基于稳健协方差矩阵的ADS-B压制式干扰抑制
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摘要
压制式干扰是广播式自动相关监视(automatic dependent surveillance-broadcast,ADS-B)系统面临的最常见且最有威胁的干扰之一。提出基于稳健协方差矩阵估计的ADS-B压制式干扰抑制算法。首先,考虑了ADS-B信号的脉冲特性,将协方差矩阵的求解转化成凸优化问题。然后,利用求解凸优化问题得到的稳健协方差矩阵设计最优权矢量。最后,对观测信号进行空域滤波,完成ADS-B的压制式干扰抑制,并对抑制结果进行性能分析。提供的仿真实验结果也验证了算法的有效性,该方法不需要知道ADS-B信号来向,且在非高斯噪声情况下同样有效。
Barrage jamming is the one of most common and serious threats for automatic dependent surveillance-broadcast(ADS-B)system.A barrage jamming suppression algorithm for ADS-B based on estimation of robust covariance matrix is proposed.Firstly,considering the pulse characteristic of ADS-B,the solution of covariance matrix is transformed into a convex optimization problem.Then,the robust covariance matrix obtained by solving the convex optimization is used to design the optimal weight vector.Finally,the barrage jamming is mitigated by spatial filtering and the performance is analyzed.Simulation results demonstrate the effectiveness of the proposed algorithm.The anti-jamming performance of this method is not limited by the prior knowledge of ADS-B direction and also effective in the case of non-Gaussian noise.
Barrage jamming is the one of most common and serious threats for automatic dependent surveillance-broadcast(ADS-B)system.A barrage jamming suppression algorithm for ADS-B based on estimation of robust covariance matrix is proposed.Firstly,considering the pulse characteristic of ADS-B,the solution of covariance matrix is transformed into a convex optimization problem.Then,the robust covariance matrix obtained by solving the convex optimization is used to design the optimal weight vector.Finally,the barrage jamming is mitigated by spatial filtering and the performance is analyzed.Simulation results demonstrate the effectiveness of the proposed algorithm.The anti-jamming performance of this method is not limited by the prior knowledge of ADS-B direction and also effective in the case of non-Gaussian noise.
引文
[1]康南,刘永刚.ADS-B技术在我国的应用和发展[J].中国民用航空,2011(11):36-38KANG N,LIU Y G.Application and development of ADS-Btechnology in China[J].China Civil Aviation,2011(11):36-38.
[2]BAO L N,WU R B,LU D,et al.A novel adaptive anti-interference algorithm based on negative diagonal loading for spoofing and jamming in global navigation satellite system(GNSS)[J].Journal of Communications Technology and Electronics,2016,61(2):157-164.
[3]吴仁彪,王文益.卫星导航自适应抗干扰技术[M].北京:科学出版社,2015:123-146.WU R B,WANG W Y.Adaptive interference mitigation in GNSS[M].Beijing:Science Press,2015:123-146.
[4]LI J,STOICA P.Robust adaptive beamforming[M].New York:Wiley,2005:106-133.
[5]JIANG X,ZENG W J,SO H C,et al.Beamforming via nonconvex linear regression[J].IEEE Trans.on Signal Processing,2016,64(7):1714-1728.
[6]HABLEEL E,BAEK J,BYON Y J,et al.How to protect ADS-B:confidentiality framework for future air traffic communication[C]∥Proc.of the IEEE Conference on Computer Communications Workshops,2015:155-160.
[7]CHEN Y,HUANG L T,YANG X L,et al.Variance analysis for leastp-norm estimator in mixture of generalized Gaussian noise[J].IEICE Trans.on Fundamentals of Electronics,Communications and Computer Sciences,2017,100(5):1226-1230.
[8]LIAO B,GUO C,HUANG L,et al.Robust adaptive beamforming with precise main beam control[J].IEEE Trans.on Aerospace and Electronic Systems,2017,53(1):345-356.
[9]LIAO B,GUO C,HUANG L,et al.Robust adaptive beamforming with random steering vector mismatch[J].Signal Processing,2016,129:190-194.
[10]CHEN Y,SO H C,KURUOGLU E E.Variance analysis of unbiased leastp-norm estimator in non-Gaussian noise[J].Signal Processing,2016,122:190-203.
[11]JIANG X,ZENG W J,YASOTHARAN A,et al.Minimum dispersion beamforming for non-Gaussian signals[J].IEEETrans.on Signal Processing,2014,62(7):1879-1893.
[12]VOROBYOV S A.Principles of minimum variance robust adaptive beamforming design[J].Signal Processing,2013,93(12):3264-3277.
