT is examined, in detail, herein and several approaches to minimizing the required bandwidth for conveying data required to conform to a minimum level of accuracy are considered. Also considered are ways of minimizing transmission requirements when both a model and change data required to attain a desired minimum discrepancy threshold must be transmitted. These possible solutions are compared to alternate transmission techniques including several forms of image compression.

Share and Cite:

J. Straub, "Model Based Data Transmission: Analysis of Link Budget Requirement Reduction," Communications and Network, Vol. 4 No. 4, 2012, pp. 278-287. doi: 10.4236/cn.2012.44032.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] M. Bozzi, M. Cametti, M. Fornaroli, P. Maguire, S. Marti, M. Pasian, K. Perregrini and S. Rawson, “Future Architectures for European Space Agency Deep-Space Ground Stations [Antenna Applications Corner],” Antennas and Propagation Magazine, Vol. 54, No. 1, 2012, pp. 254-263. doi:10.1109/MAP.2012.6202560
[2] J. Schou, et al., “Design and Ground Calibration of the Helioseismic and Magnetic Imager (HMI) Instrument on the Solar Dynamics Observatory (SDO),” Solar Physics, Vol. 275, No. 1-2, 2012, pp. 229-259. doi:10.1007/s11207-011-9842-2
[3] D. Barret, et al., “The High Time Resolution Spectrometer (HTRS) aboard the International X-ray Observatory (IXO),” Proceedings of SPIE, Vol. 2011, No. 3, pp. 77321M
[4] K. Cheung, M. Belongie and K. Tong, “End-to-End System Consideration of the Galileo Image Compression System,” TDA Progress Report 42-126, 1996.
[5] R. Lutz, “Software Engineering for Space Exploration,” IEEE Computer, Vol. 44, No. 10, 2011, pp. 41-46. doi:10.1109/MC.2011.264
[6] L. Faria, L. Fonseca and M. Costa, “Performance Evaluation of Data Compression Systems Applied to Satellite Imagery,” Journal of Electrical and Computer Engineering, Vol. 2012, No. 2012, 2012, 15 p. doi:10.1155/2012/471857
[7] J. Straub, “Reducing Link Budget Requirements with Model-Based Transmission Reduction Techniques,” Proceedings of the 26th Annual AIAA/USU Conference on Small Satellites, Logan, 13-16 August 2012.
[8] J. Straub, “Increasing Interplanetary CubeSat Mission Science Return with Model Based Transmission Reduction,” The 1st Interplanetary CubeSat Workshop, Boston, 2012.
[9] M. Trifas and J. Straub, “A Comparison of Techniques for Super-Resolution Evaluation,” Proceedings of the IS&T/SPIE Electronic Imaging Conference, Burlingame, 22 January 2012. doi:10.1117/12.912172
[10] M. Trifas and J. Straub, “Super Resolution: A Database Driven Inference Approach,” Proceedings of the 15th World Multi-Conference on Systemics, Cybernetics and Informatics, Orlando, 19-22 July 2011.
[11] M. Kharrazi, H. Sencar and N. Memon, “Blind Source Camera Identification,” Proceedings of the 2005 International Conference on Image Processing, Genoa, 11-14 September 2005, pp. 69-72.
[12] Z. Whang, A. Bovik, H. Sheikh and E. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Processing, Vol. 13, No. 4, 2004, pp. 600-612.

  
comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.