B. Karlsen, Jan Larsen, Helge B. D. Sørensen, K. Jakobsen
This paper presents statistical signal processing approaches for clutter reduction in stepped-frequency ground penetrating radar (SF-GPR) data. In particular, we suggest clutter/signal separation techniques based on principal and independent component analysis (PCA/ICA). The approaches are successfully evaluated and compared on a real SF-GPR time-series. Field-test data are acquired using a monostatic S-band rectangular waveguide antenna.
{"title":"Comparison of PCA and ICA based clutter reduction in GPR systems for anti-personal landmine detection","authors":"B. Karlsen, Jan Larsen, Helge B. D. Sørensen, K. Jakobsen","doi":"10.1109/SSP.2001.955243","DOIUrl":"https://doi.org/10.1109/SSP.2001.955243","url":null,"abstract":"This paper presents statistical signal processing approaches for clutter reduction in stepped-frequency ground penetrating radar (SF-GPR) data. In particular, we suggest clutter/signal separation techniques based on principal and independent component analysis (PCA/ICA). The approaches are successfully evaluated and compared on a real SF-GPR time-series. Field-test data are acquired using a monostatic S-band rectangular waveguide antenna.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"33 1","pages":"146-149"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86292727","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}
This contribution deals with a particular family of blind system identification techniques, referred to as minimum noise subspace (MNS) method. The MNS method is a computationally fast version of the subspace method. We develop an orthogonal version of MNS method. The orthogonal minimum subspace (OMNS) method is more efficient in computation than a standard subspace method, and is more robust to channel noise than MNS.
{"title":"Orthogonal minimum noise subspace for multiple-input multiple-output system identification","authors":"A. Safavi, K. Abed-Meraim","doi":"10.1109/SSP.2001.955278","DOIUrl":"https://doi.org/10.1109/SSP.2001.955278","url":null,"abstract":"This contribution deals with a particular family of blind system identification techniques, referred to as minimum noise subspace (MNS) method. The MNS method is a computationally fast version of the subspace method. We develop an orthogonal version of MNS method. The orthogonal minimum subspace (OMNS) method is more efficient in computation than a standard subspace method, and is more robust to channel noise than MNS.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"78 1","pages":"285-288"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85490203","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}
This paper introduces the irregular sampling problem associated with motion transformations embedded in image sequences. Moving patterns in image sequences undergo a sampling which is a function of the relative position of the object and the sampling grid. To solve this problem, it is effective to consider motion as a smooth invertible time-warping transformation. Important applications are related to this topic. Let us mention the focalization on selected moving areas characterized by a specific scale and a specific kinematic. Focalization and selective reconstruction can be performed either for analysis with interpolation, prediction, and de-noising or for coding with transmission of limited areas of interest. The Shannon sampling theorem and its generalizations as Kramer and Parzen theorems apply in this context with Clark's theorem. Clark's theorem shows that signals formed by warping band-limited signals admit formulae for reconstruction from samples. Furthermore, the warping operators that lift the pattern up to a trajectory are chosen as unitary irreducible and square-integrable group representations. These operators bring important tools to motion-selective analysis and reconstruction, namely continuous wavelets, frames, discrete wavelet transforms, and reproducing kernel subspaces. Two examples are treated with motion at constant translational velocity and angular velocity. It is shown that the analysis and reconstruction structures directly derived from motion-based groups are equivalent to warping the same structures from the usual affine multidimensional group defined for space-time transformations.
{"title":"Irregular sampling problems and selective reconstructions associated with motion transformations","authors":"J. Leduc","doi":"10.1109/SSP.2001.955328","DOIUrl":"https://doi.org/10.1109/SSP.2001.955328","url":null,"abstract":"This paper introduces the irregular sampling problem associated with motion transformations embedded in image sequences. Moving patterns in image sequences undergo a sampling which is a function of the relative position of the object and the sampling grid. To solve this problem, it is effective to consider motion as a smooth invertible time-warping transformation. Important applications are related to this topic. Let us mention the focalization on selected moving areas characterized by a specific scale and a specific kinematic. Focalization and selective reconstruction can be performed either for analysis with interpolation, prediction, and de-noising or for coding with transmission of limited areas of interest. The Shannon sampling theorem and its generalizations as Kramer and Parzen theorems apply in this context with Clark's theorem. Clark's theorem shows that signals formed by warping band-limited signals admit formulae for reconstruction from samples. Furthermore, the warping operators that lift the pattern up to a trajectory are chosen as unitary irreducible and square-integrable group representations. These operators bring important tools to motion-selective analysis and reconstruction, namely continuous wavelets, frames, discrete wavelet transforms, and reproducing kernel subspaces. Two examples are treated with motion at constant translational velocity and angular velocity. It is shown that the analysis and reconstruction structures directly derived from motion-based groups are equivalent to warping the same structures from the usual affine multidimensional group defined for space-time transformations.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"45 1","pages":"484-487"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82212641","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}
This paper introduces a novel semiblind approach to space-time linear detection in multiple-access systems. A new criterion for the selection of the linear receiver coefficients, based on the maximum likelihood (ML) principle, is derived and a practical implementation by means of a fast expectation-maximization (EM) algorithm is suggested. The semiblind criterion is obtained from a purely statistical point of view where the aim of training data is not to enhance performance but to eliminate misconvergence problems.
