自回归算子作用不变为信号组合模型的参数估计

本文针对带有ＡＲ噪声的且具有自回归算子作用下保持形式不变的一大类信号，采用分离参数和两步最小二乘的方法直接给出自回归参数估计，并由此得到真实信号参数的估计，该方法克服了以往方法ＡＲ噪声未知的难点，算法为线性求解，简便，实用，运算速度快，求解精度高。     

多项式信号加自回归噪声模型的参数估计

本文提出一种新的多项式信号加自回归ＡＲ噪声的组合模型统计处理方法。方法采用形式处理和分离参数的技术克服了测量数据中平稳相关噪声ＡＲ序列未知的难点，避免了传统差分方法中残差数据非零均值和非ＡＲ属性的缺点，相应算法为线性求解，简便、实用、运算速度快、求解精度高，可同时获得数据真实信号的估值及相应的误差估计。本方法可直接用于飞行器电子测量数据的实时处理。     

非高斯有色噪声中的正弦信号频率估计

本文研究非高斯ＡＲＭＡ有色噪声中的正弦信号频率估计问题。利用自相关函数和三阶累积量相结合，提出了一种先估计噪声模型ＡＲ参数，然后对观测值进行预滤波，最后估计信号模型参数的新方法，模拟实验结果表明，新方法具有良好的频率估计性能。     

非高斯ARMA噪声中谐波恢复的杂交ESPRIT方法

本文研究非高斯ＡＲＭＡ噪声中的谐波恢复问题，提出了一种基于二阶和三阶统计量的杂交ＥＳＰＲＩＴ方法，该方法先估计噪声过程的ＡＲ部分参数，然后对观测值进行预滤波，最后估计谐波信号参量。模拟实验还验证了该方法的有效性和高分辨率。     

非高斯有色噪声中基于四阶累积量噪声建模的谐波恢复方法

本文提出一种在非高斯ＡＲＭＡ噪声中谐波恢复的高阶累积量方法，该方法首先通过Ｈｉｂｌｅｒｔ变换构造复数观测值，然后使用它的一种特殊的四阶累积量建立噪声过程ＡＲ参数，由此对观测值滤波，最后通过ＳＶＤ－ＴＬＳ方法估计谐波信号参数，本文方法克服了以往对非高斯噪声分布的非对称性假设，成功地解决了对称分布非高斯有色噪声中的谐波恢复问题，并且适用于于谐波信号存在二次相应耦合情形，仿真实验验证了文中结论。     

CARMA模型参数的两步估计法

本文对噪声未知的ＣＡＲＭＡ模型，用自适应线性增强器与自适应滤波器并联，作ＣＡＲＭＡ模型的噪声估计器，以最小二乘法估计参数，形成了一种适用于ＣＡＲＭＡ模型的两步估计法。     

白噪声中二维谐波的二维ARMA建模

本文研究了白噪声中二维谐波的二维ＡＲＭＡ建模问题，并从理论上探讨了利用所建立的二维ＡＲＭＡ模型进行二维谐波信号频率估计的方法。     

基于小波回归估计的图像杂波抑制技术研究

非参数回归分析是研究从掺噪观测数据中某种函数（背景杂波）估计的最佳方法。在非参数回归中，往往没有关于要估计函数的任何先验知识。为解决此问题，研究了一种小波回归估计法，并详细描述了非参数回归如何将原始传感器数据变换成“信号加噪声”模型。另外，杂波抑制后，残留噪声的高斯性和独立性通过Kendall秩相关法和计算Friedman统计量的方法进行了验证，其结果表明此技术路线的有效性和可行性。     

基于输出累积量的ARMA（AR0系统的一致性阶次递推辩识

本文提出了一种根据系统输出的观测数据对ＡＲＭＡ（ＡＲ）系统进行盲识别的新算法。该模型由独立同分布非高斯随机序列驱动，其输出序列中含方差未知的加性高斯噪声。通过求解基于三阶累积量谱的代价函数。该算法以模型阶次递推形式同时辩识ＡＲＭＡ的系统阶次和估计出系统能数。     

从GAR模型参数提取特征的数字调制识别新方法

本文提出了一种从观察序列的广义自回归（ＧＡＲ）模型参数提取待识别信号的伪瞬时中心频率和伪瞬时３ｄＢ带宽特征,并利用神经网络分类器的数字调制识别新方法。这种方法充分利用了ＧＡＲ模型良好的抗噪声能力和神经网络优异的模式分类能力,能有效地改善低ＳＮＲ条件下的调制识别性能。计算机模拟结果证实了该方法具有很高的识别率和良好的稳健性。     

Recovering of autoregressive spectral estimates of signals buried in noise

In this paper, we propose a noise modeling that does not destroy AR structure of buried signals in noise independently of its nature (white or colored, Gaussian or not) and its variance. Expression of perturbed AR coefficients is derived and proposed restoration does not use any a-priori information on the nature of noise and its variance. It is shown that AR coefficients are closer to nominal ones (noise-free) in the presence of noise for lower frequency contents with respect to the sampling frequency of corresponding continuous-time processes from which samples are taken for AR estimation. For unknown frequency contents, denoising of AR coefficients is obtained by decreasing the time interval separating samples used by AR estimation. A model order selection adapted to degraded signal-to-noise ratios is proposed. Performances of the proposed recovering of original AR spectra are demonstrated via signals buried in white and colored noise. Observed results are in accordance with the developed theory.     

