Pub Date : 1992-10-07DOI: 10.1109/SSAP.1992.246867
T. Abatzoglou, L. Lam
The authors address the problem of estimating the 2-dimensional frequencies from a set of double indexed samples consisting of unknown linear combinations with efficiency. These problems arise in high resolution radar/sonar imaging, array signal processing and nuclear magnetic resonance imaging. A new approach is based on the annihilator method and a generalization of the CTLS technique. Simulation results show that this approach can estimate the 2-D frequencies with accuracies approaching the Cramer-Rao bound even when the separation of the sinusoids is a fraction of the discrete Fourier transform resolution bin.<>
{"title":"Efficient estimation of 2-dimensional frequencies of sinusoids by the annihilator method and constrained total least squares","authors":"T. Abatzoglou, L. Lam","doi":"10.1109/SSAP.1992.246867","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246867","url":null,"abstract":"The authors address the problem of estimating the 2-dimensional frequencies from a set of double indexed samples consisting of unknown linear combinations with efficiency. These problems arise in high resolution radar/sonar imaging, array signal processing and nuclear magnetic resonance imaging. A new approach is based on the annihilator method and a generalization of the CTLS technique. Simulation results show that this approach can estimate the 2-D frequencies with accuracies approaching the Cramer-Rao bound even when the separation of the sinusoids is a fraction of the discrete Fourier transform resolution bin.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126441330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-10-07DOI: 10.1109/SSAP.1992.246808
L. Sciacca, R. Evans
This paper describes a noncoherent ultrasonic array used to form three-dimensional images of defects in metal. The problem developed in terms of deconvolution in multiple dimensions to improve resolution of images blurred by the measuring system and degraded by noise is reduced to solution of a linear equation of the form y=Hx, where H is called the imaging operator H may be separated into the Kronecker product of smaller banded-Toeplitz matrices V(X)S(X)P. This structure is used to develop an algorithm to solve for X using least squares and singular value decomposition.<>
{"title":"Signal processing applied to ultrasonic imaging","authors":"L. Sciacca, R. Evans","doi":"10.1109/SSAP.1992.246808","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246808","url":null,"abstract":"This paper describes a noncoherent ultrasonic array used to form three-dimensional images of defects in metal. The problem developed in terms of deconvolution in multiple dimensions to improve resolution of images blurred by the measuring system and degraded by noise is reduced to solution of a linear equation of the form y=Hx, where H is called the imaging operator H may be separated into the Kronecker product of smaller banded-Toeplitz matrices V(X)S(X)P. This structure is used to develop an algorithm to solve for X using least squares and singular value decomposition.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125671124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hidden Markov models of chaotic signals have been used in numerical detection experiments. For broadband deterministic chaotic signals masked with noise having identical spectra at an SNR of -15 db, the experiments found flawless receiver operating characteristics. In noisy environments the performance of models trained on noise-free signals can be improved by training on signals contaminated by noise typical of the test environment. Continuous valued scalar outputs at each discrete hidden state are modeled as Gaussians with means that depend autoregressively on previous outputs.<>
{"title":"Detecting chaotic signals with nonlinear models","authors":"A. Fraser, Q. Cai","doi":"10.15760/ETD.6448","DOIUrl":"https://doi.org/10.15760/ETD.6448","url":null,"abstract":"Hidden Markov models of chaotic signals have been used in numerical detection experiments. For broadband deterministic chaotic signals masked with noise having identical spectra at an SNR of -15 db, the experiments found flawless receiver operating characteristics. In noisy environments the performance of models trained on noise-free signals can be improved by training on signals contaminated by noise typical of the test environment. Continuous valued scalar outputs at each discrete hidden state are modeled as Gaussians with means that depend autoregressively on previous outputs.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126054931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-10-07DOI: 10.1109/SSAP.1992.246853
W. Xu, J. Pierre, M. Kaveh
This paper discusses some of the practical limitations of detection methods formulated in terms of the eigenvalues of the sample covariance matrix of the output of a sensor array. It presents an approach based on the principal eigenvectors and the measured array manifold that appears to be at least as sensitive, but apparently much more robust than methods such as AIC and MDL. Comparative performance results are given for simulation data with a variety of noise statistics and for data obtained from an experimental array.<>
