A new algorithm for high-resolution two-dimensional spectral estimation

In this letter, we present a new method for two-dimensional spectral estimation. This method has a computational requirement similar to that of the Maximum-Likelihood Method (MLM), but has a resolution property which is considerably better than that of the MLM.     

New results in spectral estimation of decimated processes

The aim of this letter is to derive the spectral density of decimated processes, and to establish their model when they are generated from ARMA processes. The optimal conditions of decimation are also discussed.<>     

A new approach to spectral estimation: a tunable high-resolutionspectral estimator

Traditional maximum entropy spectral estimation determines a power spectrum from covariance estimates. Here, we present a new approach to spectral estimation, which is based on the use of filter banks as a means of obtaining spectral interpolation data. Such data replaces standard covariance estimates. A computational procedure for obtaining suitable pole-zero (ARMA) models from such data is presented. The choice of the zeros (MA-part) of the model is completely arbitrary. By suitable choices of filter bank poles and spectral zeros, the estimator can be tuned to exhibit high resolution in targeted regions of the spectrum     

A consistent nonparametric spectral estimator for randomly sampled signals

In this paper, the nonparametric spectral analysis of a randomly sampled signal is discussed. For this purpose, the general form of Masry's recursive spectral estimators is considered and improved. The corresponding algorithms require the knowledge of the sampling times, the samples, and the sampling law and that the high-frequency decay of the spectrum be at least roughly known. The minimization of the asymptotic equivalent of the mean square estimation error leads to an estimator that is not only consistent but also asymptotically optimal. Theoretical and simulation results regarding this new estimator are given and compared with the standard methods. Besides, the developed theoretical framework strictly includes the case where the sampling time process is Poisson and the signal is Gaussian.     

A two-dimensional Prony's method for spectral estimation

The problem of estimating the parameters of a model for bidimensional data made up by a linear combination of damped two-dimensional sinusoids is considered. Frequencies, amplitudes, phases, and damping factors are estimated by applying a generalization of the monodimensional Prony's method to spatial data. This procedure finds the desired quantities after an autoregressive model fitting to the data, a polynomial rooting, and a least-squares problem solution. The autoregressive models involved have a particular nature that simplifies the analysis. In fact, their characteristic polynomial factors into two parts so that many of their properties can be easily determined. Quick estimates of the parameters computed are found by using standard one-dimensional autoregressive estimation methods. An iterative procedure for refining the autoregressive parameters estimates which gives rise to better frequency estimates is also discussed. Some simulation results are reported     

A spectral matching approach for parameter and spectral estimationof nonstationary rational processes

The parameter and spectral estimation problems of nonstationary signals are considered. The nonstationary signals are modeled as rational processes with time-varying parameters. The spectral matching approach, which was introduced by Friedlander and Porat (1984), is generalized to the nonstationary case and two new estimators, namely, the time-varying spectral matching estimator (TVSME) and the time-frequency spectral matching estimator (TFSME) are proposed. The proposed methods estimate the parameters of the time-varying rational model by fitting the parametric spectrum expression to an estimated time-frequency distribution of the signal. An approximate statistical analysis is given for both methods along with computer simulation results, illustrating the performance of the proposed estimators     

A SPECTRAL ESTIMATION ALGORITHM USING THE HOUSEHOLDER TRANSFORM

Householder transform is used to triangularize the data matrix, which is basedon the near prediction error equation. It is proved that the sum of squared residuals for eachAR order can be obtained by the main diagonal elements of upper triangular matrix, so thecolumn by column procedure can be used to develop a recursive algorithm for AR modeling andspectral estimation. In most cases, the present algorithm yields the same results as the covariancemethod or modified covariance method does. But in some special cases where the numerical ill-conditioned problems are so serious that the covariance method and modified covariance methodfail to estimate AR spectrum, the presented algorithm still tends to keep good performance. Thetypical computational results are presented finally.     

Forward covariance least-squares algorithm: a new method in AR spectral estimation

A new method for estimating autoregressive (AR) process coefficients for spectral estimation is introduced by minimising the average power of the forward covariance prediction error. AR spectra generated by this method are shown to have improved performance over AR spectra generated by the best alternative forward prediction error method, the Yule-Walker estimator.     

A fast spectral estimation algorithm based on the FFT

A simple FFT-based algorithm for spectrum estimation is presented. The major difference between this and spectrum estimation using a single pass through the FFT is that the proposed algorithm is iterative and the FFT is used many times in a systematic may to search for individual spectral lines. Using simulated data, the proposed algorithm is able to detect mulitple sinusoids in additive noise. The algorithm is certainly better than the single pass FFT in separating closely spaced sinusoids. Finally the algorithm is applied to some experimental measurements to illustrate its properties     

A recursive maximum likelihood algorithm for ARMA spectral estimation

A spectral estimation technique is presented for autoregressive moving-average (ARMA) processes. The technique is based on a parameter estimation technique known as the rec ursive maximum likelihood (RML) method. The recursive spectral estimation algorithm is presented and its asymptotic properties are discussed. Simulation results are presented to illustrate the performance of the estimator for various types of data.     

