We discuss applications of time-frequency analysis to the investigation of astronomical type signals. In particular, we apply time-frequency techniques to a data set consisting of the kinetic energy in the three body ...
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ISBN:
(纸本)0819425842
We discuss applications of time-frequency analysis to the investigation of astronomical type signals. In particular, we apply time-frequency techniques to a data set consisting of the kinetic energy in the three body problem We explain how the methods of time-frequency analysis shed light on these signals and also how the concept of multicomponent signals is applied to their decomposition. We also discuss methods to do simple filtering and estimation of the signal parameters.
This paper considers ''blind beamforming'' operations on a wireless network of randomly distributed MEM sensors. Maximum power collection criterion is proposed and results in array weights obtained fro...
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ISBN:
(纸本)0819425842
This paper considers ''blind beamforming'' operations on a wireless network of randomly distributed MEM sensors. Maximum power collection criterion is proposed and results in array weights obtained from the eigenvector corresponding to largest eigenvalue of a matrix eigenvalue problem. Theoretical justification of this approach to an extension of Szego's asymptotic distribution of eigenvalues is provided. Numerical results on propagation time delay estimation and loss of coherency due to propagation disturbances are presented.
The primary basis for adaptive radar algorithm design is that (1) a Binary Hypothesis formulation with unknown parameters is an adequate test and (2) that radar interference is composed of combinations of thermal nois...
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ISBN:
(纸本)0819425842
The primary basis for adaptive radar algorithm design is that (1) a Binary Hypothesis formulation with unknown parameters is an adequate test and (2) that radar interference is composed of combinations of thermal noise, self induced clutter, and extraneous noise. This is the typical generalized likelihood formulation that yield the CFAR characteristic for the assumed conditions. implementations have shown that such formulations yield inadequate performance in complex clutter environments. As compensation measure, a secondary CFAR process then addresses the potential violation of this assumption by large ''target-like'' interference such as large Clutter discretes or a large number of targets interfering with each other. In order to detect small targets, an approach based on the Likelihood Statistic provides a technique for optimally suppressing the neighboring large signals. Performance is characterized as a function of a generalized distance and relative signal power ratios in the Joint Space-Time domain.
In time-frequency analysis, we extend functions of one variable to functions of two variables. The functions of two variables provide information about the signal that is not easily discernible from the functions of o...
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ISBN:
(纸本)0819425842
In time-frequency analysis, we extend functions of one variable to functions of two variables. The functions of two variables provide information about the signal that is not easily discernible from the functions of one variable. In this paper, we investigate a method for creating quartic functions of three variables and also a quartic function of all four variables. These quartic functions provide a meaningful representation of the signal that goes beyond the well known quadratic functions. The quartic functions are applied to the design of signal-adaptive kernels for Cohen's class and shown to provide improvements over previous methods.
The Jacobi method for singular value decomposition is well-suited for parallel architectures. Its application to signal subspace computations is well known. Basically the subspace spanned by singular vectors of large ...
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ISBN:
(纸本)0819425842
The Jacobi method for singular value decomposition is well-suited for parallel architectures. Its application to signal subspace computations is well known. Basically the subspace spanned by singular vectors of large singular values are separated from subspace spanned by those of small singular values. The Jacobi algorithm computes the singular values and the corresponding vectors in random order. This requires sorting the result after convergence of the algorithm to select the signal subspace. A modification of the Jacobi method based on a linear objective function merges the sorting into the SVD-algorithm at little extra cost. In fact, the complexity of the diagonal processor cells in a triangular array get slightly larger. In this paper we present these extensions, in particular the modified algorithm for computing the rotation angles and give an example of its usefulness for subspace separation.
This paper addresses the problem of detection and classification of complicated signals in noise. Classical detection methods such as energy detectors and linear discriminant analysis do not perform well in many situa...
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ISBN:
(纸本)0819425842
This paper addresses the problem of detection and classification of complicated signals in noise. Classical detection methods such as energy detectors and linear discriminant analysis do not perform well in many situations of practical interest. We introduce a new approach based on hidden Markov modeling in the wavelet domain. Using training data, we fit a hidden Markov model (HMM) to the wavelet transform to concisely represent its probabilistic time-frequency structure. The HMM provides a natural framework for performing likelihood ratio tests used in signal detection and classification. We compare our approach with classical methods for classification of nonlinear processes, change-point detection, and detection with unknown delay.
