基于高阶累积量符号相干累积自适应滤波算法

基于传统LMS（Least Mean Square）的自适应谱线增强（Adaptive Line Enhancement，ALE）算法的主要缺点是：抑制高斯噪声效果差，计算量大，收敛速度慢，为了尽可能的克服这些缺点，利用相干累积算法对输入数据中相干分量的相干累积作用和符号算法能减少计算量的性能，修正了传统的LMS算法，提出了基于高阶累积量符合相干累积迭代的自适应谱线增强新算法，该算法具有良好的抑制高斯有色噪声效果。计算量小，输出信号平稳等特点，能较好地克服基于LMS的ALE算法的缺点。仿真结果证实了该算法的有效性和可行性。因此，本文的研究具有良好的实用性和应用前景。

An Adaptive Filtering Algorithm of Higher-Order Cumulant-Based Signed Coherent Integration

Traditional LMS (Least Mean Square) based ALE (Adaptive Line Enhancement) algorithm has three disadvantages: ability to hand Gaussian colored noise is bad, computational complexity is high, and noise variance of the output is great. For greatly reducing these three disadvantages, firstly, we used the integrated function of the coherent components of the input and the low computational load of the signed algorithm to modify the traditional LMS algorithm. This modified LMS algorithm is regarded as the signed coherent integration(SCI) algorithm. Secondly, we developed HOCSCI (higher-order cumulant signed coherent integration) algorithm for adaptive spectrum enhancement. The performance of the new algorithm is better than that of higher-order cumulant iteration (HOCI) algorithm. Compared with the HOCI and SCI algorithm, the new algorithm has the following features: (1) more signed coherent integrated terms are introduced into the cumulant updating equation. Thus it is easier to guarantee the adaptive integration action, and then very weak non-linear frequency modulation signals can be enhanced; (2) the computational load of the HOCSCI algorithm is smaller than that of the HOCI algorithm; (3) the stability of the output signals of the HOCSCI algorithm is better than that of the HOCI or SCI algorithm. Simulation results validate these conclusions.