Modern Spectral Estimation Techniques Research Articles

A NEW PARAMETER ESTIMATION METHOD FOR GTD MODEL BASED ON MODIFIED COMPRESSED SENSING

The electromagnetic scattering mechanism of radar targets in the high-frequency domain can be characterized exactly by geometrical theory of difiraction (GTD) model. In this paper, we propose a novel parameter estimation method for GTD model based on compressed sensing. The sparse characteristic of radar echoes is analyzed, and the parameter estimation problem is converted to one of sparse signal reconstruction. Furthermore, clustering and linear least-minimum-squares algorithms are utilized to improve the accuracy of the result. Compared with several modern spectral estimation techniques, the proposed method gives a more precise estimation of the GTD model parameters, especially the scattering centers. Simulations with synthetic and measured data in an anechoic chamber conflrm the efiectiveness of the method.

Multibaseline cross-track SAR interferometry: a signal processing perspective

Synthetic aperture radar interferometry (InSAR) is a powerful and increasingly expanding technique for measuring the topography of a surface, its changes over both short- and long-time scale, and other changes in the detailed characteristics of the surface. We provide a tutorial description of recent results on multibaseline (MB) InSAR processing. The main focus is on the problem of retrieving both heights and radar reflectivities of natural layover areas by means of a cross-track InSAR (XTI-SAR) system with a uniform linear array (ULA). It is formulated as the problem of detecting and estimating a multicomponent signal corrupted by multiplicative noise - the speckle in the radar imaging jargon - and by additive white Gaussian noise. Application to the InSAR problem of both nonparametric and parametric modern spectral estimation techniques is described. The problem of estimating the number of signal components in the presence of speckle is also addressed. Finally, a brief mention is given to recent research trends on robust methods for nonperfectly calibrated arrays, on processing for non-ULA configurations, and on MB SAR tomography, which is an extension of MB SAR interferometry for the full 3D mapping of semitransparent volume scattering layers. The state of the art of other advanced multichannel interferometric techniques is also briefly recalled.

Implementation of algorithms for extracting tonal components in underwater noise

Underwater target classification and tracking problems utilize the noise signals emanating from the targets as well as the target dynamics features. Target classification is carried out by extracting certain classification clues about the target such as its emission frequencies, and other target specific features from the self noise or active transmissions. The self noise generated by the targets being nonstationary in nature are to be analyzed with short term averaged data segments for extracting the tonal components using various spectral estimation techniques. Though classical spectral analysis techniques are computationally efficient, it suffers from several inherent limitations such as frequency resolution, performance degradation due to implicit windowing of the data, etc. In an attempt to improve the spectral resolution, several modern spectral estimation techniques, which utilize the procedures of indirect Fourier analysis by fitting the measured short data segments to an assumed model, have been evolved. This paper presents the development of procedures for the estimation of power spectral densities using modern techniques based on parameter estimation such as auto regressive, moving average, auto regressive moving average, maximum likelihood, etc. The performance of the estimator has been validated by computing the emission frequencies of a 50-foot vessel.

On the potential applicability of auto-regressive spectral estimation to HF chirp sounders

Abstract A major limitation of HF chirp sounders is the use of fast Fourier transform (FFT) analysis to resolve the different multipath components. To reduce the effects of spectral leakage associated with the FFT, the signal is usually weighted by a suitable window prior to spectral analysis. For negligible frequency dispersion, multipath resolution including the two magneto-ionic waves is determined by the width of the mainlobe of the window function whereas, for large frequency dispersion, the use of window functions improves the resolution of multipath. Modern spectral estimation techniques such as auto-regressive (AR) modelling offer higher resolution capabilities which are only limited by the signal to noise ratio. This paper discusses the required stages for both FFT analysis and AR analysis. The results of applying both techniques to ionospheric data obtained over a short skywave link in the U.K. are presented. These include distributions for the 6 dB width, group time delay and order of the AR model. The resolution advantage of the AR method is demonstrated by ionograms.

A perspective on Fourier spectroscopy

This is the substance of a talk presented at the reminiscent session of the Durham Conference on FTS. It is mainly a commentary on the relation of modern spectral estimation techniques to FTS.

Power spectral density bounds (Corresp.)

The determination of a power density spectrum from a finite set of correlation samples is an ill-posed problem. Furthermore. it is not possible even to bound the values that consistent power density spectra can take on at a particular point. A more reasonable problem is to try to determine the total spectral power in some frequency interval. Although this power cannot be determined exactly, upper and lower bounds on its possible values can be determined. This observation leads to a unified treatment of certain classical and modern spectral estimation techniques and to new interpretations for two data adaptive spectral estimators. maximum likelihood method (MLM) and data adaptive spectral estimator (DASE). According to these new interpretations. MLM and DASE provide upper bounds on spectral power in a specified frequency region subject to the assumption that the spectral density is constant in that region. These methods make no use of an extendibility constraint that can be used to obtain tight upper bounds, as well as nontrivial lower bounds on power. Cybenko has studied a related problem of bounding windowed power, for an arbitrary window, with no assumptions about the form of the spectral density. A new type of classical resolution limit for these bounds is derived and a numerical example is presented.