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Robust least mean square adaptive FIR filter algorithm

IEE PROCEEDINGS-VISION IMAGE AND signal processing 2001年第5期148卷 332-336页

作者： Banjac, Z Kovacevic, B Veinovic, M Milosavljevic, M Inst Appl Math & Elect YU-11000 Belgrade Yugoslavia Univ Belgrade Fac Elect Engn YU-11000 Belgrade Yugoslavia

The authors propose a new robust adaptive FIR filter algorithm for system identification applications based on a statistical approach named the M estimation. The proposed robust least mean square algorithm differs from the conventional one by the insertion of a suitably chosen nonlinear transformation of the prediction residuals. The effect of nonlinearity is to assign less weight to a small portion of large residuals so that the impulsive noise in the desired filter response will not greatly influence the final parameter estimates. The convergence of the parameter estimates is established theoretically using the ordinary differential equation approach. The feasibility of the approach is demonstrated with simulations.

关键词： prediction theory adaptive filters least mean squares methods differential equations Digital signal processing Simulation, modelling and identification robust least mean square algorithm impulsive noise M estimation parameter estimation prediction residuals convergence impulse noise LMS algorithm FIR filters filtering methods in signal processing ordinary differential equation approach adaptive filter algorithm Interpolation and function approximation (numerical analysis) nonlinear transformation Other topics in statistics filtering theory Differential equations (numerical analysis) convergence of numerical methods statistical approach FIR filter algorithm system identification applications nonlinearity effect

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Iterative signal-order and noise estimation for multivariate data

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ELECTRONICS LETTERS 2001年第2期37卷 134-135页

作者： Lee, J Staelin, DH MIT Elect Res Lab Cambridge MA 02139 USA

The iterative order and noise (ION) estimation algorithm estimates the signal order of multivariate data, e.g. using a scree plot [1], and the unknown noise variances, e.g. using the expectation-maximisation (EM) algo... 详细信息

关键词： filtering theory multivariate data scree plot filtering methods in signal processing iterative methods iterative signal-order estimation linear regression principal component analysis principal component analyses unknown noise variances Wiener filtering signal processing theory Interpolation and function approximation (numerical analysis) expectation-maximisation algorithm Wiener filters noise estimation signal resolution Other topics in statistics

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Maximum likelihood approach to the detection of changes between multitemporal SAR images

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IEE PROCEEDINGS-RADAR SONAR AND NAVIGATION 2001年第4期148卷 200-210页

作者： Lombardo, P Oliver, CJ Univ Roma La Sapienza Dept INFOCOM I-00184 Rome Italy DERA Malvern WR14 3PS Worcs England

The authors introduce maximum likelihood techniques for optimised discrimination between agricultural and wooded regions, based on a multitemporal sequence of ERS images. The inherent resolution of the system is inadequate to make such a classification on an individual image. However. the different temporal change patterns of the two classes can be exploited. One approach uses joint annealed segmentation of the image sequence. providing optimised exploitation of the speckle model in determining the common set of region boundaries in the underlying radar cross-section. This is followed by maximum likelihood change detection using a normalised log temporal texture measure. This is shown to be superior to constructing the normalised log measure directly, including speckle fluctuations, followed by a single annealed segmentation process. Finally, it is demonstrated how simple filtering of this normalised log measure can provide reasonable classification with greatly reduced computation load.

关键词： image sequence optimisation forestry agriculture image classification Radar theory maximum likelihood estimation joint annealed segmentation Agriculture Optimisation techniques ERS images vegetation mapping image segmentation radar cross-section remote sensing by radar optimised exploitation filtering methods in signal processing normalised log temporal texture measure multitemporal sequence Radar equipment, systems and applications Other topics in Earth sciences speckle maximum likelihood approach temporal change patterns region boundaries filtering Image recognition radar imaging Other topics in statistics synthetic aperture radar wooded region classification agricultural region optimised discrimination radar cross-sections median filters image sequences speckle model maximum likelihood change detection multitemporal SAR images

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Bounds on matched filter performance in doubly dispersive Gaussian WSSUS channels

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ELECTRONICS LETTERS 2001年第6期37卷 383-384页

作者： Hunziker, T Dahlhaus, D ETH Zentrum Swiss Fed Inst Technol Commun Technol Lab CH-8092 Zurich Switzerland

The matched filter bound (MFB) is derived for doubly dispersive Gaussian WSSUS channels. Bq means of the Karhunen-Loeve expansion. both monotonically increasing and monotonically decreasing sequences of error probabilities converging towards thle MFB are presented in closed form as a function of the pulse shapes and the scattering function.

