Pub Date : 1992-10-07DOI: 10.1109/SSAP.1992.246777
A. Tewfik, M. Kim
A new iterative procedure for estimating the direction of arrival of plane waves in the presence of noise with an unknown correlation structure can also be used to separate line spectra from a wide class of smooth spectra. It exploits the differences between the correlation structures of the generalized wavelet coefficients of the noise and signal processes. For simplicity of exposition, the procedure is explained in the context of the problem of localizing narrowband signal sources located in the far-field of a uniformly spaced linear sensor array. It can be applied as well to non-linear or non-uniform arrays and wideband signals.<>
{"title":"Robust wavelet domain array processing","authors":"A. Tewfik, M. Kim","doi":"10.1109/SSAP.1992.246777","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246777","url":null,"abstract":"A new iterative procedure for estimating the direction of arrival of plane waves in the presence of noise with an unknown correlation structure can also be used to separate line spectra from a wide class of smooth spectra. It exploits the differences between the correlation structures of the generalized wavelet coefficients of the noise and signal processes. For simplicity of exposition, the procedure is explained in the context of the problem of localizing narrowband signal sources located in the far-field of a uniformly spaced linear sensor array. It can be applied as well to non-linear or non-uniform arrays and wideband signals.<<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":"131882464","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.1992.246899
B. Veen, J. Joseph, K. Hecox
Measurements of electrical field potentials at the scalp are used to localize sources of electrical activity within the brain. Neuronal sources are modeled as current dipoles. The medum (brain, skull scalp) is linear so that the potential at the scalp is the superposition of the potentials from many active neurons. These properties are used to develop a mathematical model relating neural activity to the spatial distribution of the scalp potential. Linearly constrained minimum variance spatial filters are based on this model to estimate power as a function of position within the brain. Simulations illustrate the effectiveness of this approach for localizing an unknown number of sources.<>
{"title":"Localization of intra-cerebral sources of electrical activity via linearly constrained minimum variance spatial filtering","authors":"B. Veen, J. Joseph, K. Hecox","doi":"10.1109/SSAP.1992.246899","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246899","url":null,"abstract":"Measurements of electrical field potentials at the scalp are used to localize sources of electrical activity within the brain. Neuronal sources are modeled as current dipoles. The medum (brain, skull scalp) is linear so that the potential at the scalp is the superposition of the potentials from many active neurons. These properties are used to develop a mathematical model relating neural activity to the spatial distribution of the scalp potential. Linearly constrained minimum variance spatial filters are based on this model to estimate power as a function of position within the brain. Simulations illustrate the effectiveness of this approach for localizing an unknown number of sources.<<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":"133105795","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.1992.246830
J. Cardoso
In blind array processing, the array manifold is unknown but, under the signal independence assumption, the signal parameters can be estimated by recourse to higher-order information. The author considers the 4th-order cumulant tensor and shows that sample cumulant enhancement based on rank and symmetry properties yield cumulant estimates with exact theoretical structure. Any identification procedure based on enhancement cumulants is then equivalent to cumulant matching, bypassing the need for initialization and optimization.<>
{"title":"Fourth-order cumulant structure forcing: application to blind array processing","authors":"J. Cardoso","doi":"10.1109/SSAP.1992.246830","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246830","url":null,"abstract":"In blind array processing, the array manifold is unknown but, under the signal independence assumption, the signal parameters can be estimated by recourse to higher-order information. The author considers the 4th-order cumulant tensor and shows that sample cumulant enhancement based on rank and symmetry properties yield cumulant estimates with exact theoretical structure. Any identification procedure based on enhancement cumulants is then equivalent to cumulant matching, bypassing the need for initialization and optimization.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"9 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113975635","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.1992.246840
A. Papandreou, F. Hlawatsch, G. Boudreaux-Bartels
The proposed new class of quadratic time-frequency distributions is based on the 'hyperbolic time shift' and scale invariance properties that are important in the analysis of Doppler invariant signals used in bat and dolphin echolocation, and of 'locally self-similar' signals used in fractals and fractional Brownian motion. The hyperbolic class can be characterized by 2-D kernels, and kernel constraints are derived for some desirable TFD properties. The Bertrand distribution and the Altes distribution are members of the hyperbolic class. The authors define a 'localized' subclass and study the intersection between the affine class and the hyperbolic class.<>
