A new method for spectral analysis of non-stationary signals from impact tests

To obtain the spectrum of impulse response from impact test, the paper proposes an approach based on envelope of cross-correlation function. This is achieved by correlating the impulse response and reference single-harmonic signals. An envelope of cross-correlation makes it possible to detect and identify the harmonics of impulse response. The presented method gives a possibility to point the frequency value of the harmonic component of an impulse response signal independently of the commonly used fast Fourier transform. Its main advantage over the fast Fourier transform is that the spectral resolution does not depend on duration of the impulse response

LOW SNR TRESHOLD IN RAPID ESTIMATORS OF COMPLEX SINUSOIDALS

A task of estimation of complex sinusoid frequency is considered. A particular but practically important case of low signal-to-noise ratio (SNR) is studied. The low SNR threshold, commonly overlooked in development of the rapid estimator of complex sinusoidals, is addressed. Signals of different length are considered and SNR is varied in wide limits. It is demonstrated that a simple interpolation with factor 2 lowers the SNR threshold by 1.5dB for the most complicated practical situations. Further interpolation does not bring any improvement. This allows proposing a compromise practical algorithm that provides accuracy close to the limit and is still very simple and fast

SDPLL-Based Frequency Estimation of a Sinusoid in Colored Noise

The problem of frequency estimation of a single sinusoid observed in colored noise is addressed. Our estimator is based on the operation of the sinusoidal digital phase-locked loop (SDPLL) which carries the frequency information in its phase error after the noisy sinusoid has been acquired by the SDPLL. We show by computer simulations that this frequency estimator beats the Cramer-Rao bound (CRB) on the frequency error variance for moderate and high SNRs when the colored noise has a general low-pass filtered (LPF) characteristic, thereby outperforming, in terms of frequency error variance, several existing techniques some of which are, in addition, computationally demanding. Moreover, the present approach generalizes on existing work that addresses different methods of sinusoid frequency estimation involving specific colored noise models such as the moving average (MA) noise model. An insightful theoretical analysis is presented to support the practical findings

Computational imaging and automated identification for aqueous environments

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2011Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods. Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classi fication with bag-of-words models and multi-stage boosting for rock sh detection. Methods for extracting images of sh from videos of longline operations are demonstrated. A prototype digital holographic imaging device is designed and tested for quantitative in situ microscale imaging. Theory to support the device is developed, including particle noise and the effects of motion. A Wigner-domain model provides optimal settings and optical limits for spherical and planar holographic references. Algorithms to extract the information from real-world digital holograms are created. Focus metrics are discussed, including a novel focus detector using local Zernike moments. Two methods for estimating lateral positions of objects in holograms without reconstruction are presented by extending a summation kernel to spherical references and using a local frequency signature from a Riesz transform. A new metric for quickly estimating object depths without reconstruction is proposed and tested. An example application, quantifying oil droplet size distributions in an underwater plume, demonstrates the efficacy of the prototype and algorithms.Funding was provided by NOAA Grant #5710002014, NOAA NMFS Grant #NA17RJ1223, NSF Grant #OCE-0925284, and NOAA Grant #NA10OAR417008

Frequency and phase estimation in time series with quasi periodic components

A classical model in time series analysis is a stationary process superposed by one or several deterministic sinusoidal components. Di erent methods are applied to estimate the frequency (w) of those components such as Least Squares Estimation and the maximization of the periodogram. In many applications the assumption of a constant frequency is violated and we turn to a time dependent frequency function (w(s)). For example in the physics literature this is viewed as nonlinearity of the phase of a process. A way to estimate w(s) is the local application of the above methods. In this dissertation we study the maximum periodogram method on data segments as an estimator of w(s) and subsequently a least squares technique for estimating the phase. We prove consistency and asymptotic normality in the context of "infill asymptotics", a concept that off ers a meaningful asymptotic theory in cases of local estimations. Finally, we investigate an estimator based on a local linear approximation of the frequency function, prove its consistency and asymptotic normality in the "infi ll asymptotics" sense and show that it delivers better estimations than the ordinary periodogram. The theoretical results are also supported by some simulations

Deep and near space tracking stations in support of lunar and planetary exploration missions

The aim of this dissertation is to describe the methodologies required to design, operate, and validate the performance of ground stations dedicated to near and deep space tracking, as well as the models developed to process the signals acquired, from raw data to the output parameters of the orbit determination of spacecraft. This work is framed in the context of lunar and planetary exploration missions by addressing the challenges in receiving and processing radiometric data for radio science investigations and navigation purposes. These challenges include the designing of an appropriate back-end to read, convert and store the antenna voltages, the definition of appropriate methodologies for pre-processing, calibration, and estimation of radiometric data for the extraction of information on the spacecraft state, and the definition and integration of accurate models of the spacecraft dynamics to evaluate the goodness of the recorded signals. Additionally, the experimental design of acquisition strategies to perform direct comparison between ground stations is described and discussed. In particular, the evaluation of the differential performance between stations requires the designing of a dedicated tracking campaign to maximize the overlap of the recorded datasets at the receivers, making it possible to correlate the received signals and isolate the contribution of the ground segment to the noise in the single link. Finally, in support of the methodologies and models presented, results from the validation and design work performed on the Deep Space Network (DSN) affiliated nodes DSS-69 and DSS-17 will also be reported

Computational imaging and automated identification for aqueous environments

Thesis (Ph. D.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2011."June 2011." Cataloged from PDF version of thesis.Includes bibliographical references (p. 253-293).Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods. Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classification with bag-of-words models and multi-stage boosting for rock sh detection. Methods for extracting images of sh from videos of long-line operations are demonstrated. A prototype digital holographic imaging device is designed and tested for quantitative in situ microscale imaging. Theory to support the device is developed, including particle noise and the effects of motion. A Wigner-domain model provides optimal settings and optical limits for spherical and planar holographic references. Algorithms to extract the information from real-world digital holograms are created. Focus metrics are discussed, including a novel focus detector using local Zernike moments. Two methods for estimating lateral positions of objects in holograms without reconstruction are presented by extending a summation kernel to spherical references and using a local frequency signature from a Riesz transform. A new metric for quickly estimating object depths without reconstruction is proposed and tested. An example application, quantifying oil droplet size distributions in an underwater plume, demonstrates the efficacy of the prototype and algorithms.by Nicholas C. Loomis.Ph.D

Rapid Frequency Estimation

Frequency estimation plays an important role in many digital signal processing applications. Many areas have benefited from the discovery of the Fast Fourier Transform (FFT) decades ago and from the relatively recent advances in modern spectral estimation techniques within the last few decades. As processor and programmable logic technologies advance, unconventional methods for rapid frequency estimation in white Gaussian noise should be considered for real time applications. In this thesis, a practical hardware implementation that combines two known frequency estimation techniques is presented, implemented, and characterized. The combined implementation, using the well known FFT and a less well known modern spectral analysis method known as the Direct State Space (DSS) algorithm, is used to demonstrate and promote application of modern spectral methods in various real time applications, including Electronic Counter Measure (ECM) techniques

Recent advances in rapid frequency estimation

There are two main types of frequency estimator-the traditional, based on FFT's, and the more modern, based on linear filtering. Statistical and computational efficiency usually have to be traded off against each other. Typically the filter-based estimators are statistically grossly inefficient. This paper discusses the extension of the Quinn-Fernandes (filter-based, statistically optimal) technique to complex-valued signals, and the use of zero-padding to improve the statistical efficiency of the Fourier transform interpolative (FTI) estimator to statistical near-optimality.7 page(s