Abstract
GPS Block IIR-M and Block IIF satellites have the capability of flex power, which can redistribute the transmitting power of different signal components. Flex power can effectively improve the anti-jamming performance of GPS signals and is part of the GPS modernization plans. Since 2020, two kinds of regional GPS flex power have been observed, one started on February 14, 2020, and ended on September 13, 2020, while the other one has been on-state since October 1, 2020. Both of these two kinds of flex power improved the power of L2 P(Y) signal. We establish a real-time detection system for flex power based on the random forest algorithm in machine learning, combined with polynomial fitting. Moreover, we establish a special voting detection system and use the constant false alarm detection technology (CFAR) to find the detection threshold of each satellite and determine whether the satellite has flex power. To verify the performance of system, three kinds of data are used for testing. Judging from the results of these three-mode data samples, the false alarm rate of the system is around \(10^{ - 5}\), and the probability of missed alarm maintains around \(10^{ - 3}\), which can effectively prove the detection performance of the GPS flex power.
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The data used to support the findings of this study are available from the corresponding author upon request.
References
Barbour B (2011) Global positioning system status. 2D SPACE OPERATIONS SQUADRON SCHRIEVER AFB CO. https://www.gps.gov/cgsic/international/2009/stockholm/barbour.pdf. Accessed 26 Oct 2009
Breunig MM, Kriegel HP, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data. https://doi.org/10.1145/342009.335388
Defense Science Board (2005) The future of the global positioning system. Tech. rep., Office of the Under Secretary of Defense for Acquisition, Technology, and Logistics. https://www.acq.osd.mil/dsb/reports/2000s/ADA443573.pdf
Esenbuğa ÖG, Hauschild A (2020) Impact of flex power on GPS block IIF differential code biases. GPS Solut 24:91. https://doi.org/10.1007/s10291-020-00996-x
Falletti E, Pini M, Presti LL (2011) Low complexity carrier-to-noise ratio estimators for GNSS digital receivers. IEEE Trans Aerosp Electron Syst 47(1):420–437. https://doi.org/10.1109/TAES.2011.5705684
Fisher SC, Ghasemi K (1999) GPS IIF—the next generation. Proc IEEE 87(1):24–47. https://doi.org/10.1109/5.736340
Xie Gang (2009) Principles of GPS and receiver design. Publishing House of Electronics Industry
InsideGNSS Team (2010) IS-GPS-200E: flexing power and fixing phrase ambiguities. InsideGNSS.http://insidegnss.com/is-gps-200e-flexing-power-and-fixing-phrase-ambiguities. Accessed 10 Aug 2018
IS-GPS-200J. (2018) Interface specification IS-GPS-200: Navstar GPS space segment/ navigation user segment interfaces. https://www.gps.gov/technical/icwg/IS-GPS-200J.pdf
Jimenez-Banos D, Perello-Gisbert J, Crisci M (2011) The measured effects of GPS flex power capability collected on sensor station data. In: 2010 5th ESA workshop on satellite navigation technologies and European workshop on GNSS signals and signal processing (NAVITEC). https://doi.org/10.1109/NAVITEC.2010.5708073
Keller JM, Gray MR, Givens JA (1985) A fuzzy K-nearest neighbor algorithm. IEEE Trans Syst, Man, Cybern 15(4):580–585. https://doi.org/10.1109/TSMC.1985.6313426
Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2(3):18–22
Ma J, Perkins S (2003) Time-series novelty detection using one-class support vector machines. Proc Int Jt Conf Neural Netw 3:1741–1745. https://doi.org/10.1109/IJCNN.2003.1223670
Harold W.Martin III (2018) Policy update. https://www.gps.gov/multimedia/presentations/2018/03/munich/martin.pdf. Accessed 6 March 2018
Moore B (1981) Principal component analysis in linear systems: controllability, observability, and model reduction. IEEE Trans Autom Control 26(1):17–32. https://doi.org/10.1109/TAC.1981.1102568
Rajan JA, Irvine J (2005) GPS IIR-M and IIF: payload modernization. In: Proc. ION NTM 2005, San Diego, California, USA, January 24–26, pp 508–514
Rish I (2001) An empirical study of the naive bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, vol 3, no 22, pp 41–46
Rousseeuw PJ, Driessen KV (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics 41(3):212–223. https://doi.org/10.1080/00401706.1999.10485670
Steigenberger P, Thölert S, Montenbruck O (2019) Flex power on GPS Block IIR-M and IIF. GPS Solut 23:8. https://doi.org/10.1007/s10291-018-0797-8
Woo KT (1999) Optimum semi-codeless carrier phase tracking of L2. In: Proc. ION GPS 1999, Proceedings of the 12th international technical meeting of the satellite division of the institute of navigation, Nashville, Tennessee, USA, pp 289–306. https://doi.org/10.1002/j.2161-4296.2000.tb00204.x
Acknowledgements
The work of this paper was supported by the National Natural Science Foundation of China (Grant No.U20A0193 and No.62003354).
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Yang, X., Liu, W., Huang, J. et al. Real-time monitoring of GPS flex power based on machine learning. GPS Solut 26, 73 (2022). https://doi.org/10.1007/s10291-022-01257-9
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DOI: https://doi.org/10.1007/s10291-022-01257-9