基于循环累量不变量的MPSK信号调制识别算法
Algorithm for modultion classification of mpsk signals based on cyclic cumulant invariants
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摘要: 提出一种新的循环累量不变量分类特征,来实现MPSK信号的调制样式分类。新分类特征只利用了码元速率的先验信息,对基带成型脉冲的形状具有稳健性;对MPSK基带信号的时移、载波相位误差、信号幅度变化具有不变性;并可抑制加性平稳噪声。利用循环时变累量的多信号选择性,所提出的分类特征可实现多信号分类识别,理论分析和计算机仿真结果都证实了分类算法的有效性。
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关键词:
- 调制分类; 高阶统计量; 循环时变累量
Abstract: A new invariants classification feature based on cyclic temporary cumulant is proposed for classification of MPSK signals. The new feature only uses the symbol rate information, which is resistant to shaping pulse and invariant with respect to the time-translation, local carrier phase offset and amplitude scale of the baseband MPSK signals, and can suppress stationary additive noise. The signal selectivity of cyclic temporary cumulants makes it possible for the proposed feature to be applied to modulation classification of multi-signals. Theoretial analysis and extensive computer simulations show the efficiency of the proposed classification algorithm. -
Huang Chung-Yu, A. Polydoros, Likelihood methods for MPSK modulation classification, IEEE Trans. on Communications, 1995, COM-43(2/3/4), 1493-1504.[2]A. Swami, B. M. Sadler, Hierarchical digital modulation using cumulants, IEEE Trans. on Communications, 2000, COM-48(3), 416-429.[3]W.A. Gardner.[J].C. M. Spooner, Cyclic spectral analysis for signal detection and modulation recognition, MILCOM88, San Diego, CA.1988,:-[4]Y.K. Kim, C. L. Weber, Generalized single classifier with applications to SQPSK v.s. 2k PSK,MILCOM89, Boston, MA, 1989, Vol.3,754-759.[5]J. Reichert, Automatic classification of communication signals using higher order statistics,ICASSP92, San Francisco, 1992, Vol.5, 221-224.[6]J.G. Proakis, Digital Communication, Third Ed., New York, McGraw- Hill Book Co., 1995,chapter 4.[7]W.A. Gardner, C. M. Spooner, The cumulant theory of cyclostationary time-series, Part Ⅰ:Foundation, IEEE Trans. on Signal Processing, 1994, SP-42(12), 3387-3408.[8]C.M. Spooner, W. A. Gardner, The cumulant theory of cyclostationary time-series, Part Ⅱ:Development and Applications, IEEE Trans. on Signal Processing, 1994, SP-42(12), 3409-3429.[9]C.L. Nikias, Higher-Order Spectra Analysis--A Nonlinear Signal Processing Framework, Englewood Cliffs, New Jersey, PTR Prentice Hall, 1993, chapter 2. ［10］沈清,汤霖,模式识别导论,长沙,国防科技大学出版社,1991,25-35. 期刊类型引用(5)
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