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A blind source separation algorithm of non-stationary signals based on local polynomial Fourier transform

机译:基于局部多项式傅里叶变换的非平稳信号盲源分离算法

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The separation of non-stationary signals is a hot topic. The blind source separation algorithms based on time-frequency analysis can not only describe the time-domain feature and the frequency-domain feature of signals but also extract the time-varying feature of non-stationary signals. Blind source separation owns better performance in solving the problem of non-stationary signals separation. In traditional methods based on linear time-frequency analysis, the time-frequency resolution is not high. Meanwhile, in traditional methods based on bilinear time-frequency, there is the problem of the cross-term interference. According to these problems, this paper proposes a blind source separation algorithm to separate the source signals based on local polynomial Fourier transform that adjusts the time-frequency resolution and the cross-term interference. First, the observed signals that are received by the senors are processed through whitening. Then, the whitened signals are transformed to get time-frequency distribution matrices through local polynomial Fourier transform. What's more, a strategy of choosing time-frequency distribution matrices is proposed to detect the matrices that own the underlying diagonal or off-diagonal structure and remove other matrices. The thresholds of traditional strategies in other papers are not needed in this paper. Therefore, the strategy of this paper can avoid the blindness of choosing the threshold. Finally, a joint diagonalization of a combined set of time-frequency distribution matrices is done to estimate the mixing matrix and the source signals. The simulation results indicate that the proposed algorithm of this paper can extract the non-stationary source signals effectively and own better performance than other comparison algorithms.
机译:非平稳信号的分离是一个热门话题。基于时频分析的盲源分离算法不仅可以描述信号的时域特征和频域特征,还可以提取非平稳信号的时变特征。盲源分离在解决非平稳信号分离问题上具有更好的性能。在基于线性时频分析的传统方法中,时频分辨率不高。同时,在基于双线性时频的传统方法中,存在交叉项干扰的问题。针对这些问题,本文提出了一种基于局部多项式傅立叶变换的盲源分离算法,该算法可以调整时频分辨率和交叉项干扰。首先,通过白化处理传感器接收到的观测信号。然后,通过局部多项式傅立叶变换对白化后的信号进行变换,得到时频分布矩阵。此外,提出了一种选择时频分布矩阵的策略,以检测拥有底层对角线或非对角线结构的矩阵,并删除其他矩阵。本文不需要传统策略的门槛。因此,本文的策略可以避免选择阈值的盲目性。最后,对一组时频分布矩阵组合进行联合对角化,以估计混合矩阵和源信号。仿真结果表明,与其他比较算法相比,本文提出的算法能够有效地提取非平稳源信号,并具有更好的性能。

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