关键词： pulse shapes Karhunen-Loeve expansion scattering function filtering theory Integral transforms in numerical analysis MFB filtering methods in signal processing monotonically decreasing sequences Gaussian channels matched filter performance monotonically increasing sequences error statistics doubly dispersive Gaussian WSSUS channels matched filters Karhunen-Loeve transforms signal processing theory closed form error probabilities

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Blind equalisation using approximate maximum likelihood source separation

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ELECTRONICS LETTERS 2001年第1期37卷 61-62页

作者： Choi, S Cichocki, A Chungbuk Natl Univ Dept Elect Engn Cheongju 361763 Chungbuk South Korea RIKEN Brain Sci Inst Lab Adv Brain Signal Proc Wako Saitama 3510198 Japan

Blind equalisation of single input multiple output FIR channels can be reformulated as the problem of blind source separation. It has been shown that the natural gradient-based source separation method could recover the source successfully for ill-conditioned channels since it has the equivariant property. However, the effect of additive noise was not considered. A new approximate maximum likelihood source separation (AMLSS) method using the natural gradient is developed and applied to the task of blind equalisation. It is shown that the proposed method outperforms the BSBE in the presence of Gaussian noise.

关键词： maximum likelihood estimation Communication channel equalisation and identification blind source separation ill-conditioned channels filtering methods in signal processing white noise FIR filters single input multiple output FIR channels natural gradient blind equalisers blind equalisation approximate maximum likelihood source separation equivariant property Gaussian noise additive noise Other topics in statistics

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Adaptive SIR estimation in DS-CDMA cellular systems using Kalman filtering

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ELECTRONICS LETTERS 2001年第5期37卷 315-317页

作者： Freris, N Jeans, TG Taaghol, P Univ Surrey Ctr Commun Syst Res Guildford GU2 5XH Surrey England

A novel adaptive signal-to-interference ratio (SIR) estimation algorithm for a DS-CDMA cellular system is proposed. The algorithm uses Kalman filtering, the coefficients of a which are varied according to the mobile station speed. Simulations show significantly improved performance over the conventional algorithm found in the literature.

关键词： Mobile radio systems adaptive Kalman filters Protocols adaptive SIR estimation Kalman filtering filtering methods in signal processing DS-CDMA cellular systems cellular radio adaptive signal-to-interference ratio estimation algorithm code division multiple access mobile station speed Multiple access communication

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RNS quasi-chaotic generator for self-correcting secure communication

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ELECTRONICS LETTERS 2001年第5期37卷 325-327页

作者： Panella, M Martinelli, G Univ Rome La Sapienza INFOCOM Dept I-00184 Rome Italy

A novel quasi-chaotic generator is proposed. Its architecture is tailored to a particular secure communication system, which is structured for self-correcting transmission errors. It is composed of a suitable set of R... 详细信息

关键词： RNS digital filter Cryptography residue number systems self-correcting transmission error Chaotic behaviour in circuits chaos generators filtering methods in signal processing quasi-chaotic generator Codes Digital filters digital filters secure communication system telecommunication security error correction

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Speech quality and recognition rate improvement in care noise environments

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ELECTRONICS LETTERS 2001年第12期37卷 800-802页

作者： Jeong, S Hahn, M Informat & Commun Univ Sch Engn Yusung Ku Taejon 305732 South Korea

In car noise environments, speech quality is severely degraded and speech recognition systems fail to produce high recognition rates. Highpass filtering and Kalman filtering with a whitening process are applied to car... 详细信息

关键词： whitening process speech recognition rate improvement speech recognition signal processing theory speech enhancement filtering theory highpass filtering acoustic noise Kalman filters Kalman filtering speech intelligibility speech quality degradation speech quality improvement automobiles Speech processing techniques car noise corrupted speech signals Speech recognition filtering methods in signal processing car noise environments Speech recognition and synthesis

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signal reconstruction technique allowing exclusion of antialiasing filter

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ELECTRONICS LETTERS 2001年第3期37卷 199-200页

作者： Poberezhskiy, Y Poberezhskiy, G Private Address Los Angeles USA Private Address San Marcos USA

It is shown that an analogue signal can be reconstructed without the employment of an antialiasing filter when the signal samples are multiplied by properly selected weight functions and the products are summed. This ... 详细信息

关键词： signal sampling uniform sampling theorem filtering theory digital transceivers filtering methods in signal processing real valued bandpass signals signal sample multiplication signal reconstruction transceivers analogue signal reconstruction weight functions antialiasing filter Radio links and equipment

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Gain fusion algorithm for decentralised parallel Kalman filters

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IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS 2000年第1期147卷 97-103页

作者： Paik, BS Oh, JH Korea Adv Inst Sci & Technol Dept Mech Engn Yusong Gu Taejon 305701 South Korea

A new gain fusion algorithm is proposed for application to decentralised sensor systems. The proposed algorithm gives computer-efficient suboptimal estimation results, such that it reconstructs the global estimate and covariance from local Kalman filter gains and estimates without significant loss of accuracy. Compared to the conventional algorithm, the smaller communication requirement and the removal of the calculation requirement of inverse covariances make the proposed algorithm more suitable for real time applications. A numerical example shows that the proposed algorithm provides a convincing suboptimal decentralised algorithm. In addition, the proposed gain fusion algorithm can be easily extended to accommodate local Kalman filters with reduced order.

关键词： Kalman filters filtering theory reduced order filter suboptimal decentralised algorithm multisensor systems Kalman filter decentralised sensor systems parallel algorithms gain fusion algorithm Sensor fusion filtering methods in signal processing Parallel programming and algorithm theory signal processing theory inverse covariances sensor fusion

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