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Statistical interpretation of non-local means

IET COMPUTER VISION 2010年第3期4卷 162-172页

作者： Thacker, N. A. Manjon, J. V. Bromiley, P. A. Univ Manchester Sch Canc & Imaging Sci Manchester Lancs England Univ Politecn Valencia ITACA E-46071 Valencia Spain

Noise filtering is a common step in image processing, and is particularly effective in improving the subjective quality of images. A large number of techniques have been developed, many of which concentrate on the problem of removing noise without damaging small structures such as edges. One recent approach that demonstrates empirical merit is the non-local means (NLM) algorithm. However, in order to use noise filtering algorithms in quantitative or clinical image analysis tasks an understanding of their behaviour that goes beyond subjective appearance must be developed. The purpose of this study is to investigate the statistical basis of NLM in order to attempt to understand the conditions required for its use. The theory is illustrated on synthetic data and clinical magnetic resonance images of the brain.

关键词： image processing filtering theory Other topics in statistics clinical magnetic resonance images Biology and medical computing statistical analysis Biomedical magnetic resonance imaging and spectroscopy noise filtering algorithms image denoising Medical magnetic resonance imaging and spectroscopy Patient diagnostic methods and instrumentation brain statistical interpretation Optical, image and video signal processing Computer vision and image processing techniques filtering methods in signal processing Probability theory, stochastic processes, and statistics clinical image analysis tasks nonlocal means biomedical MRI

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Discrimination of exo-atmospheric active decoys using acceleration information

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IET RADAR SONAR AND NAVIGATION 2010年第4期4卷 626-638页

作者： Rao, B. Zhao, Y. -L. Xiao, S. -P. Wang, X. -S. Natl Univ Def Technol Sch Elect Sci & Engn Changsha 410073 Hunan Peoples R China Unit 63880 Luoyang 471003 Peoples R China

Range deception is a common electronic countermeasure technique used for ballistic missile penetration. The well-designed decoys of range deception can even form stable tracks. Discrimination of these decoys is difficult and one potential way is at radar data processing level by using motion features. This study presents a novel method, called acceleration matched discrimination algorithm, which fully utilises the fact that the accelerations of exo-atmospheric active decoys are essentially different from that of physical targets (e. g. warhead), that can discriminate these decoys at the radar data processing level. First, the acceleration model of active decoys is explicitly derived. Secondly, the acceleration matched coefficient (AMC) is defined based on the filtered acceleration and theoretical acceleration. By employing the extended Kalman filter, the instantaneous variance of AMC is also derived. Finally, the discrimination algorithm is designed based on a batch-processing weighted least squares estimate and its estimated variance. Theoretical analysis and simulations indicate that the discrimination method is valid and feasible. Furthermore, the discrimination performance analysis due to the influence of radar position, radar measurement error and data rate are also covered.

关键词： theoretical acceleration radar imaging image motion analysis Electronic warfare missiles motion features Optical, image and video signal processing ballistic missile penetration well-designed decoys Radar equipment, systems and applications range deception radar data processing level radar position acceleration matched discrimination algorithm filtering methods in signal processing military radar least squares approximations ballistics electronic countermeasure technique Kalman filters electronic countermeasures extended Kalman filter radar measurement error acceleration information batch-processing weighted least square estimate acceleration matched coefficient Interpolation and function approximation (numerical analysis) Military radar, detection and tracking systems exoatmospheric active decoys filtered acceleration active decoy acceleration model

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Neural network method for direction of arrival estimation with uniform cylindrical microstrip patch array

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IET MICROWAVES ANTENNAS & PROPAGATION 2010年第2期4卷 153-161页

作者： Caylar, S. Dural, G. Leblebicioglu, K. Middle E Tech Univ Dept Elect & Elect Engn TR-06531 Ankara Turkey 3rd ASMC Turkish Air Force TR-06790 Ankara Turkey

In this study, a new neural network algorithm is proposed for real-time multiple source tracking problem with cylindrical patch antenna array based on a previously reported Modified Neural Multiple Source Tracking (MN-MUST) algorithm. The proposed algorithm, namely cylindrical microstrip patch array modified neural multiple source tracking (CMN-MUST) algorithm implements MN-MUST algorithm on a cylindrical microstrip patch array structure. CMN-MUST algorithm uses the advantage of directive pattern of microstrip patch elements by considering only a part of array elements for a chosen sector. This reduces neural network sizes and also improves the spatial filtering performance. The proposed algorithm improves MN-MUST algorithm in the sense of accuracy and speed while covering the full azimuth range.

