传统的低阶自适应滤波器难以抑制或消除激光位移信号的高阶统计噪声，且在微位移测量条件下，基于最大熵或自然梯度的盲源分离方法的收敛速度和信噪比会急剧降低．为此，文中利用混合信号的峭度变化特性，提出了最大三阶相关峭度反卷积算法，设计了反卷积逆滤波器，分析了最大三阶相关峭度反卷积算法的收敛性和稳定条件，并构建了实验平台进行盲提取实验．结果表明，最大三阶相关峭度反卷积算法可有效地盲提取激光位移信号和多重反射信号，较 FastICA 算法具有更快的收敛速度和更高的信噪比．

The traditional low- order adaptive filter is inefficient in suppressing or eliminating the higher- order statis-tical noises of laser displacement signals,and the convergence speed as well as the SNR (Signal- to- Noise Ratio) ofblind source separation based on the maximum entropy or the natural gradient may remarkably decrease under mi-cro- displacement measurement conditions.In order to solve these problems,a maximum 3- shifted correlated kurto-sis deconvolution (M3CKD) algorithm is presented on the basis of the kurtosis variation of mixed signals,and thecorresponding inverse filter is designed.Moreover,the convergence and stability conditions of the algorithm areanalyzed,and a blind extraction is carried out on a constructed experiment platform.The results indicate that theproposed algorithm is efficient in the blind extraction of laser displacement signals and multiple reflection signals,and that it outperforms the FastICA algorithm due to its high convergence speed and large signal- to- noise ratio.