{"title":"An application of the maximum likelihood principle to semiblind space-time linear detection in multiple-access wireless communications","authors":"M. Bugallo, J. Míguez, L. Castedo","doi":"10.1109/SSP.2001.955256","DOIUrl":"https://doi.org/10.1109/SSP.2001.955256","url":null,"abstract":"This paper introduces a novel semiblind approach to space-time linear detection in multiple-access systems. A new criterion for the selection of the linear receiver coefficients, based on the maximum likelihood (ML) principle, is derived and a practical implementation by means of a fast expectation-maximization (EM) algorithm is suggested. The semiblind criterion is obtained from a purely statistical point of view where the aim of training data is not to enhance performance but to eliminate misconvergence problems.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"41 1","pages":"198-201"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85149868","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}
This paper proposes a method to detect infrared land mine signatures embedded in rotationally invariant colored noise. A common problem in statistical image processing is high dimensionality. This causes a need for large sets of training data. To overcome this, an alternative formulation of the generalized likelihood ratio test (GLRT) is presented. This formulation makes it possible to utilize the circular-symmetry, rendering a substantial decrease in model dimensionality and consequently, in the amount of training data needed. Simulations indicate that a significant gain in performance can be achieved compared to both the non-parameterized detector and the matched filter.
{"title":"Land mine detection in rotationally invariant noise fields","authors":"L. Svensson, M. Lundberg","doi":"10.1109/SSP.2001.955249","DOIUrl":"https://doi.org/10.1109/SSP.2001.955249","url":null,"abstract":"This paper proposes a method to detect infrared land mine signatures embedded in rotationally invariant colored noise. A common problem in statistical image processing is high dimensionality. This causes a need for large sets of training data. To overcome this, an alternative formulation of the generalized likelihood ratio test (GLRT) is presented. This formulation makes it possible to utilize the circular-symmetry, rendering a substantial decrease in model dimensionality and consequently, in the amount of training data needed. Simulations indicate that a significant gain in performance can be achieved compared to both the non-parameterized detector and the matched filter.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"17 1","pages":"170-173"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77856378","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}
Summary form only given, as follows. Detection algorithms, whose design takes into account prior knowledge about the signals and the channel, face a quandary: they provide marked improvement in performance when the field operating conditions match well this available knowledge; but they experience strong degradation when the actual conditions depart from the assumed ones. In other words, high resolution and robustness are commonly at odds. A third important variable affecting this tradeoff is the computational complexity of the solution. A geometric based approach to designing detectors leads to a satisfying compromise: simple to implement detectors that are robust to mismatches and that exhibit good performance. The approach designs a representation subspace that is a good approximation (in the gap metric sense) to the signal set (a priori information), and uses multiresolution and wavelet analysis to design the representation subspace and implement the detector. The approach can be applied to multipath channels, and detection results illustrate the robustness of the geometric gap detector.
{"title":"A geometric and multiresolution analysis approach to robust detection","authors":"José M. F. Moura","doi":"10.1109/SSP.2001.955206","DOIUrl":"https://doi.org/10.1109/SSP.2001.955206","url":null,"abstract":"Summary form only given, as follows. Detection algorithms, whose design takes into account prior knowledge about the signals and the channel, face a quandary: they provide marked improvement in performance when the field operating conditions match well this available knowledge; but they experience strong degradation when the actual conditions depart from the assumed ones. In other words, high resolution and robustness are commonly at odds. A third important variable affecting this tradeoff is the computational complexity of the solution. A geometric based approach to designing detectors leads to a satisfying compromise: simple to implement detectors that are robust to mismatches and that exhibit good performance. The approach designs a representation subspace that is a good approximation (in the gap metric sense) to the signal set (a priori information), and uses multiresolution and wavelet analysis to design the representation subspace and implement the detector. The approach can be applied to multipath channels, and detection results illustrate the robustness of the geometric gap detector.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"48 1","pages":"2-"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76013049","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}
This paper addresses the problem of building appropriate statistical models of the way the Internet appears from the point of view of congestion, to a transmission control protocol (TCP) sender. TCP is a mechanism for implementing full duplex, acknowledged, end-to-end transmission over an Internet protocol (IP) network. This work has been motivated by the TCP variant, the so-called Vegas implementation. TCP Vegas is really the first implementation to be based loosely on system theoretic ideas in the sense that it measures the segment round-trip times across the network to adjust its transmission rate. This paper develops a new linear system framework for TCP, and applies recursive prediction error identification techniques to specify statistical models which may be used to develop alternative control strategies. Network simulations are used to illustrate behaviour.