关键词： real-time systems direction of arrival estimation Communications computing microstrip antenna arrays Neural computing techniques Antenna theory modified neural multiple source tracking algorithm spatial filtering performance filtering methods in signal processing array elements neural network algorithm real-time multiple source tracking problem telecommunication computing neural nets neural network method microstrip patch elements Antenna arrays directive pattern uniform cylindrical microstrip patch array antenna radiation patterns filtering theory direction-of-arrival estimation cylindrical patch antenna array

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Broadband microstrip bandpass filters using triple-mode resonator

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IET MICROWAVES ANTENNAS & PROPAGATION 2010年第9期4卷 1275-1282页

作者： Tu, W. -H. Natl Cent Univ Dept Elect Engn Tao Yuan 32001 Taiwan

A broadband microstrip bandpass filter using a stub-loaded resonator is proposed. The basic block of the proposed filter is a triple-mode resonator that consists of a half-wave resonator and a shunt open-circuited stub. A parameter study of how to control the frequencies of the three modes is presented. Two bandpass filters using the proposed resonator have been designed, fabricated and tested to validate the design concept. Design guidelines and a comparison with previous work are also included. For the first bandpass filter, from 1.75 to 3.25 GHz, the measured return loss is greater than 16 dB and the measured insertion loss is less than 0.9 dB. The maximum group delay variation is 0.6 ns. Furthermore, there are two transmission zeros at 1.3 and 3.8 GHz for a better cut-off response. For the second one, from 1.6 to 3.7 GHz, the measured return loss is greater than 14 dB and the measured insertion loss is less than 1 dB. The maximum group delay variation is 0.6 ns, and there are two transmission zeros at 1.36 and 4.03 GHz.

关键词： broadband microstrip bandpass filters stub-loaded resonator filtering methods in signal processing Telecommunication applications broadband networks resonator filters frequency 1.75 GHz to 3.25 GHz triple-mode resonator microstrip filters group delay variation band-pass filters basic block

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Frequency synthesis using digital-to-frequency conversion and filtering

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ELECTRONICS LETTERS 2010年第14期46卷 979-U38页

作者： Aouini, S. Roberts, G. W. McGill Univ Integrated Microsyst Lab Montreal PQ H3A 2A7 Canada

A novel topology for frequency synthesis using digital-to-frequency conversion and a filtering technique is presented. The implementation uses a high-order digital sigma-delta modulator to encode a DC value, which is then mapped to a corresponding frequency using a digital-to-frequency conversion algorithm. The output bit-sequence with the sigma-delta encoded frequency is then applied to a high-order phase-locked loop (PLL) behaving as a filter operating on the incoming instantaneous frequency. The theory of the proposed scheme is described and validated with simulation and experiment. A prototype board-level implementation with a sixth-order PLL was constructed and frequencies ranging from 30.5 to 44.5 MHz were experimentally generated with a 25 kHz resolution from a single 100 MHz master clock.

