中文摘要：

分析了非参数化谱估计方法，分析表明，它们所解决的优化问题都是加权最小二乘（weightedleastsquare，WLS），不同在于如何估计广义噪声协方差矩阵来构建加权矩阵。基于统一框架，提出了一种能同时估计信号频谱和观测噪声的自适应迭代非参数谱估计方法。该方法在每一次迭代时都利用上一次估计结果来逐步逼近真实的广义噪声协方差矩阵。分析和仿真表明，本文方法具有分辨率高，谱泄漏抑制好，并能增强信号协方差矩阵的可逆性和频谱范围选择的随意性等特点。

英文摘要：

Nonparametric spectral estimation methods are analyzed. It shows they are all weighted least square（WLS） estimation and the only difference is how to build a weighted matrix from generalized noise covari ance. Based on the same idea, an adaptive iterative nonparametric spectral estimation method is proposed. It can simultaneously estimate the signal spectrum and measurement noise. During each iteration process, the generalized noise covariance matrix is estimated from the last time estimative result. And it can approach the real noise co variance matrix iteratively. Analyses and numerical simulations validate that the proposed method has the char acteristics of high resolution with low spectrum leakage, the enhanced invertibility of signal covariance matrixes and a free choice of spectrum range.