作者：
Pitton, JWMathSoft
Seattle WA 98109 1700 Westlake Ave N United States
An algorithm for computing positive time-frequency distributions (TFDs) for nonstationary signals is presented. This work extends the earlier work of the author and his colleagues in computing positive TFDs [11, 15]. ...
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ISBN:
(纸本)0819425842
An algorithm for computing positive time-frequency distributions (TFDs) for nonstationary signals is presented. This work extends the earlier work of the author and his colleagues in computing positive TFDs [11, 15]. A general approach to the problem of computing these signal-dependent distributions is derived. The method is based on an evolutionary spectrum formulation of positive TFDs. Following earlier work [15], a relationship is derived between positive TFDs and the ambiguity function of the observed signal. In particular, it is shown that the TFD is approximately equal to the two-dimensional Fourier transform of the ambiguity function. A method for computing the positive TFD is then presented based on minimizing the squared error in this approximation subject to the TFD being positive and satisfying the time and frequency marginals. The squared error may be weighted non-uniformly, resulting in a constrained weighted least-squares optimization problem. A solution to this optimization problem based on an alternating projections framework is presented, and an example is provided. The resulting TFD provides excellent time-frequency resolution while maintaining positivity and satisfying the marginals.
Spread spectrum modulation techniques such as frequency-hopped code division multiple access (FH-CDMA) are an efficient way to allow multi-user transmission over a limited bandwidth. Recently, there has been a push to...
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ISBN:
(纸本)0819425842
Spread spectrum modulation techniques such as frequency-hopped code division multiple access (FH-CDMA) are an efficient way to allow multi-user transmission over a limited bandwidth. Recently, there has been a push to increase user capacity over the same bandwidth. In particular, smart antenna arrays have been studied for their capacity gain potential. In this regard, we propose the use of blind signal separation for recovery of signals received through an antenna array. To implement this technique requires the antenna array vectors to be stationary which does not hold in FH-CDMA, so we also propose a new method for frequency compensation. We present the theoretical details of the frequency compensation and compare its performance to no frequency compensation. We then present the blind signal separation algorithm applied to a complex antenna array matrix and complex signals with noise and show in simulation that the blind signal separation works.
Blind source separation is an emerging field of fundamental research with a broad range of applications. It is motivated by practical problems that involve several source signals and several sensors. Each sensor recei...
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ISBN:
(纸本)0819425842
Blind source separation is an emerging field of fundamental research with a broad range of applications. It is motivated by practical problems that involve several source signals and several sensors. Each sensor receives an instantaneous linear mixture of the source signals. The problem of the blind source separation consists then of recovering the original waveforms of the sources without any knowledge of the mixture structure. So far, the problem of the blind source separation has been solved using statistical information available on the source signals. A blind source separation approach for non-stationary signals based on time-frequency representations (TFR) have been recently introduced by the authors (SPIE 1996). Herein, we generalize the TFR based blind source separation approach to arbitrary variables, including time and frequency. 'Spatial joint arbitrary variable distributions' are introduced and used for blind source separation via joint diagonalization techniques.
We consider the definition and interpretation of instantaneous frequency and other time-varying frequencies of a signal, and related concepts of instantaneous amplitude, instantaneous bandwidth and the time-varying sp...
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ISBN:
(纸本)0819425842
We consider the definition and interpretation of instantaneous frequency and other time-varying frequencies of a signal, and related concepts of instantaneous amplitude, instantaneous bandwidth and the time-varying spectrum of a signal. A definition for the average frequency at each time is given, and we show that spectrograms and Cohen-Posch time-frequency distributions can yield this result for the first conditional moment in frequency. For some signals this result equals the instantaneous frequency, but generally instantaneous frequency is not the average frequency at each time in the signal. We discuss monocomponent versus multicomponent signals, and give an estimate of the time-varying spectrum given the instantaneous frequencies and bandwidths of the components. We also consider the role of the complex signal in defining instantaneous amplitude, frequency and bandwidth, and ways to obtain a complex signal satisfying certain physical properties, given a real signal (or its time-varying spectrum). Depending upon the physical properties desired (e.g., the instantaneous amplitude of a magnitude-bounded signal should itself be bounded), one obtains different complex representations - and hence different instantaneous amplitudes, frequencies and bandwidths - of the given signal.
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