关键词： Digital signal processing high-order digital sigma-delta modulator high-order phase-locked loop filtering theory digital-to-frequency conversion network topology A/D and D/A convertors frequency 30.5 MHz to 44.5 MHz frequency 25 kHz Modulators, demodulators, discriminators and mixers frequency 100 MHz frequency synthesis prototype board-level implementation signal generators filtering technique phase locked loops sixth-order PLL filtering methods in signal processing frequency synthesizers master clock sigma-delta encoded frequency sigma-delta modulation

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Multi-target tracking in clutter with sequential Monte Carlo methods

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IET RADAR SONAR AND NAVIGATION 2010年第5期4卷 662-672页

作者： Liu, B. Ji, C. Zhang, Y. Hao, C. Wong, K. -K. Duke Univ Dept Stat Sci Durham NC 27708 USA SAMSI Res Triangle Pk NC 27709 USA Univ London Univ Coll Dept Elect & Elect Engn London WC1E 7JE England Chinese Acad Sci Inst Acoust Beijing 100190 Peoples R China

For multi-target tracking (MTT) in the presence of clutters, both issues of state estimation and data association are crucial. This study tackles them jointly by Sequential Monte Carlo methods, a.k.a. particle filters. A number of novel particle algorithms are devised. The first one, which we term Monte-Carlo data association (MCDA), is a direct extension of the classical sequential importance resampling (SIR) algorithm. The second one is called maximum predictive particle filter (MPPF), in which the measurement combination with the maximum predictive likelihood is used to update the estimate of the multi-target's posterior. The third, called proportionally weighting particle filter (PWPF), weights all feasible measurement combinations according to their predictive likelihoods, and uses them proportionally in the importance sampling framework. We demonstrate the efficiency and superiority of our methods over conventional approaches through simulations.

关键词： Radar theory state estimation filtering methods in signal processing Monte-Carlo data association maximum predictive particle filter proportionally weighting particle filter clutters maximum predictive likelihood sensor fusion target tracking clutter Monte Carlo methods particle filtering (numerical methods) sequential Monte Carlo methods sequential importance resampling algorithm multitarget tracking

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Brain-computer interface for single-trial EEG classification for wrist movement imagery using spatial filtering in the gamma band

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IET signal processing 2010年第5期4卷 510-517页

作者： Khan, Y. U. Sepulveda, F. Univ Essex Dept Comp & Elect Syst Brain Comp Interface Res Grp Colchester CO4 3SQ Essex England

The aim of this study is to discriminate between the left and right wrist movements imagery in four different directions. To achieve this goal, the authors have applied spatial filtering on the EEG signal in the gamma frequency band to extract key features to perform classification. Specifically, the original EEG signal is transformed in to a spatial pattern and applied to the radial basis function (RBF) classifier. The authors demonstrate that spatial filtering method in multichannel EEG effectively extracts discriminant information from single-trial EEG for left and right wrist movement imagery. An average recognition rate of approximately 89% was achieved in all the four type of movements (extension, flexion, pronation and supination) between left and right wrist in five healthy subjects. The results are comparable to the highest rates reported in the literature.

关键词： Biology and medical computing biomedical imaging Bioelectric signals brain-computer interfaces electroencephalography Neural computing techniques discriminant information extraction Digital signal processing Electrical activity in neurophysiological processes pattern classification spatial filtering EEG signal radial basis function classifier multichannel EEG radial basis function networks filtering methods in signal processing average recognition rate Electrodiagnostics and other electrical measurement techniques gamma band brain-computer interface medical signal processing wrist movement imagery spatial filters Data handling techniques single-trial EEG classification

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Adaptive complex unscented Kalman filter for frequency estimation of time-varying signals

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2010年第2期4卷 93-103页

作者： Dash, P. K. Hasan, S. Panigrahi, B. K. SOA Univ Multidisciplinary Res Ctr Bhubaneswar Orissa India Silicon Inst Technol Bhubaneswar Orissa India Indian Inst Technol New Delhi India

A simple and robust non-linear filter algorithm has been proposed in this study for estimating the frequency of a time-varying sinusoidal signal under high noise conditions. The real signal is first converted to an analytical signal and its complex state-space model is derived. An unscented complex Kalman filter (CUKF) is then obtained using the complex signal model and the error covariances along with the Kalman gain are updated iteratively. Also, the stability and the convergence characteristics of the proposed filter are presented for a single sinusoid embedded in noise. It has been shown that the proposed algorithm works efficiently for the estimation of abrupt changes in signal frequency under high noise conditions. To evaluate the performance of the proposed algorithm several computer simulation results of real-time and synthetic signals are presented. Further to improve the performance of the proposed filter in the presence of significant noise and distortions, the covariance matrices are tuned iteratively.

