Pub Date : 1997-07-21DOI: 10.1109/HOST.1997.613493
B. Friedlander, L. Scharf
This paper explores the structure of time-frequency representations (TFR) in a discrete time setting. We define a proper TFR to be a function of the signal that has natural time- and frequency-shift properties. We then derive the basic structure of a proper TFR and argue for a quadratic TFR as the simplest form of a proper TFR.
{"title":"Time-frequency representations and their structure","authors":"B. Friedlander, L. Scharf","doi":"10.1109/HOST.1997.613493","DOIUrl":"https://doi.org/10.1109/HOST.1997.613493","url":null,"abstract":"This paper explores the structure of time-frequency representations (TFR) in a discrete time setting. We define a proper TFR to be a function of the signal that has natural time- and frequency-shift properties. We then derive the basic structure of a proper TFR and argue for a quadratic TFR as the simplest form of a proper TFR.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114664000","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 : 1997-07-21DOI: 10.1109/HOST.1997.613564
S. Bates, S. McLaughlin
Both the fractional Brownian motion (fBm) and the autoregressive integrated moving average (ARIMA) models have been applied to teletraffic scenarios in recent years. These models became popular after the discovery that Ethernet and VBR video data appear to possess the property of self-similarity. However the results presented in this paper suggest that Ethernet data is more impulsive than traffic generated by these models.
{"title":"Testing the Gaussian assumption for self-similar teletraffic models","authors":"S. Bates, S. McLaughlin","doi":"10.1109/HOST.1997.613564","DOIUrl":"https://doi.org/10.1109/HOST.1997.613564","url":null,"abstract":"Both the fractional Brownian motion (fBm) and the autoregressive integrated moving average (ARIMA) models have been applied to teletraffic scenarios in recent years. These models became popular after the discovery that Ethernet and VBR video data appear to possess the property of self-similarity. However the results presented in this paper suggest that Ethernet data is more impulsive than traffic generated by these models.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122859817","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 : 1997-07-21DOI: 10.1109/HOST.1997.613540
Sergio Cruces, L. Castedo
This paper addresses the blind separation of convolutive mixtures of independent and non-Gaussian sources. We present a block-based Gauss-Newton algorithm which is able to obtain a separation solution using only a specific set of output cross-cumulants and the hypothesis of soft mixtures. The order of the cross-cumulants is chosen to obtain a particular form of the Jacobian matrix that ensures convergence and reduces computational burden. The method can be seen as an extension and improvement of the Van-Gerven's symmetric adaptive decorrelation (SAD) method. Moreover the convergence analysis presented in the paper provides a theoretical background to derive an improved version of the Nguyen-Jutten (1995) algorithm.
{"title":"Blind separation of convolutive mixtures: a Gauss-Newton algorithm","authors":"Sergio Cruces, L. Castedo","doi":"10.1109/HOST.1997.613540","DOIUrl":"https://doi.org/10.1109/HOST.1997.613540","url":null,"abstract":"This paper addresses the blind separation of convolutive mixtures of independent and non-Gaussian sources. We present a block-based Gauss-Newton algorithm which is able to obtain a separation solution using only a specific set of output cross-cumulants and the hypothesis of soft mixtures. The order of the cross-cumulants is chosen to obtain a particular form of the Jacobian matrix that ensures convergence and reduces computational burden. The method can be seen as an extension and improvement of the Van-Gerven's symmetric adaptive decorrelation (SAD) method. Moreover the convergence analysis presented in the paper provides a theoretical background to derive an improved version of the Nguyen-Jutten (1995) algorithm.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123369811","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 : 1997-07-21DOI: 10.1109/HOST.1997.613508
A. Redfern, G.T. Zhou
The focus of this paper is on Volterra nonlinear system identification from input-output data. When the system is linear-quadratic and the input is Gaussian, closed-form expressions for the kernels were derived by Tick (1961) based on input-output cross-cumulants. However, there have been no known variance expressions for the kernel estimates. In this paper, we analyze the performance of the first- and second-order kernel estimates when the input is zero-mean white Gaussian, and the additive noise has unknown color and distribution. Closed-form variance expressions are presented and verified by simulations.
