基于聚类分析的分数阶Fourier变换信号分离与检测

Separation and Detection of Signals by Fractional Fourier Transform Based on Cluster Analysis

针对应用分数阶Fourier变换检测多分量信号计算量大、效率低的问题,提出在分数阶Fourier域采取混沌-多步拟牛顿法和聚类分析相结合的方法分离与检测信号。在保证全局快速搜索最强信号的条件下,实现一次检测多个较强信号,并将已检信号逐次消去,减小对剩余信号检测的影响。仿真结果表明,该方法能快速分离检测多种调制的频谱混叠信号。

For the problem of intensive calculation and low efficiency in application of fractional Fourier transform （FRFT） for the detection of multi - component signal, this paper proposes to take chaos - multi - step - quasi - Newton method and cluster analysis in the fractional Fourier domain separation and detection of signals. In glob- al condition of quick searching for the strongest signal, detection of multiple strong signals is realized, and the signals seized are cancelled successively to reduce the impact on the remaining signal detection. The simulation results show that this method can separate and detect a variety of modulation signal with spectral aliasing.