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基于改进型最大信噪比算法的抗压制性干扰研究 被引量:2

Research into Anti-blanket Jamming Technology Based on Improved Maximum SNR Algorithm
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摘要 自雷达诞生以来,抗干扰能力一直是评价一部雷达性能的关键性指标,特别是随着战场电磁环境的日益复杂,雷达抗干扰问题变得尤其重要。针对抗压制性干扰,改进了传统意义上基于最大信噪比的盲源分离算法,提出了使用干扰重构算法取代滑动平均处理的方法,获得了比传统方法分离出质量更高的雷达信号,输出信号与源信号相关性更强。 Since there are radars, anti-jamming capability is always the key index to evaluate a raciar, especially with the increasing complication of battlefield electromagnetic environment,anti-jamming problem of radar becomes particularly important. For anti-blanket jamming,this paper improves the blind source separation algorithm based on traditional maximum signal to noise ratio, presents the method that uses jamming remodelment algorithm to replace the slide average treatment method, then gets the radar signal of better quality than by traditional method,and the correlation between the output signal and the source signal is stronger.
作者 王雨
出处 《舰船电子对抗》 2016年第1期31-35,共5页 Shipboard Electronic Countermeasure
关键词 抗压制性干扰 盲源分离 最大信噪比 干扰重构 anti-blanket iamming blind source separation maximum signal to noise ratio jamming remodelment
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参考文献7

  • 1HYVARINEN A.独立成分分析[M].周宗潭,董国华,徐昕,等译.北京:电子工业出版社,2014:142-178.
  • 2COMON P.Separation of stochastic process[C]//Workshop on Highorder Spectrum Analysis.Colorado,1989:174-179.
  • 3冯大政,保铮,张贤达.信号盲分离问题多阶段分解算法[J].自然科学进展,2002,12(3):324-328. 被引量:11
  • 4HERAULT J,JUTTEN C.Space or time adaptive signal processing by neural network model[C]//American Insititute for Physics:Neural Networks for Computing AIP conf Proceeding 151.New York:American Insititute for Physics,1986:13-16.
  • 5STONE J V.Blind source separation using temporal predictability[J].Neural Computation,2001(7):150-165.
  • 6张小兵,马建仓,陈翠华,刘恒.基于最大信噪比的盲源分离算法[J].计算机仿真,2006,23(10):72-75. 被引量:27
  • 7BORGA M.Learning Multidimensional Signal Processing[D].Linkoping,Sweden:Linkoping University,1998.

二级参考文献17

  • 1[1]Tong L,et al.Waveform-preserving blind estimation of multiple in-dependent sources.IEEE Trans Signal Processing,1993,41(7):2461
  • 2[2]Chang C,et al.A matrix-pencil approach to blind separation of col-ored nonstationary signals.IEEE Trans Signal Processing,2000,48(3):900
  • 3[3]Gonen E,et al.Applications of cumulants to array processing Part III:Blind beamforming for coherent signal.IEEE Trans Signal Pro-cessing,1997,45(9):2252
  • 4[4]Ray T,et al.ESPRIT-estimation of signal parameters via rotational invariance techniques.Opt Eng,1990,29(4):296
  • 5[5]Belouchrani A,et al.A blind source separation technique using sec-ond-order statistics.IEEE Trans Signal Processing,1997,45(2):434
  • 6[6]Cardoso JF,et al.Blind beamforming for non-Gaussian signals.IEE Proceedings-F,1993,140(6):362
  • 7[7]Wax M,et al.A least squares approach to blind beamforming.IEEE Trans Signal Processing,1999,47(1):231
  • 8[8]Goldstein J S,et al.A multistage representation of the Wiener filter based on orthogonal projections.IEEE Trans,Information Theory,1998,44(7):2943
  • 9[9]Tong L,et al.Indeterminacy and identifiability of blind identifica-tion.IEEE Trans Circuits and Systems,1991,38(5):499
  • 10A J Bell and T J Sejnowski. An information approach to blind separation and blind deconvolution[J]. Neural Computation,1995,7(6): 1129 - 1159.

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