Abstract
Space domain awareness (SDA) has become increasingly important as industry and society seek further interest in occupying space for surveillance, communication, and environmental services. To maintain safe launch and orbit-placement of future satellites, there is a need to reliably track the positions and trajectories of discarded launch designs that are debris objects orbiting Earth. In particular, debris with sizes on the order of 20 cm or smaller travelling at high speeds maintain enough energy to pierce and permanently damage current, functional satellites. To monitor debris, the Dynamic Data Driven Applications Systems (DDDAS) paradigm can enhance accuracy with object modeling and observational updates. This paper presents a theoretical analysis of modeling the radar returns of space debris as simulated signatures for comparison to real measurements. For radar modeling, when the incident radiation wavelength is comparable to the radius of the debris object, Mie scattering is dominant. Mie scattering describes situations where the radiation scatter propagates predominantly, i.e., contains the greatest power density, along the same direction as the incident wave. Mie scatter modeling is especially useful when tracking objects with forward scatter bistatic radar, as the transmitter, target, and receiver lie along the same geometrical trajectory. The Space Watch Observing Radar Debris Signatures (SWORDS) baseline method involves modeling the radar cross-sections (RCS) of space debris signatures in relation to the velocity and rotational motions of space debris. The results show the impact of the debris radii varying from 20 cm down to 1 cm when illuminated by radiation of comparable wavelength. The resulting scattering nominal mathematical relationships determine how debris size and motion affects the radar signature. The SWORDS method demonstrates that the RCS is proportional to linear size, and that the Doppler shift is predominantly influenced by translation motion.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, G., Pham, K.D., Blasch, E.: Special section guest editorial: sensors and systems for space applications. Opt. Eng. 58(4), 041601 (2019). https://doi.org/10.1117/1.OE.58.4.041601
Erwin, S.: Air Force: SSA is no more; it’s ‘space domain awareness’, SpaceNews, 14 November 2019 (2019). https://spacenews.com/air-force-ssa-is-no-more-its-space-domain-awareness/
Blake, T.: Space domain awareness (SDA) (2011). https://apps.dtic.mil/sti/pdfs/ADA550594.pdf
Holzinger, M.J., Jah, M.K.: Challenges and potential in space domain awareness. J. Guid. Control. Dyn. 41(1), 15–18 (2018). https://doi.org/10.2514/1.G003483
Vasso, A., Cobb, R., Colombi, J., Little, B., Meyer, D.: Augmenting the space domain awareness ground architecture via decision analysis and multi-objective optimization. J. Defense Analytics Logistics 5(1), 77–94 (2021). https://doi.org/10.1108/JDAL-11-2020-0023
Blasch, E., Shen, D., Chen, G., Sheaff, C., Pham, K.: Space object tracking uncertainty analysis with the URREF ontology. In: 2021 IEEE Aerospace Conference, Big Sky, MT (2021).https://doi.org/10.1109/AERO50100.2021.9438207
NASA: Frequently asked questions: Orbital debris (2011). https://www.nasa.gov/news/debris_faq.html
Sciré, G., Santoni, F., Piergentili, F.: Analysis of orbit determination for space based optical space surveillance system. Adv. Space Res. 56(3), 421–428 (2015). https://doi.org/10.1016/j.asr.2015.02.031
Muntoni, G., et al.: Space debris detection in low earth orbit with the Sardinia radio telescope. Electronics 6(3), 59 (2017). https://doi.org/10.3390/electronics6030059
Jia, B., Pham, K.D., Blasch, E., Wang, Z., Shen, D., Chen, G.: Space object classification using deep neural networks. In: 2018 IEEE Aerospace Conference, Big Sky, MT (2018). https://doi.org/10.1109/AERO.2018.8396567
Wong, X.I., Majji, M., Singla, P.: Photometric stereopsis for 3D reconstruction of space objects. In: Chapter 13 in Handbook of Dynamic Data Driven Applications Systems, vol. 1, 2nd Edn. (2022). https://doi.org/10.1007/978-3-319-95504-9_13
Balanis, C.A.: Advanced Engineering Electromagnetics, New York, NY. Wiley, pp. 655–665 (2012)
Wikipedia: Mie scattering. https://en.wikipedia.org/wiki/Mie_scattering
Baker, C.: PCL waveforms: NATO S&T organization educational note STO-EN-SET-243-02 (2018)
Stevenson, M., Nicolls, M., Park, I., Rosner, C.: Measurement precision and orbit tracking performance of the kiwi space radar. In: Advanced Maui Optical and Space Surveillance (AMOS) Technologies Conference (2020). https://amostech.com/technicalpapers/2020/optical-systems-instrumentation/stevenson.pdf
GlobalSecurity.org: Space fence (AFSSS S-Band). https://www.globalsecurity.org/space/systems/space-fence.htm
LeoLabs: Global phased-array radar network. https://leolabs.space/radars/
Ulander, L.M.H., Frölind, P.-O., Gustavsson, A. Ragnarsson. R., Stenström, G.: Airborne passive SAR imaging based on DVB-T signals. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, pp. 2408–2411 (2017). https://doi.org/10.1109/IGARSS.2017.8127477
Chen, V.C.: The Micro-Doppler Effect in Radar, Norwood, MA. Artech House (2019)
Majumder, U., Blasch, E., Garren, D.: Deep Learning for Radar and Communications Automatic Target Recognition, Norwood, MA. Artech House (2020)
Niu, R., Zulch, P., Distasio, M., Blasch, E., Shen, D., Chen, G.: Joint sparsity based heterogeneous data-level fusion for target detection and estimation. In: SPIE Conference on Sensors and Systems for Space Applications X, Anaheim, CA, vol. 10196 (2017). https://doi.org/10.1117/12.2266072
Junkins, J.L., Singla, P.: How nonlinear is it? A tutorial on nonlinearity of orbit and attitude dynamics. J. Astron. Sci. 52(1–2), 7–60 (2004). https://doi.org/10.1007/BF03546420
Kahler, B., Blasch, E.: Sensor management fusion using operating conditions. In: 2008 IEEE National Aerospace and Electronics Conference (NAECON), Dayton, OH, pp. 281–288 (2008). https://doi.org/10.1109/NAECON.2008.4806559
Choi, E.J., et al.: A study on the enhancement of detection performance of space situational awareness radar system. J. Astron. Space Sci. 35(4), 279–286 (2018). https://doi.org/10.5140/JASS.2018.35.4.279
Darema, F., Blasch, E.P., Ravela, S., Aved, A.J. (Eds.), Handbook of Dynamic Data Driven Applications Systems, vol. 2. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-27986-7
Acknowledgments
We thank Dr. Erik Blasch for concept development and paper editing. Partial research support through the Air Force Office of Scientific Research (AFOSR) Grant Number FA9550-20-1-0176 is acknowledged. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force Research Laboratory or the U.S. Government.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Henry, J.K.A., Narayanan, R.M., Singla, P. (2024). Radar Cross-Section Modeling of Space Debris. In: Blasch, E., Darema, F., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2022. Lecture Notes in Computer Science, vol 13984. Springer, Cham. https://doi.org/10.1007/978-3-031-52670-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-52670-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-52669-5
Online ISBN: 978-3-031-52670-1
eBook Packages: Computer ScienceComputer Science (R0)