Blind identification of an autoregressive system using a nonlineardynamical approach

The problem of identifying an autoregressive (AR) system with arbitrary driven noise is considered here. Using an abstract dynamical system to represent both chaotic and stochastic processes in a unified framework, a dynamic-based complexity measure called phase space volume (PSV), which has its origins in chaos theory, can be applied to identify an AR model in chaotic as well as stochastic noise environments. It is shown that the PSV of the output signal of an inverse filter applied to identify an AR model is always larger than the PSV of the input signal of the AR model. Therefore, by minimizing the PSV of the inverse filter output, one can estimate the coefficients and the order of the AR system. A major advantage of this minimum-phase space volume (MPSV) identification technique is that it works like a universal estimator that does not require precise statistical information about the AR input signal. Because the theoretical PSV is so difficult to compute, two approximations of PSV are also considered: the e-PSV and nearest neighbor PSV. Both approximations are shown to approach the ideal PSV asymptotically. The identification performance based on these two approximations are evaluated using Monte Carlo simulations. Both approximations are found to generate relatively good results in identifying an AR system in various noise environments, including chaotic, non-Gaussian, and colored noise     

New autoregressive (AR) order selection criteria based on the prediction error estimation

The most important problem in data modeling using the AR model is the order selection. Some AR order selection criteria estimate the prediction error and choose the order that minimizes this estimated prediction error. All of these criteria use the same formula for estimating the prediction error from the residual variance for all AR models. However, experimental results show that the relationship between the prediction error and the residual variance depends on the AR model. In this paper, we introduce new formulas for estimating the prediction error using the residual variance. These formulas depend on the AR model, and are obtained through assuming a white Gaussian noise as the input noise to the AR model and assuming that the least-squares-forward (LSF) method is used for estimating the AR coefficients. The performance of the new order selection criteria introduced in this paper is compared with other AR order selection criteria using simulated data. Results show that the new criteria have good performance in estimating the prediction error and in selecting an appropriate order for the AR model.     

Causal and semicausal AR image model identification using the EMalgorithm

The method presented by T. Katayama and T. Hirai (1990), who considered the problem of semicausal autoregressive (AR) parameter identification for images degraded by observation noise, is extended. In particular, an approach to identifying both the causal and semicausal AR parameters without a priori knowledge of the observation noise power is proposed. The image is decomposed into 1-D independent complex scalar subsystems resulting from the vector state-space model, using the unitary discrete Fourier transform (DFT). Then the expectation-maximization algorithm is applied to each subsystem to identify the AR parameters of the transformed image. The AR parameters of the original image are then identified using the least-square method. The restored image is obtained as a byproduct of the EM algorithm.     

Inverse filtering based method for estimation of noisy autoregressive signals

In this paper we present a new method for estimating the parameters of an autoregressive (AR) signal from observations corrupted with white noise. The least-squares (LS) estimate of the AR parameters is biased when the observation noise is added to the AR signal. This bias is related to observation noise variance. The proposed method uses inverse filtering technique and Yule-Walker equations for estimating observation noise variance to yield unbiased LS estimate of the AR parameters. The performance of the proposed unbiased algorithm is illustrated by simulation results and they show that the performance of the proposed method is better than the other estimation methods.     

Identification of image and blur parameters in frequency domainusing the EM algorithm

We extend a method presented previously, which considers the problem of the semicausal autoregressive (AR) parameter identification for images degraded by observation noise only. We propose a new approach to identify both the causal and semicausal AR parameters and blur parameters without a priori knowledge of the observation noise power and the PSF of the degradation. We decompose the image into 1-D independent complex scalar subsystems resulting from the vector state-space model by using the unitary discrete Fourier transform (DFT). Then, by applying the expectation-maximization (EM) algorithm to each subsystem, we identify the AR model and blur parameters of the transformed image. The AR parameters of the original image are then identified by using the least squares (LS) method. The restored image is obtained as a byproduct of the EM algorithm.     

Parameter estimation of autoregressive signals in presence of colored AR(1) noise as a quadratic eigenvalue problem

In this paper, we consider the problem of parameter estimation of autoregressive (AR) signals from observations corrupted with colored AR(1) noise. The proposed method is based on Yule-Walker equations. We express these equations as a quadratic eigenvalue problem and then the parameters of the signal and noise are estimated by solving this eigenvalue problem. We also apply the proposed method to the problem of sinusoidal frequency estimation in colored noise. The performance of the proposed algorithm is evaluated by computer simulation examples.     

多变量自回归信号信息融合辨识方法

近年来,为了提高系统模型和状态估计的精度,多传感器数据融合引起了广泛关注。对于带白色公共干扰噪声和有色观测噪声的多传感器多变量自回归（AR）模型,当AR模型参数和噪声方差未知时,提出了一种信息融合多段辨识方法,其中采用多维递推辅助变量（MRIV）方法得到AR模型参数的局部和融合估值器,再用相关方法得到局部和融合噪声方差估值器。这些估值器具有一致性,通过一个信号仿真例子验证了其有效性。     

在脉冲噪声环境中用于快衰信道估计的改进型算法

该文分析了在存在噪声干扰的情况下,进行估计快衰信道的方法。在无线通信系统中,快衰信道可以采用AR(Auto-Regressive)模型进行预测,而LS (Least Square)算法和自适应Kalman滤波器可以分别对AR模型的参数和信道的冲激响应进行估计,但是这两种算法对噪声干扰非常敏感。该文提出改进型的RLM算法和Kalman 滤波器,并在存在噪声的情况下,使用它们并行对AR参数和信道的冲激响应进行联合估计。仿真结果显示:相比于传统的算法,改进后的算法在联合估计信道时,提高了抵抗大脉冲干扰的能力,加快了待估的参数的收敛速度。