{"title":"Performance analysis of Volterra kernel estimators with Gaussian inputs","authors":"A. Redfern, G.T. Zhou","doi":"10.1109/HOST.1997.613508","DOIUrl":"https://doi.org/10.1109/HOST.1997.613508","url":null,"abstract":"The focus of this paper is on Volterra nonlinear system identification from input-output data. When the system is linear-quadratic and the input is Gaussian, closed-form expressions for the kernels were derived by Tick (1961) based on input-output cross-cumulants. However, there have been no known variance expressions for the kernel estimates. In this paper, we analyze the performance of the first- and second-order kernel estimates when the input is zero-mean white Gaussian, and the additive noise has unknown color and distribution. Closed-form variance expressions are presented and verified by simulations.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125425704","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 : 1997-07-21DOI: 10.1109/HOST.1997.613558
D. W. Griffith, J.G. Gonzalez, G. Arce
Characterizing signals jointly in the time and frequency domains through time-frequency representations (TFRs) such as the Wigner-Ville distribution (WVD) is a natural extension of Fourier analysis and gives a more complete representation of signal behavior particularly in the case of non-stationary signals. In the presence of additive impulsive noise, TFRs quickly break down and any information about the desired signal is lost. To combat these effects, we propose in this paper a family of memoryless nonlinearities which have been shown to produce a signal autocorrelation statistic which is well-behaved in the presence of stable noise. The result of this approach is a TFR which is both robust and simple to implement, and has many of the mathematical properties associated with the standard WVD. We illustrate the improvement in performance that can be obtained with several examples.
{"title":"Robust time-frequency representations for signals in /spl alpha/-stable noise using fractional lower-order statistics","authors":"D. W. Griffith, J.G. Gonzalez, G. Arce","doi":"10.1109/HOST.1997.613558","DOIUrl":"https://doi.org/10.1109/HOST.1997.613558","url":null,"abstract":"Characterizing signals jointly in the time and frequency domains through time-frequency representations (TFRs) such as the Wigner-Ville distribution (WVD) is a natural extension of Fourier analysis and gives a more complete representation of signal behavior particularly in the case of non-stationary signals. In the presence of additive impulsive noise, TFRs quickly break down and any information about the desired signal is lost. To combat these effects, we propose in this paper a family of memoryless nonlinearities which have been shown to produce a signal autocorrelation statistic which is well-behaved in the presence of stable noise. The result of this approach is a TFR which is both robust and simple to implement, and has many of the mathematical properties associated with the standard WVD. We illustrate the improvement in performance that can be obtained with several examples.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128785394","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 : 1997-07-21DOI: 10.1109/HOST.1997.613524
G. Scarano, G. Jacovitti
In this contribution, a generalization of the super exponential blind deconvolution method is discussed. The generalization consists in the definition of a "spikyness" criterion involving nonlinearities rather than only powers. This allows to rephrase Bussgang deconvolution in the framework of super exponential deconvolution using a spikyness criterion which takes into account the pdf of the input series to be recovered. Improved performance is expected when generalized super exponential deconvolution is tuned to suitable optimality criteria.
{"title":"On the optimality of Bussgang and super exponential blind deconvolution methods","authors":"G. Scarano, G. Jacovitti","doi":"10.1109/HOST.1997.613524","DOIUrl":"https://doi.org/10.1109/HOST.1997.613524","url":null,"abstract":"In this contribution, a generalization of the super exponential blind deconvolution method is discussed. The generalization consists in the definition of a \"spikyness\" criterion involving nonlinearities rather than only powers. This allows to rephrase Bussgang deconvolution in the framework of super exponential deconvolution using a spikyness criterion which takes into account the pdf of the input series to be recovered. Improved performance is expected when generalized super exponential deconvolution is tuned to suitable optimality criteria.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130178496","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 : 1997-07-21DOI: 10.1109/HOST.1997.613517
J. Solinsky
A number of fourth-order HOS approaches are reviewed for applicability to RF propagation in wideband application in urban environments. A lattice geometry is used to model constraints in the propagation which constrain the mean direction of arrival. Errors in estimation of this angle, and methods of increasing the signal level can potentially be mitigated using blind deconvolution with a distributed set of sources. With a moving receiver, a time average can also potentially improve these estimates. Data results shown some of these modeled propagation effects, which have dramatic angular changes with slight frequency shifts.