[13]SUN Y,BABU P,PALOMAR D P,et al.Regularized robust estimation of mean and covariance matrix under heavy-tailed distributions[J].IEEE Trans.on Signal Processing,2015,63(12):3096-3109.
[14]SOLOVEYCHIK I,WIESEL A.Performance analysis of Tyler’s covariance estimator[J].IEEE Trans.on Signal Processing,2015,63(2):418-426.
[15]TYLER D E.A distribution-free M-estimator of multivariate scatter[J].The Annals of St-atistics,1987,15(1):234-251.
[16]SUN Y,BABU P,PALOMAR D P,et al.Robust estimation of structured covariance matrix for heavy-tailed elliptical distributions[J].IEEE Trans.on Signal Processing,2016,64(14):3576-3590.
[17]MORAALES-JIMENEZ D,COUILLET R,MCKAY M R,et al.Large dimensional analysis of robust M-estimators of covariance with outliers[J].IEEE Trans.on Signal Processing,2015,63(21):5784-5797.
[18]TSAKALIDES P,NIKIAS C L.Robust adaptive beamforming in alpha-stable noise environments[C]∥Proc.of the IEEEInternational Conference on Acoustics,Speech and Signal Processing,1996:2884-2887.
[2]BAO L N,WU R B,LU D,et al.A novel adaptive anti-interference algorithm based on negative diagonal loading for spoofing and jamming in global navigation satellite system(GNSS)[J].Journal of Communications Technology and Electronics,2016,61(2):157-164.
[3]吴仁彪,王文益.卫星导航自适应抗干扰技术[M].北京:科学出版社,2015:123-146.WU R B,WANG W Y.Adaptive interference mitigation in GNSS[M].Beijing:Science Press,2015:123-146.
[4]LI J,STOICA P.Robust adaptive beamforming[M].New York:Wiley,2005:106-133.
[5]JIANG X,ZENG W J,SO H C,et al.Beamforming via nonconvex linear regression[J].IEEE Trans.on Signal Processing,2016,64(7):1714-1728.
[6]HABLEEL E,BAEK J,BYON Y J,et al.How to protect ADS-B:confidentiality framework for future air traffic communication[C]∥Proc.of the IEEE Conference on Computer Communications Workshops,2015:155-160.
[7]CHEN Y,HUANG L T,YANG X L,et al.Variance analysis for leastp-norm estimator in mixture of generalized Gaussian noise[J].IEICE Trans.on Fundamentals of Electronics,Communications and Computer Sciences,2017,100(5):1226-1230.
[8]LIAO B,GUO C,HUANG L,et al.Robust adaptive beamforming with precise main beam control[J].IEEE Trans.on Aerospace and Electronic Systems,2017,53(1):345-356.
[9]LIAO B,GUO C,HUANG L,et al.Robust adaptive beamforming with random steering vector mismatch[J].Signal Processing,2016,129:190-194.
[10]CHEN Y,SO H C,KURUOGLU E E.Variance analysis of unbiased leastp-norm estimator in non-Gaussian noise[J].Signal Processing,2016,122:190-203.
[11]JIANG X,ZENG W J,YASOTHARAN A,et al.Minimum dispersion beamforming for non-Gaussian signals[J].IEEETrans.on Signal Processing,2014,62(7):1879-1893.
[12]VOROBYOV S A.Principles of minimum variance robust adaptive beamforming design[J].Signal Processing,2013,93(12):3264-3277.
[13]SUN Y,BABU P,PALOMAR D P,et al.Regularized robust estimation of mean and covariance matrix under heavy-tailed distributions[J].IEEE Trans.on Signal Processing,2015,63(12):3096-3109.
[14]SOLOVEYCHIK I,WIESEL A.Performance analysis of Tyler’s covariance estimator[J].IEEE Trans.on Signal Processing,2015,63(2):418-426.
[15]TYLER D E.A distribution-free M-estimator of multivariate scatter[J].The Annals of St-atistics,1987,15(1):234-251.
[16]SUN Y,BABU P,PALOMAR D P,et al.Robust estimation of structured covariance matrix for heavy-tailed elliptical distributions[J].IEEE Trans.on Signal Processing,2016,64(14):3576-3590.
[17]MORAALES-JIMENEZ D,COUILLET R,MCKAY M R,et al.Large dimensional analysis of robust M-estimators of covariance with outliers[J].IEEE Trans.on Signal Processing,2015,63(21):5784-5797.
[18]TSAKALIDES P,NIKIAS C L.Robust adaptive beamforming in alpha-stable noise environments[C]∥Proc.of the IEEEInternational Conference on Acoustics,Speech and Signal Processing,1996:2884-2887.