{"title":"Quadratic time-frequency distributions: the new hyperbolic class and its intersection with the affine class","authors":"A. Papandreou, F. Hlawatsch, G. Boudreaux-Bartels","doi":"10.1109/SSAP.1992.246840","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246840","url":null,"abstract":"The proposed new class of quadratic time-frequency distributions is based on the 'hyperbolic time shift' and scale invariance properties that are important in the analysis of Doppler invariant signals used in bat and dolphin echolocation, and of 'locally self-similar' signals used in fractals and fractional Brownian motion. The hyperbolic class can be characterized by 2-D kernels, and kernel constraints are derived for some desirable TFD properties. The Bertrand distribution and the Altes distribution are members of the hyperbolic class. The authors define a 'localized' subclass and study the intersection between the affine class and the hyperbolic class.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"34 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":"116256126","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.1992.246878
Y.M. Cheng, D. O'Shaughnessy, P. Mermelstein
Various forms of speech processing in practice take, or can be transformed to, the same form of input/output, and the relation of input/output generally can be expressed by a nonlinear function. This paper proposes a framework for identifying statistically the non-linear function concerning the statistical nature of speech. It demonstrates the applicability of this framework in a particular context, that of speech recovery.<>
{"title":"Statistical signal mapping: a general tool for speech signal processing","authors":"Y.M. Cheng, D. O'Shaughnessy, P. Mermelstein","doi":"10.1109/SSAP.1992.246878","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246878","url":null,"abstract":"Various forms of speech processing in practice take, or can be transformed to, the same form of input/output, and the relation of input/output generally can be expressed by a nonlinear function. This paper proposes a framework for identifying statistically the non-linear function concerning the statistical nature of speech. It demonstrates the applicability of this framework in a particular context, that of speech recovery.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"31 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":"121526969","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.1992.246887
F. A. Sakarya, M. Hayes
A novel approach to on-line localization of partial discharges in a power transformer is presented. A 2-D technique based on subspace rotation is given. By far-field source and narrowband signal assumptions, the localization problem is reduced to an arrival angle estimation problem. The first step uses two eigenvalue decompositions to clean the data and compute the time delays. The second step converts time delays into source locations using the geometrical relationships between them. The new localization technique computes 2-D arrival angles directly from the normalized eigenvectors of a newly derived signal space matrix. The method eliminates the constraint on subarray spacing and the need for a search procedure inherent in most 2-D eigenanalysis techniques, and does not require pairing of angles.<>
{"title":"Partial discharges in high-voltage transformers","authors":"F. A. Sakarya, M. Hayes","doi":"10.1109/SSAP.1992.246887","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246887","url":null,"abstract":"A novel approach to on-line localization of partial discharges in a power transformer is presented. A 2-D technique based on subspace rotation is given. By far-field source and narrowband signal assumptions, the localization problem is reduced to an arrival angle estimation problem. The first step uses two eigenvalue decompositions to clean the data and compute the time delays. The second step converts time delays into source locations using the geometrical relationships between them. The new localization technique computes 2-D arrival angles directly from the normalized eigenvectors of a newly derived signal space matrix. The method eliminates the constraint on subarray spacing and the need for a search procedure inherent in most 2-D eigenanalysis techniques, and does not require pairing of angles.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"11 4 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":"122688128","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.1992.246795
L.V. Dominguez, A. A. Colomer
This paper presents a method to detect transient signals in noise making use of both array processing and time-frequency analysis of signals. The method is applied to ultrasonic signals used to detect flaws in grainy materials.<>
{"title":"Combined sensor array and time-frequency processing for transient signal detection","authors":"L.V. Dominguez, A. A. Colomer","doi":"10.1109/SSAP.1992.246795","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246795","url":null,"abstract":"This paper presents a method to detect transient signals in noise making use of both array processing and time-frequency analysis of signals. The method is applied to ultrasonic signals used to detect flaws in grainy materials.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"51 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":"126198064","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.1992.246879