{"title":"Fourth-order HOS deconvolution in wideband communication applications with lattice geometric motion","authors":"J. Solinsky","doi":"10.1109/HOST.1997.613517","DOIUrl":"https://doi.org/10.1109/HOST.1997.613517","url":null,"abstract":"A number of fourth-order HOS approaches are reviewed for applicability to RF propagation in wideband application in urban environments. A lattice geometry is used to model constraints in the propagation which constrain the mean direction of arrival. Errors in estimation of this angle, and methods of increasing the signal level can potentially be mitigated using blind deconvolution with a distributed set of sources. With a moving receiver, a time average can also potentially improve these estimates. Data results shown some of these modeled propagation effects, which have dramatic angular changes with slight frequency shifts.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127796594","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 : 1997-07-21DOI: 10.1109/HOST.1997.613535
A. Govindaraju, Jitendra Tugnait
This paper is concerned with the problem of near-field source localization. The problem is tackled using the method of blind separation of independent signals (sources) from their linear instantaneous (memoryless) mixtures. The various signals are assumed to be zero-mean non-Gaussian but not necessarily linear or i.i.d. Approaches using higher-order cumulants are developed using the fourth-order normalized cumulants of the "beamformed" data. The instantaneous mixture matrix is obtained by cross-correlating the extracted inputs with the observed outputs. The first approach is source-extractive, i.e., the sources are extracted and cancelled one-by-one. The other approach is based upon cumulant matching of the estimated and model-based cumulants parametrized by the source parameters (range, bearing and cumulant). Illustrative simulation examples are provided.
{"title":"Near-field localization using inverse filter criteria-based blind separation and cumulant matching","authors":"A. Govindaraju, Jitendra Tugnait","doi":"10.1109/HOST.1997.613535","DOIUrl":"https://doi.org/10.1109/HOST.1997.613535","url":null,"abstract":"This paper is concerned with the problem of near-field source localization. The problem is tackled using the method of blind separation of independent signals (sources) from their linear instantaneous (memoryless) mixtures. The various signals are assumed to be zero-mean non-Gaussian but not necessarily linear or i.i.d. Approaches using higher-order cumulants are developed using the fourth-order normalized cumulants of the \"beamformed\" data. The instantaneous mixture matrix is obtained by cross-correlating the extracted inputs with the observed outputs. The first approach is source-extractive, i.e., the sources are extracted and cancelled one-by-one. The other approach is based upon cumulant matching of the estimated and model-based cumulants parametrized by the source parameters (range, bearing and cumulant). Illustrative simulation examples are provided.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124488564","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 : 1997-07-21DOI: 10.1109/HOST.1997.613491
J. C. K. Yan, D. Hatzinakos
In this paper, we propose a new noise filtering scheme that is based on higher-order statistics (HOS) for photographic images corrupted by signal-dependent film grain noise. In addition, reliable estimation of the noise parameter using HOS is proposed. This parameter estimation technique can be used to generate film grain noise which has applications in motion picture and television productions. Simulation results show that the proposed filter perform better than existing methods which are based on second-order statistics.
{"title":"Signal-dependent film grain noise removal and generation based on higher-order statistics","authors":"J. C. K. Yan, D. Hatzinakos","doi":"10.1109/HOST.1997.613491","DOIUrl":"https://doi.org/10.1109/HOST.1997.613491","url":null,"abstract":"In this paper, we propose a new noise filtering scheme that is based on higher-order statistics (HOS) for photographic images corrupted by signal-dependent film grain noise. In addition, reliable estimation of the noise parameter using HOS is proposed. This parameter estimation technique can be used to generate film grain noise which has applications in motion picture and television productions. Simulation results show that the proposed filter perform better than existing methods which are based on second-order statistics.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121713491","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 : 1997-07-21DOI: 10.1109/HOST.1997.613546
S. Bourennane, M. Frikel, A. Bendjama
In this paper we develop an algorithm to improve the accuracy of the estimation of the direction of arrival of the wide-band sources. It is well known that when the noise cross-spectral matrix is unknown, these estimates may be grossly inaccurate. Using both the fourth order cumulant for suppression of the Gaussian noise, the transformation matrices for estimating the coherent signal subspace and a noneigenvector algorithm a robust method for the source characterisation problem in the presence of noise with an unknown cross-spectral matrix is developed. We show that the performance of bearing estimation algorithms improves substantially when our robust algorithm is used. Simulation results are presented for the unknown noise spectral matrix.
{"title":"Fast wideband source separation based on higher-order statistics","authors":"S. Bourennane, M. Frikel, A. Bendjama","doi":"10.1109/HOST.1997.613546","DOIUrl":"https://doi.org/10.1109/HOST.1997.613546","url":null,"abstract":"In this paper we develop an algorithm to improve the accuracy of the estimation of the direction of arrival of the wide-band sources. It is well known that when the noise cross-spectral matrix is unknown, these estimates may be grossly inaccurate. Using both the fourth order cumulant for suppression of the Gaussian noise, the transformation matrices for estimating the coherent signal subspace and a noneigenvector algorithm a robust method for the source characterisation problem in the presence of noise with an unknown cross-spectral matrix is developed. We show that the performance of bearing estimation algorithms improves substantially when our robust algorithm is used. Simulation results are presented for the unknown noise spectral matrix.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130364841","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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