Parameter estimation for Middleton Class A interference processes

The problem of estimating the parameters of the Middleton Class A interference model is considered. On the assumption of the availability of a set of independent samples from Class A envelope distribution, the asymptotic performances of several estimation procedures are explored. From this analysis, estimates based on the method of moments are seen to be consistent and computationally desirable but highly inefficient, whereas more efficient likelihood-based estimators are seen to be computationally unwieldy. However, an estimator that initiates likelihood iteration with the method-of-moments estimates is seen to overcome these difficulties in its asymptotic performance. Unfortunately, simulation of this third estimator for practical sample sizes reveals poor performance under these conditions. To overcome this lack of small-sample efficiency, a similar estimator that initiates likelihood iteration with physically motivated (but nonoptimal) estimates is also proposed. Simulation of this latter estimator for practical sample sizes indicates that near-optimal performance is attained by this technique     

一种新的大载波频偏估计算法

基于卫星通信快速、准确捕获载波频率的需求,提出了一种根据反余弦函数的大载波频偏估计算法。通过详细的理论推导和仿真验证,表明了该算法抗噪声能力强、频偏估计范围大、估计精度高以及易于实现的良好性能。     

基于几何估计的光谱解混方法

光谱解混是高光谱数据分析的重要技术之一.全约束(即非负性约束和归一化约束)最小二乘线性光谱混合模型(FCLS-LSMM)具有模型简单和物理意义明确等优点而得以广泛使用.然而,FCLS-LSMM的传统优化求解方法的迭代过程非常复杂.近年提出的几何方法为降低LSMM的求解复杂度提供了新思路,但是所获得的结果并非真正意义上的全约束最小二乘解.为此,建立了一种完全符合FCLS要求的LSMM几何求解方法,具有复杂度低和可以获得理论最优解等优点.实验表明了所提出方法的有效性.     

基于几何估计的光谱解混方法

光谱解混是高光谱数据分析的重要技术之一。全约束(即非负性约束和归一化约束)最小二乘线性光谱混合模型(FCLS-LSMM)具有模型简单和物理意义明确等优点而得以广泛使用。然而,FCLS-LSMM的传统优化求解方法的迭代过程非常复杂。近年提出的几何方法为降低LSMM的求解复杂度提供了新思路,但是所获得的结果并非真正意义上的全约束最小二乘解。为此,建立一种完全符合FCLS要求的LSMM几何求解方法,具有复杂度低和可以获得理论最优解等优点。实验表明了所提出方法的有效性。     

Asymptotically efficient estimation of spectral moments

The article studies parametric estimation of spectral moments of a zero-mean complex Gaussian stationary process immersed in independent Gaussian noise. With the merit of the maximum-likelihood (ML) approach as motivation, this work exploits a Whittle's (1953) type objective function that is able to capture the relevant features of the log-likelihood function while being much more manageable. The resulting estimates are strongly consistent and asymptotically efficient. As an example, application to Doppler weather radar data is considered     

Cross spectral analysis of nonstationary processes

Consideration is given to the generalization of stationary cross spectral analysis methods to a class of nonstationary processes, specifically, the class of semistationary finite energy processes possessing sample functions that are of finite energy almost surely. A new quantity called the time-frequency coherence (TFC) is defined, and it is demonstrated that its properties are analogous to those possessed by the stationary coherence function. The problem of estimating the TFC by using elements of L. Cohen's (1966) class of joint time-frequency representations is investigated. It is shown that the only admissible estimators are those based on the class of time-frequency smoothed periodograms. Thus, the familiar procedure of segmentation and (smoothed) short-time Fourier analysis cannot be improved upon (within the framework considered) by the use of the higher-resolution nonparametric time-frequency methods. Procedures for selection of the appropriate estimators and a possible application are suggested     

The application of spectral estimation methods to bearing estimation problems

The equivalence between the problem of determining the bearing of a radiating source with an array of sensors and the problem of estimating the spectrum of a signal is demonstrated. Modern spectral estimation algorithms are derived within the context of array processing using an algebraic approach. Emphasis is placed on the problem of determining the bearing of a sound source with an array. Special issues encountered in applying these estimates are discussed.     

基于统计的MODIS地表反射率简单估算方法

利用NASA-ESE提供的大气校正算法对EOS/MODIS进行计算,得到CH1-7的地面反射率,再与MODIS所接收到的大气上界反射率数据进行比较,参考野外实测得到的大气光学特性和地面反射率数据,提出一种基于统计方法的MODIS前7个通道的大气校正方法.     

A brief summary of a generalized framework for power spectral estimation

This correspondence presents a brief summary of a generalized framework for power spectral estimation and shows how three previous estimation methods fit into this framework as special cases. Further, this correspondence clarifies some recent comments made on spectral estimation; in particular specific references are given that strongly support the use of overlapped weighted segment averaging for spectral estimation.     

Smoothed nonparametric spectral estimation via cepsturm thresholding - Introduction of a method for smoothed nonparametric spectral estimation

Cepstrum thresholding is shown to be an effective, automatic way of obtaining a smoothed nonparametric estimate of the spectrum of a stationary signal. In the process of introducing the cepstrum thresholding-based spectral estimator, we discuss a number of results on the cepstrum of a stationary signal, which might also be of interest to researchers in spectral analysis and allied topics, such as speech processing