67a2b342-fc0a-470c-97e6-336bbdb6104120211116080233764naun:naunmdt@crossref.orgMDT DepositInternational Journal of Applied Mathematics and Informatics2074-127810.46300/91014http://www.naun.org/cms.action?id=3064319202131920211510.46300/91014.2021.15https://www.naun.org/cms.action?id=23306Wei-LungMaoDept. of electric engineering National Yunlin University of Science and Technology Yunlin County 64002, TaiwanChorng-SiiHwangDept. of electric engineering National Yunlin University of Science and Technology Yunlin County 64002, TaiwanChung-WenHungDept. of electric engineering National Yunlin University of Science and Technology Yunlin County 64002, TaiwanJyhSheenDept. of Electronic Engineering National Formosa University Yunlin County 632, TaiwanThe global positioning system (GPS) provides accurate positioning and timing information that is useful in various civil and military applications. The adaptive filtering predictor for GPS jamming suppression applications is proposed. This research uses the gLab-G software to substitute for the hardware receiver to record the GPS signal waveform. The normalized least-mean-square (NLMS) and set-membership NLMS (SM-NLMS) filtering methods are employed for continuous wave interference suppression. Simulation results reveal that our proposed methods can provide the better performances when the interference-to-noise ratios (INR) are varied from 20 to 50 dB. The anti-jamming performances are evaluated via extensive simulation by computing mean squared prediction error (MSPE) and signal-to-noise ratio (SNR) improvements.1116202111162021596410https://www.naun.org/main/UPress/ami/2021/a202013-010(2021).pdf10.46300/91014.2021.15.10https://www.naun.org/main/UPress/ami/2021/a202013-010(2021).pdfE. D. Kaplan, editor, “Understanding GPS: Principles and Applications”, Artech House, London 1996. J. B. Y. Tsui, “Fundamentals of global positioning system receivers, a software approach”, John Wiley & Sons, Canada, 2000. 10.1109/78.348134S. Haykin, and L. Li, “Nonlinear adaptive prediction of nonstationary signal”, IEEE Trans. Signal Processing. , 43, pp. 526-535, 1995. 10.1109/78.705435J. Baltersee, and J. A. Chambers, “Nonlinear adaptive prediction of speech with a pipelined recurrent neural network”, IEEE Trans. Signal Processing, 46, pp. 2207-2216, 1998. 10.1109/25.752570P. R. Chang, and J. T. Hu, “Narrow-band interference suppression in spread spectrum CDMA communications using pipelined recurrent neural network”, IEEE Trans. Vehicular Technology, 48, pp. 467-477, 1999. 10.1162/neco.1989.1.2.270R. J. Williams and D. Zipser, “A learning algorithm for continually running fully recurrent neural networks”, Neural Compu, vol.1, pp 270- 280, 1989. 10.1007/978-1-4615-2734-3_9R. Bijjani and P. K. Das, “Neural networks as exciser for spread spectrum communication systems” in Neural networks in telecommunications, Eds. B. Yuhas and N. Ansari, Chapter 9, pp. 173- 189, Kluwer Academic Publisher, 1994. P. S. R. Diniz, “Adaptive Filtering: Algorithms and Practical Implementation,Springer”, New York, NY, 3rd edition, 2008. 10.1109/iswcs.2010.5624323Markus V. S. Lima and Paulo S. R. Diniz, “On the Steady-State MSE Performance of theSet-Membership NLMS Algorithm”, Wireless Communication Systems (ISWCS), 2010 7th International Symposium on, 19-22 sept., pp.389-393, 2010. 10.1109/tvt.2008.926608R. C. de Lamare and P. S. R. Diniz, “Set-membership adaptive algorithmsbased on time-varying error bounds for CDMA interference suppression,”IEEE Trans. Vehicular Technology, vol. 58, no. 2, pp. 644–654, Feb. 2009. 10.1109/tac.1968.1098790F. C. Schweppe, “Recursive state estimate: Unknown but bounded errors and system inputs,” IEEE Trans. on Automatic control, vol13, pp. 22-28, Feb. 1968. 10.1016/0005-1098(82)90110-8E. Fogel and Y.-F. Huang, “On the value of information in system identification-bounded noise case,” Automatica, vol. 18, pp.229-238, March 1982. 10.1109/53.41661J. R. Deller, “Set-Membership identification in digital signal processing,” IEEE Acoust., Speech, Signal Processing Magazine, vol. 6, pp. 4-20, Oct. 1989. A. Antoniou and W.-s. Lu, “Practical Optimization: Algorithms and Engineering Applications”, Springer, New York, NY, 2007. 10.1109/5.736341MICHAEL S. BRAASCH, “GPS Receiver Architectures and Measurements”, Proceedings of the IEEE. vol. 87, pp.48-64, Jan 1999. M. Braasch and F. van Graas, “Guidance accuracy considerations for realtime GPS interferometry,” in Proc. 4th Int. Tech. Meeting Satellite Division of the Institute of Navigation, pp. 373–386, Sept. 1991. P. Nieuwjaar, “GPS signal structure,” NATO AGARD Lecture Series No. 161, The NAVSTAR GPS System, Sept. 1988. 10.1109/its.2006.4433396Rodrigo C. de Lamare, Paulo S. R. Diniz,“Set-Membership Adaptive Algorithms Based on Time-Varying Error Bounds for Interference Suppression”, Information Theory, arXiv:1301.0097v1, 1 Jan 2013.