周期性凹口数据的雷达图像恢复

在转台目标雷达成像中,数据的缺失及背景杂波的存在会严重影响图像的质量,降低图像分辨力.本文采用零多普勒杂波估计与删减算法滤除固定背景杂波,改进Burg算法恢复缺失数据,并将两者结合形成了含数据凹口及强多普勒杂波数据的二维ISAR成像算法流程,实现了对T72坦克不完整数据的360°全方位二维ISAR图像恢复.实验结果证明了算法的有效性.     

A two-dimensional technique for frequency-wavenumber estimation

Simultaneous frequency and wavenumber estimation using two-dimensional (2-D) linear prediction on a space-time data array is investigated. The method used is a direct extension of our previously presented one-dimensional (1-D) frequency estimation technique. It is relatively simple computationally and is superior to the 2-D Fourier transform method in resolving signals closely spaced in frequency and wavenumber.     

An efficient algorithm for two-dimensional frequency estimation

This paper presents a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. We derive the theoretical performance of the 2D-MODE estimator and show that it is asymptotically statistically efficient under either the assumption that the number of temporal snapshots is large or the signal-to-noise ratio is high. Numerical examples showing the performance of this algorithm and comparing it with the computationally efficient subspace rotation algorithms are also given. We show that the statistical performance of the 2D-MODE algorithm is better than that of the subspace rotation methods. The amount of computations required by the former is no more than a few times of that needed by the latter for either small numbers of spatial measurements or a single temporal snapshot, which are the cases of interest herein.     

A novel method for two-dimensional frequency estimation

This paper proposes a novel solution to two-dimensional (2-D) frequency estimation problems. The solution is applicable to the cases where the data length is much larger in one dimension than the other. The method estimates 2-D frequencies based on several one-dimensional (1-D) frequency estimation processes and hence has a low computational complexity. To avoid resolving close frequencies in 1-D processing, we construct matrices to estimate the linear combinations of the 2-D frequencies. Performance evaluation of this method is presented based on the comparison of the Crame/spl acute/r-Rao bound (CRB) and numerical simulations.     

Cumulant-based LP method for two-dimensional spectral estimation

A cumulant-based linear prediction (CBLP) method for two-dimensional (2-D) spectral estimation is presented. The main idea of the method is to compute the coefficients of two different single-quadrant prediction filters by applying the LP theory to a selected 2-D fourth-order mixed cumulant slice of the noisy signal. These coefficients are employed in formulating two different autoregressive spectral models. Both spectral models are combined to obtain the desired spectral estimate. The effectiveness of the proposed CBLP method is demonstrated through computer simulation     

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     

Order-recursive spectrum estimation

The use of a multiple-pole filter in the time-average estimation of the autocorrelation allows the power spectrum estimates to be recursive in the order of multiplicity of the filter pole. The recursive generation of the estimates from various filter orders provides the flexibility to select the estimator of interest in terms of the variance and spectral and temporal resolution     

Robust-resistant spectrum estimation

Conventional spectrum estimates of both the smoothed-periodogram and autoregressive variety lack robustness toward outliers in the original data. Outliers and other local perturbations are modeled by non-Gaussian additive noise, which is zero most of the time. Correspondingly, the lack of robustness of the conventional estimates of the spectrum manifest not only inflated variances but also damaging asymptotic biases. This paper discusses robust-resistant methods of spectrum estimation which do not suffer in this way. The main approach involves "data cleaning" by either one-sided or two-sided outlier interpolators based on autoregressive approximations. The autoregressive coefficients are themselves estimated robustly in an iterative loop along with the data-cleaning operation. The well-cleaned data are then used along with the autoregressive model to form smoothed spectral density estimates via prewhitening. Study of the so-called "linear part" of the nonlinear outlier interpolator algorithm shows that considerable bias reduction is realizable through use of the robust procedure. Some examples of applications of the robust methodology are presented. Special considerations for real-time processing and large data sets are discussed. Extensions of the method to cross-spectrum estimation, missing data, and irregularly spaced data problems are briefly mentioned.     

Amplitude estimation of signal with unknown duration

Quasilikelihood and maximum likelihood algorithms for estimating the amplitude of arbitrary waveform signal with unknown duration have been synthesized. Characteristics of the synthesized algorithms have been also found.     