{"title":"Practical detection with calibrated arrays","authors":"W. Xu, J. Pierre, M. Kaveh","doi":"10.1109/SSAP.1992.246853","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246853","url":null,"abstract":"This paper discusses some of the practical limitations of detection methods formulated in terms of the eigenvalues of the sample covariance matrix of the output of a sensor array. It presents an approach based on the principal eigenvectors and the measured array manifold that appears to be at least as sensitive, but apparently much more robust than methods such as AIC and MDL. Comparative performance results are given for simulation data with a variety of noise statistics and for data obtained from an experimental array.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126187304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-10-07DOI: 10.1109/SSAP.1992.246884
F. Li, H. Liu
The new approach includes the steps of preprocessing to extract structure, several subspace methods (ESPRIT, MUSIC, and Min-Norm) to estimate time delay of seismic arrival at each sensor, and postprocessing. The advantages are high resolution and less computation.<>
{"title":"A high-resolution approach to simultaneously estimate seismic stacking velocity and zero-offset time","authors":"F. Li, H. Liu","doi":"10.1109/SSAP.1992.246884","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246884","url":null,"abstract":"The new approach includes the steps of preprocessing to extract structure, several subspace methods (ESPRIT, MUSIC, and Min-Norm) to estimate time delay of seismic arrival at each sensor, and postprocessing. The advantages are high resolution and less computation.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126963792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-10-07DOI: 10.1109/SSAP.1992.246857
A. Quinn
The author adopts a strong Bayesian philosophy and derives the marginal inference for the nonlinear parameters in a general deterministic signal model, having integrated over the linear terms. The marginal inference is shown to embody Ockham's razor in an objective manner via the Ockham parameter inference. From this, a new definition of hypothesis complexity, is proposed. The marginal inference provides a means of testing the status of an alternative-free hypothesis, thereby unifying the detection and estimation tasks. Robust estimates may then be inferred below the thresholds for maximum likelihood estimation. The analysis is extended to a multi-hypothesis environment, using the example of a periodic model of unknown order. The fundamental frequency is estimated in a unified procedure which can either (i) simultaneously estimate the model order, or (ii) marginalize analytically over the model order. Both techniques confer improved inferential consistency and a much reduced numerical load when compared with the popular evidence-based technique, which is also described.<>
{"title":"A unified approach to model-based signal processing using Bayesian marginal inference","authors":"A. Quinn","doi":"10.1109/SSAP.1992.246857","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246857","url":null,"abstract":"The author adopts a strong Bayesian philosophy and derives the marginal inference for the nonlinear parameters in a general deterministic signal model, having integrated over the linear terms. The marginal inference is shown to embody Ockham's razor in an objective manner via the Ockham parameter inference. From this, a new definition of hypothesis complexity, is proposed. The marginal inference provides a means of testing the status of an alternative-free hypothesis, thereby unifying the detection and estimation tasks. Robust estimates may then be inferred below the thresholds for maximum likelihood estimation. The analysis is extended to a multi-hypothesis environment, using the example of a periodic model of unknown order. The fundamental frequency is estimated in a unified procedure which can either (i) simultaneously estimate the model order, or (ii) marginalize analytically over the model order. Both techniques confer improved inferential consistency and a much reduced numerical load when compared with the popular evidence-based technique, which is also described.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122619326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-10-07DOI: 10.1109/SSAP.1992.246788
H. Lee, R. Stovall
The paper addresses both the problems of estimating DOA from a single array snapshot, and also from a sequence of array snapshots possibly having different polarizations. The principal result is identification of the maximum likelihood estimator of DOA. The associated estimation algorithm is designated as the dual beam scan (DBS) Algorithm. For large signal-to-noise ratios the DBS algorithm is unbiased, and has minimum variance. The DBS computational requirements are modest, and similar to those of conventional monopulse processors. Specifically the eigenanalyses associated with the extended MUSIC algorithm are bypassed.<>