{"title":"Internet transport layer system identification","authors":"L. White","doi":"10.1109/SSP.2001.955294","DOIUrl":"https://doi.org/10.1109/SSP.2001.955294","url":null,"abstract":"This paper addresses the problem of building appropriate statistical models of the way the Internet appears from the point of view of congestion, to a transmission control protocol (TCP) sender. TCP is a mechanism for implementing full duplex, acknowledged, end-to-end transmission over an Internet protocol (IP) network. This work has been motivated by the TCP variant, the so-called Vegas implementation. TCP Vegas is really the first implementation to be based loosely on system theoretic ideas in the sense that it measures the segment round-trip times across the network to adjust its transmission rate. This paper develops a new linear system framework for TCP, and applies recursive prediction error identification techniques to specify statistical models which may be used to develop alternative control strategies. Network simulations are used to illustrate behaviour.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"40 1","pages":"349-352"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76181316","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}
This paper addresses optimal estimation for time-varying autoregressive (TVAR) models. First, we propose a statistical model on the time evolution of the frequencies, moduli and real poles instead of a standard model on the AR coefficients, as it makes more sense from a physical viewpoint. Second, optimal estimation involves solving a complex optimal filtering problem which does not admit any closed-form solution. We propose a new particle filtering scheme which is an improvement over the so-called auxiliary particle filter. The hyperparameters timing the evolution of the model parameters are also estimated on-line to make the model robust. Simulations demonstrate the efficiency of both our model and algorithm.
{"title":"Improved auxiliary particle filtering: applications to time-varying spectral analysis","authors":"C. Andrieu, M. Davy, A. Doucet","doi":"10.1109/SSP.2001.955284","DOIUrl":"https://doi.org/10.1109/SSP.2001.955284","url":null,"abstract":"This paper addresses optimal estimation for time-varying autoregressive (TVAR) models. First, we propose a statistical model on the time evolution of the frequencies, moduli and real poles instead of a standard model on the AR coefficients, as it makes more sense from a physical viewpoint. Second, optimal estimation involves solving a complex optimal filtering problem which does not admit any closed-form solution. We propose a new particle filtering scheme which is an improvement over the so-called auxiliary particle filter. The hyperparameters timing the evolution of the model parameters are also estimated on-line to make the model robust. Simulations demonstrate the efficiency of both our model and algorithm.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"111 1","pages":"309-312"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77478479","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}
Space-time adaptive processing (STAP) has emerged as a key technology for improving the performance of radar systems required to operate in the presence of severe and dynamic interference which generally includes clutter as well as jamming. While the theory of optimum STAP is well known, practical issues, such as interference heterogeneity, finite sample support, mismatched signal models and computational load, need to be overcome when it comes to implementing STAP in operational radar systems. This paper proposes an advanced STAP formulation which addresses important issues facing practical implementation and then tailors this general formulation for the case of interference rejection in over-the-horizon (OTH) radar to evaluate experimentally its target detection and localisation performance.
{"title":"An advanced STAP implementation for surveillance radar systems","authors":"G. Fabrizio, M. Turley","doi":"10.1109/SSP.2001.955240","DOIUrl":"https://doi.org/10.1109/SSP.2001.955240","url":null,"abstract":"Space-time adaptive processing (STAP) has emerged as a key technology for improving the performance of radar systems required to operate in the presence of severe and dynamic interference which generally includes clutter as well as jamming. While the theory of optimum STAP is well known, practical issues, such as interference heterogeneity, finite sample support, mismatched signal models and computational load, need to be overcome when it comes to implementing STAP in operational radar systems. This paper proposes an advanced STAP formulation which addresses important issues facing practical implementation and then tailors this general formulation for the case of interference rejection in over-the-horizon (OTH) radar to evaluate experimentally its target detection and localisation performance.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"10 1","pages":"134-137"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76924030","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}
The paper introduces a robust approach to subspace based blind channel identification. The technique is based on estimating the noise subspace from the sample sign covariance matrix. The theoretical motivation for the technique is shown under the white Gaussian noise assumption. A simulation study is performed to demonstrate the robust performance of the algorithm both in Gaussian and non-Gaussian noise. The results indicate that when the noise is Gaussian, the proposed method has similar good performance as the standard subspace method. When the noise is heavy-tailed, the proposed method outperforms the conventional subspace technique.
{"title":"Blind channel identification using robust subspace estimation","authors":"S. Visuri, H. Oja, V. Koivunen","doi":"10.1109/SSP.2001.955277","DOIUrl":"https://doi.org/10.1109/SSP.2001.955277","url":null,"abstract":"The paper introduces a robust approach to subspace based blind channel identification. The technique is based on estimating the noise subspace from the sample sign covariance matrix. The theoretical motivation for the technique is shown under the white Gaussian noise assumption. A simulation study is performed to demonstrate the robust performance of the algorithm both in Gaussian and non-Gaussian noise. The results indicate that when the noise is Gaussian, the proposed method has similar good performance as the standard subspace method. When the noise is heavy-tailed, the proposed method outperforms the conventional subspace technique.","PeriodicalId":70952,"journal":{"name":"信号处理","volume":"49 1","pages":"281-284"},"PeriodicalIF":0.0,"publicationDate":"2001-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74830230","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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