Wavelet thresholding techniques for power spectrum estimation

Estimation of the power spectrum S(f) of a stationary random process can be viewed as a nonparametric statistical estimation problem. We introduce a nonparametric approach based on a wavelet representation for the logarithm of the unknown S(f). This approach offers the ability to capture statistically significant components of ln S(f) at different resolution levels and guarantees nonnegativity of the spectrum estimator. The spectrum estimation problem is set up as a problem of inference on the wavelet coefficients of a signal corrupted by additive non-Gaussian noise. We propose a wavelet thresholding technique to solve this problem under specified noise/resolution tradeoffs and show that the wavelet coefficients of the additive noise may be treated as independent random variables. The thresholds are computed using a saddle-point approximation to the distribution of the noise coefficients     

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.     

Nonparametric spectral analysis of gapped data via an adaptive filtering approach

We present an algorithm for nonparametric complex spectral analysis of gapped data via an adaptive finite impulse response (FIR) filtering approach, referred to as the gapped-data amplitude and phase estimation (GAPES) algorithm. The incomplete data sequence may contain gaps of various sizes. The GAPES algorithm iterates the following two steps: (1) estimating the adaptive FIR filter and the corresponding complex spectrum via amplitude and phase estimation (APES), a nonparametric adaptive FIR filtering approach, and (2) filling in the gaps via a least-squares APES fitting criterion. The initial condition for the iteration is obtained from the available data segments via APES. Numerical results are presented to demonstrate the effectiveness of the proposed GAPES algorithm.This work was supported in part by the Senior Individual Grant Program of the Swedish Foundation for Strategic Research, AFRL/SNAT, Air Force Research Laboratory, Air Force Material Command, USAF, under grant number F33615-99-1-1507, and the National Science Foundation Grant MIP-9457388. The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notice thereon.     

Power spectrum parameter estimation

The power spectrum Of a zero-mean stationary Gaussian random process is assumed to be known except for one or more parameters which are to be estimated from an observation of the process during a finite time interval. The approximation is introduced that the coefficients of the Fourier series expansion of a realization of long-time duration are uncorrelated. Based on this approximation maximum likelihood estimates are derived and lundamental limits on the variances attainable are found by evaluation of the Cramér-Rao lower bound. Parameters specifically considered are amplitude, center frequency, and frequency scale factor. Also considered is ripple frequency which refers to the cosine factor in the spectrum produced by the addition of a delayed replica of the random process. The dual problem of estimating parameters of the time-varying power level of a nonstationary baud-limited white noise process is examined.     

PARAFAC-based channel estimation and data recovery in nonlinear MIMO spread spectrum communication systems

In this paper, a new tensorial modeling is first proposed for nonlinear multiple-input multiple-output (MIMO) direct sequence spread spectrum communication systems. The channel is modeled as an instantaneous MIMO Volterra system. Then, a direct data approach for joint blind channel estimation and data recovery is developed using the parallel factor (PARAFAC) decomposition of a third-order tensor composed of received signals, exploiting space, time and code diversities. A blind channel estimation method based on the PARAFAC decomposition of a fifth-order tensor composed of covariances of the received signals is also proposed, considering phase shift keying (PSK) modulated transmitted signals. The proposed estimation algorithms are evaluated by simulating a nonlinear uplink MIMO radio over fiber (ROF) communication system.     

Target adjacency effect estimation using ground spectrum measurement and Landsat-5 Satellite data

This paper addresses the estimation of adjacency effect in ground spectrum and Landsat-5 pixels. The adjacency effect influences the digital number value of a pixel by adding surface surrounding scatter signals and path scatter signals at the sensor. Along with the increasing use of satellite high-resolution imagery and quantitative remote sensing, much attention has been paid to the experimental measurement and estimation of the natural phenomena of adjacency effects. Based on the theory of radiation transfer, a procedure was designed to measure the reflectance from the surface target materials and the materials in a box which is 1.5 m above the surface to avoid upwelling reflectance. At every 3/spl times/3 sites, the measurement was carried out during 10:30 to 13:30 of local time at the Guanting Remote Sensing Test Site in north Beijing. The results show that the adjacency effect becomes stronger from visible, near infrared to shortwave infrared wavelength; the adjacency effect weakens with the increase of distance between testing site. At last, the adjacency effect of Landsat-5 image was corrected, and the quality of the resulting image was improved.     