J. Tourneret, B. Lacaze, F. Castanie
Cepstral coefficients, used in pattern recognition and classification with the k-nearest-neighbor method, give far better results than classification with the centroid distance rule. This paper proposes an analysis of cepstral coefficient probability density which reveals why the k-NN rule is in many instances a necessary tool in this particular representation space.<>
{"title":"Study of the cepstral coefficient probability density function","authors":"J. Tourneret, B. Lacaze, F. Castanie","doi":"10.1109/SSAP.1992.246879","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246879","url":null,"abstract":"Cepstral coefficients, used in pattern recognition and classification with the k-nearest-neighbor method, give far better results than classification with the centroid distance rule. This paper proposes an analysis of cepstral coefficient probability density which reveals why the k-NN rule is in many instances a necessary tool in this particular representation space.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"76 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":"121741344","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.1992.246810
P. Hall
Rotor dynamic instabilities have resulted in seldom understood and costly failures in high-performance rotating machinery. At times, machines test perfectly in the lab, yet fail under load and cannot achieve their designed capacities in the field. Nonlinear dynamics offers a way to help understand and possibly prevent these problems. Computed order tracking prepares experimental data for dynamical analysis by resampling data in real time to match a time-varying forcing function which in this instance is the rotational speed of the machine. A commercially available instrument can be used to view Poincare diagrams of a rotor test set as it begins to exhibit signs of instability.<>
{"title":"Measuring instabilities and chaos in the real world: applications in rotating machinery","authors":"P. Hall","doi":"10.1109/SSAP.1992.246810","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246810","url":null,"abstract":"Rotor dynamic instabilities have resulted in seldom understood and costly failures in high-performance rotating machinery. At times, machines test perfectly in the lab, yet fail under load and cannot achieve their designed capacities in the field. Nonlinear dynamics offers a way to help understand and possibly prevent these problems. Computed order tracking prepares experimental data for dynamical analysis by resampling data in real time to match a time-varying forcing function which in this instance is the rotational speed of the machine. A commercially available instrument can be used to view Poincare diagrams of a rotor test set as it begins to exhibit signs of instability.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"20 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":"124115101","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.1992.246859
L.D. Rankin, E. Kesler, T. Dyson
A novel approach to the detection and estimation of directional signals incident upon a linear array of sensors uses the diverse dimensions of the array polynomials associated with the statistically independent beams output by the adaptive Gram-Schmidt processor. Zero patterns generated by these polynomials provide the information necessary to detect the number of incident signals and estimate their angles of arrival. L/sup 2/ norms based on phase differences between zeros in observed pairs of beams are critical in the detection process. Monte-Carlo results compare the performance of the proposed method with that of the Root MUSIC algorithm for one signal and two closely spaced signals in white stationary noise. These results cover varying SNR, number of snapshots, and signal spacing for the two signals. Single trial results for multiple strong jammers are presented.<>
{"title":"A novel use of Gram-Schmidt for detection and estimation","authors":"L.D. Rankin, E. Kesler, T. Dyson","doi":"10.1109/SSAP.1992.246859","DOIUrl":"https://doi.org/10.1109/SSAP.1992.246859","url":null,"abstract":"A novel approach to the detection and estimation of directional signals incident upon a linear array of sensors uses the diverse dimensions of the array polynomials associated with the statistically independent beams output by the adaptive Gram-Schmidt processor. Zero patterns generated by these polynomials provide the information necessary to detect the number of incident signals and estimate their angles of arrival. L/sup 2/ norms based on phase differences between zeros in observed pairs of beams are critical in the detection process. Monte-Carlo results compare the performance of the proposed method with that of the Root MUSIC algorithm for one signal and two closely spaced signals in white stationary noise. These results cover varying SNR, number of snapshots, and signal spacing for the two signals. Single trial results for multiple strong jammers are presented.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"43 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":"131461002","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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