关键词： nonlinear filters adaptive complex unscented Kalman filter Digital signal processing filtering methods in signal processing covariance matrices frequency estimation error covariances Other topics in statistics Kalman filters robust nonlinear filter algorithm Algebra time-varying sinusoidal signal Kalman gain complex state-space model

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Partial-code-aided scheme for narrowband interference suppression in direct-sequence spread spectrum systems

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IET COMMUNICATIONS 2010年第18期4卷 2240-2251页

作者： Zhao, T. Yang, Z. Zhao, Y. Peking Univ State Key Lab Adv Opt Commun Syst & Networks Beijing 100871 Peoples R China

The non-linear filtering is a commonly used time-domain technique for narrowband interference (NBI) suppression in the direct-sequence spread spectrum (DSSS) communication systems. In this study, the authors analyse how the decision feedback error of the spread spectrum (SS) signal influences the performance of the NBI prediction. A partial-code-aided scheme is proposed, which despreads partial chips in a spreading sequence and reconstructs the rest part by spreading the bit decision. This method utilises partial spreading gain to improve the decision accuracy of the SS signals while keeping similar time consumption and implementation complexity with the traditional scheme. It has been verified by simulation that with different NBI models and under different channel conditions, the proposed non-linear filtering method always outperforms the traditional one with chip-wise decision feedback.

关键词： Electromagnetic compatibility and interference automatic repeat request DSSS communication system Protocols wireless channel spread spectrum signal chip-wise decision feedback time-domain technique partial chip nonlinear filters Codes wireless channels codes direct-sequence spread spectrum system time-domain analysis partial-code-aided scheme filtering methods in signal processing spread spectrum communication nonlinear filtering Mathematical analysis decision feedback error Radio links and equipment narrowband interference suppression NBI suppression interference suppression

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z-Domain counterpart to Prony's method for exponential-sinusoidal decomposition

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IET signal processing 2010年第5期4卷 537-547页

作者： Corinthios, M. Ecole Polytech Dept Genie Elect Montreal PQ H3T 1J4 Canada

Prony's method has applications in exponential sinusoidal modelling, parametric modelling, filter design, system modelling and system identification. Similarly to Pade approximation, Prony's method and refinements thereof are major tools for statistical signal analysis, system auto-regressive moving average (ARMA) modelling and least-squares digital filter design. In this study, a z-domain counterpart to Prony's method is proposed as a spectral analysis approach to exponential-sinusoidal decomposition in the presence of noise contamination. The approach is particularly effective in the case where the signal components have 'well behaved' frequencies, meaning that they are multiples of the fundamental frequency. Spectral weighting is applied to power spectra over the z-plane. Spectral peaks of signals contaminated by noise are used to estimate the amplitude, frequency, damping and phase of damped sinusoidal components. The present approach requires no a priori knowledge of the number of damped sinusoidal components present in the contaminated signal, and hence no knowledge of the system order. As expected, however, the analysed signal duration should be long enough to reveal signal properties in the presence of noise. In the case where signal components are not well behaved, spectral leakage would necessitate windowing and higher resolution frequency analysis in order to identify the successive components with improved accuracy.

关键词： high resolution frequency analysis statistical signal analysis parametric modelling autoregressive moving average processes ARMA modelling least-squares digital filter design Prony method system identification system modelling system autoregressive moving average modelling noise contamination filtering methods in signal processing IIR filter least squares approximations spectral analysis IIR filters exponential sinusoidal modelling Pade approximation signal processing theory infinite impulse response filter exponential-sinusoidal decomposition z-domain Interpolation and function approximation (numerical analysis) filter design spectral analysis approach spectral weighting Other topics in statistics

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