Computationally efficient two-dimensional Capon spectrum analysis

We present a computationally efficient algorithm for computing the 2-D Capon (1969) spectral estimator. The implementation is based on the fact that the 2-D data covariance matrix will have a Toeplitz-block-Toeplitz structure, with the result that the inverse covariance matrix can be expressed in closed form by using a special case of the Gohberg-Heinig (1974) formula that is a function of strictly the forward 2-D prediction matrix polynomials. Furthermore, we present a novel method, based on a 2-D lattice algorithm, to compute the needed forward prediction matrix polynomials and discuss the difference in the so-obtained 2-D spectral estimate as compared with the one obtained by using the prediction matrix polynomials given by the Whittle-Wiggins-Robinson (1963, 1965) algorithm. Numerical simulations illustrate the improved resolution as well as the clear computational gain in comparison to both the well-known classical implementation and the method published by Liu et al.(see IEEE Trans. Aerosp. Electron. Syst., vol.34, p.1314-19, 1998)     

宽带循环平稳信号的二维空间谱估计

本文提出了一种新的宽带二维波达方向估计方法。理论分析表明,该方法不需要进行二维搜索,既适用于宽带信号,也适用于窄带信号,且能够实现二维参数的自动配对。通过利用循环平稳特性,该方法能够抑制宽带干扰和加性噪声的影响,从而大大提高了算法的信号检测能力。     

On the smoothed periodogram method for spectrum estimation

The performance of three alternative methods for spectrum estimation by periodogram smoothing is analysed. Computed results show that the use of tapering windows is harmful in that it increases the variance of the computed spectrum for little return in the form of leakage suppression. In contrast, interval doubling by concatenation of zeroes reduces variance as well as effectfuly supresses leakage.     

Multitaper spectrum estimation for time series with gaps

Gaps in time series can produce spurious features in power spectrum estimates. These artifacts can be suppressed by averaging spectrum estimates obtained by first windowing the time series with a collection of orthogonal tapers. Such multitaper methods have been used for data without gaps since the early 1980s and for more general sampling schemes since the late 1980s. We introduce three families of tapers for time series with gaps. Two of the families solve optimization problems. They minimize bounds on different measures of bias. Computing them involves solving large eigenvalue problems with special structure that can be exploited to construct efficient algorithms. The third family solves no particular optimization problem but is inexpensive to compute and gives spectrum estimates that are quite similar to the other two for actual and simulated helioseismic data. All three families of gap-adapted multitaper estimates have lower variance and bias than the periodogram. In simulations of helioseismic data with gaps, standard methods for constructing confidence intervals for multitaper spectrum estimates, including parametric approximations and resampling in the temporal and spectral domains, all failed to attain their nominal confidence level. We present a novel resampling technique that, in the same simulations, gave confidence intervals that attained the correct confidence level.     

Noncausal 2-D spectrum estimation for direction finding

A two-dimensional noncausal autoregressive (NCAR) plus additive noise model-based spectrum estimation method is presented for planar array data typical of signals encountered in array processing applications. Since the likelihood function for NCAR plus noise data is nonlinear in the model parameters and is further complicated by the unknown variance of the additive noise, computationally intensive gradient search algorithms are required for computing the estimates. If a doubly periodic lattice is assumed, the complexity of the approximate maximum likelihood (ML) equation is significantly reduced without destroying the theoretical asymptotic properties of the estimates and degrading the observed accuracy of the estimated spectra. Initial conditions for starting the approximate ML computation are suggested. Experimental results that can be used to evaluate the signal-plus-noise approach and compare its performance to those of signal-only methods are presented for Gaussian and simulated planar array data. Statistics of estimated spectrum parameters are given, and estimated spectra for signals with close spatial frequencies are shown. The approximate ML parameter estimate's asymptotic properties, such as consistency and normality, are established, and lower bounds for the estimate's errors are derived, assuming that the data are Gaussian