基于DAC统计模型的卫星导航欺骗干扰检测
Detection of Spoofing of Satellite Navigation Receivers Based on Statistical Model of DAC
DOI: 10.12677/HJWC.2014.45012, PDF,    国家自然科学基金支持
作者: 张 茴, 孙闽红, 王海泉, 沈 雷, 甘一鸣：杭州电子科技大学通信工程学院，杭州；邱 雨：杭州谱恒科技有限公司，杭州
关键词: 干扰识别；欺骗干扰；DAC；统计建模；Jamming Identification； Spoofing Detection； DAC； Statistics Modeling
摘要: 针对欺骗干扰信号常在时域、频域和空域与真实信号重叠，导致识别欺骗干扰难度较大的问题，本文假定真实发射机与欺骗干扰机射频前端器件除数模转换器(DAC)外均工作于理想状态，提出了一种基于DAC建模的欺骗干扰识别方法。首先，利用积分非线性(INL)和差分非线性(DNL)对DAC的非线性进行统计建模，提取特征向量；其次，基于似然比检测和欧氏距离法实现欺骗干扰识别；最后，通过仿真实验验证了方法的有效性。
Abstract: Since real signals and spoofing signals are overlapped in time domain, frequency domain and time-frequency domain, it is not easy to distinguish them. In this paper, a new identification method based on the model of digital-to-analog converter (DAC) is proposed on a condition that the devices of transmitter and jammer are in desired state except the DAC. Firstly, the DAC is modeled with the integral non-linear (INL) and differential non-linear (DNL) to extract the parameter vector; secondly, the spoofing signals are identified by using a LRT detector and a naïve method; lastly, the effectiveness of the proposed method is verified via simulation.
文章引用：张茴, 孙闽红, 王海泉, 沈雷, 邱雨, 甘一鸣. 基于DAC统计模型的卫星导航欺骗干扰检测[J]. 无线通信, 2014, 4(5): 73-82. https://dx.doi.org/10.12677/HJWC.2014.45012

参考文献

[1] Olivier, K., Cilliers, J.E. and Du Plessis, M. (2011) Design and performance of wideband DRFM for radar test and evaluation. Electronics Letters, 47, 824-825
[2] Molchanov, P.O., Astola, J.T., Egiazarian, K.O., et al. (2011) Target classification by using pattern features extracted from bispectrum-based radar doppler signatures., 2011 Proceedings of International IEEE on Radar Symposium (IRS), 791-796.
[3] Chen, V.C., Miceli, W.J. and Himed, B. (2009) Mi-cro-doppler analysis in ISAR—Review and perspectives. International IEEE Radar Conference, Surveillance for a Safer World, 1-6.
[4] Psiaki, M.L., O’Hanlon, B.W., Bhatti, J.A., et al. (2011) Civilian GPS spoofing detection based on dual-receiver correlation of military signals. Proceedings of the Institute of Navigation GNSS (ION GNSS 2011), 2011.
[5] Nielsen, J., Broumandan, A. and Lachapelle, G. (2012) GNSS spoofing detection for single antenna hand-held receivers. Navigation, 58, 335-344.
[6] Langley, L.E. (2006) Specific emitter identification and classical parameter fusion technology. Litton Applied Technology, San Jose, 377-381.
[7] Li, L., Ji, H.B. and Jiang, L. (2011) Quadratic time-frequency analysis and sequential recognition for specific emitter identification. IET Signal Processing, 5, 568-574.
[8] IEEE (1984) IEEE Standard 746-1984. In Performance Measurements of A/D and D/A Converters for PCM Television Video Circuits. IEEE, New York.
[9] Billingsley, P. (1999) Convergence of Probability Measures. Wiley, New York, 83-85.
[10] Radliov, G.I., Heydenreich, M., van der Hofstad, R.W. and Hegt, J.A. (2007) Brownian-bridge-based statistical analysis of the dacinl caused by current mismatch. IEEE Transaction on Circuits and Systems, 54, 146-150.
[11] Polak, A., Dolatshahi, S. and Goeckel, D. (2011) Identifying wireless users via transmitter imperfections. Selected Areasin Communications, 29, 1469-1479
[12] Greco, M., Gini, F. and Farina, A. (2008) Radar detection and classification of jamming signals belonging to a cone class. IEEE Transaction on Signal Processing, 56, 1984-1993