{"title":"A simple efficient algorithm for determining the direction of arrival for a single emitter with unknown polarization","authors":"H. Lee, R. Stovall","doi":"10.1109/SSAP.1992.246788","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246788","url":null,"abstract":"The paper addresses both the problems of estimating DOA from a single array snapshot, and also from a sequence of array snapshots possibly having different polarizations. The principal result is identification of the maximum likelihood estimator of DOA. The associated estimation algorithm is designated as the dual beam scan (DBS) Algorithm. For large signal-to-noise ratios the DBS algorithm is unbiased, and has minimum variance. The DBS computational requirements are modest, and similar to those of conventional monopulse processors. Specifically the eigenanalyses associated with the extended MUSIC algorithm are bypassed.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"53 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133141472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-10-07DOI: 10.1109/SSAP.1992.246825
A. T. Erdem, M. Sezan
The observed images are allowed to be spatially shifted with respect to one another, and the observation noise is assumed to be Gaussian. An algorithm is proposed that recovers the image by separately reconstructing its Fourier phase and Fourier log-magnitude, in the least-squares sense, from the modulo-2 pi phase and log-magnitude of the bispectrum of the image estimated from the given noisy observations. A technique proposed by the authors is used to unwrap the modulo-2 pi bispectral phase and to reconstruct the Fourier phase of the image. Experimental results demonstrate the performance of the proposed algorithm.<>
{"title":"Least-squares reconstruction of an image from its noisy observations using the bispectrum","authors":"A. T. Erdem, M. Sezan","doi":"10.1109/SSAP.1992.246825","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246825","url":null,"abstract":"The observed images are allowed to be spatially shifted with respect to one another, and the observation noise is assumed to be Gaussian. An algorithm is proposed that recovers the image by separately reconstructing its Fourier phase and Fourier log-magnitude, in the least-squares sense, from the modulo-2 pi phase and log-magnitude of the bispectrum of the image estimated from the given noisy observations. A technique proposed by the authors is used to unwrap the modulo-2 pi bispectral phase and to reconstruct the Fourier phase of the image. Experimental results demonstrate the performance of the proposed algorithm.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114158862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-10-07DOI: 10.1109/SSAP.1992.246848
C. Griffin, I. Kirsteins
A new algorithm for estimating the arrival-times and Doppler-shifts of overlapping signal pulses is based on an extension of a procedure for high resolution time-delay estimation. This approach can exploit prior information from ocean acoustic propagation models about the interference structure.<>
{"title":"Signal resolution in low Doppler interference","authors":"C. Griffin, I. Kirsteins","doi":"10.1109/SSAP.1992.246848","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246848","url":null,"abstract":"A new algorithm for estimating the arrival-times and Doppler-shifts of overlapping signal pulses is based on an extension of a procedure for high resolution time-delay estimation. This approach can exploit prior information from ocean acoustic propagation models about the interference structure.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126057589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-10-07DOI: 10.1109/SSAP.1992.246796
J. W. Silverstein, P. L. Combettes
This paper brings into play elements of the spectral theory of such matrices and demonstrates their relevance to source detection and bearing estimation in problems with sizable arrays. These results are applied to the sample spatial covariance matrix, R, of the sensed data. It is seen that detection can be achieved with a sample size considerably less than that required by conventional approaches. It is argued that more accurate estimates of direction of arrival can be obtained by constraining R to be consistent with various a priori constraints including those arising from large dimensional random matrix theory. A set theoretic formalism is used for this feasibility problem. Unsolved issues are discussed.<>
{"title":"Large dimensional random matrix theory for signal detection and estimation in array processing","authors":"J. W. Silverstein, P. L. Combettes","doi":"10.1109/SSAP.1992.246796","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246796","url":null,"abstract":"This paper brings into play elements of the spectral theory of such matrices and demonstrates their relevance to source detection and bearing estimation in problems with sizable arrays. These results are applied to the sample spatial covariance matrix, R, of the sensed data. It is seen that detection can be achieved with a sample size considerably less than that required by conventional approaches. It is argued that more accurate estimates of direction of arrival can be obtained by constraining R to be consistent with various a priori constraints including those arising from large dimensional random matrix theory. A set theoretic formalism is used for this feasibility problem. Unsolved issues are discussed.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126726563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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