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An effective weighted vector median filter for impulse noise reduction based on minimizing the degree of aggregation

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IET IMAGE processing 2021年第1期15卷 228-238页

作者： Meng, Xiangxi Lu, Tongwei Min, Feng Lu, Tao Wuhan Inst Technol Sch Comp Sci & Engn Wuhan Peoples R China Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan 430074 Peoples R China

Impulse noise is regarded as an outlier in the local window of an image. To detect noise, many proposed methods are based on aggregated distance, including spatially weighted aggregated distance, n nearest neighbour distance, local density, and angle-weighted quaternion aggregated distance. However, these methods ignore the weight of each pixel or have limited adaptability. This study introduces the concept of degree of aggregation and proposes a weighting method to obtain the weight vector of the pixels by minimizing the degree of aggregation. The weight vector obtained gives larger components on the signal pixels than on the noisy pixels. Then it is fused with the aggregated distance to form a weighted aggregated distance that can reasonably characterise the noise and signal. The weighted aggregated distance, along with an adaptive segmentation method, can effectively detect the noise. To further enhance the effect of noise detection and removal, an adaptive selection strategy is incorporated to reduce the noise density in the local window. At last, noisy pixels detected are replaced with the weighted channel combination optimization values. The experimental results exhibit the validity of the proposed method by showing better performance in terms of both objective criteria and visual effects.

关键词： Algebra Optical, image and video signal processing filtering methods in signal processing Optimisation techniques Computer vision and image processing techniques Optimisation techniques Algebra

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Digital filter-based grid synchronization for autonomous microgrids

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IET RENEWABLE POWER GENERATION 2021年第15期15卷 3732-3742页

作者： Elyasichamazkoti, Farhad Aminifar, Farrokh Davarpanah, Mahdi Univ Utah Dept Elect & Comp Engn Salt Lake City UT 84112 USA Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran 113654563 Iran

Having a robust and accurate grid synchronization unit for distributed generators (DGs) has been accentuated due to rapidly growing integration of inverter-based DGs to the autonomous microgrids. Although most of the synchronization methods are based on the phased locked loops (PLL) technique, digital filters could result in more salient dynamic performance in non-ideal sinusoidal situations. This paper presents a novel digital filter as a grid synchronization method that overcomes the flaws of conventional PLLs under harmonic distorted and unbalanced cases as well as large delay of window-based digital filters. For the sake of computational advantages, the proposed method is executed in two distinct offline and online stages. In the offline stage, the second-order Taylor series expansion approximation of the bandpass oscillatory signal calculates the state-transition matrices and state vectors. Then, by applying the concept of Kalman filter, the state vectors are updated in the online calculation section much easier and faster compared to all-online approach. Compared to the existing methods, the proposed method generates a dynamic response with less settling time, more accuracy, and immunity against harmonic distortion, imbalances, and noise.

关键词： Modulators, demodulators, discriminators and mixers filtering methods in signal processing Control of electric power systems Other topics in statistics Distributed power generation Power convertors and power supplies to apparatus

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A novel filtering algorithm for the resolver position signal based on recursive least squares and phase-locked loop

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IET POWER ELECTRONICS 2021年第16期14卷 2535-2544页

作者： Lian, Chuanqiang Xiao, Fei Liu, Jilong Gao, Shan Naval Univ Engn Natl Key Lab Sci & Technol Vessel Integrated Powe Wuhan Peoples R China

This paper presents a novel filtering algorithm for the resolver position signal based on recursive least squares (RLS) and phase-locked loop (PLL). The proposed RLS-PLL-based algorithm designs a harmonic iterative estimation and elimination mechanism, which can accurately estimate and eliminate the specified harmonic contained in the resolver online and obtain an ideal reconstructed position signal. The convergence analysis, parameter design and pseudo code of the proposed algorithm are all provided. The advantage of the presented method mainly lies in that it has excellent position signal filtering performance in the entire operating condition, including low speed, high speed, steady state, and dynamic. Experimental results demonstrate that compared to the results achieved with the conventional look-up table (LUT) method and the advanced type-III angle tracking observe (ATO), the proposed RLS-PLL-based algorithm has improved filtering performance. Under some typical operating conditions, the harmonic content of the position signal reconstructed by the new method is reduced by an order of magnitude.

关键词： Modulators, demodulators, discriminators and mixers Optical, image and video signal processing filtering methods in signal processing Spatial variables control Interpolation and function approximation (numerical analysis)

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Affine projection Weibull M-transform least mean square algorithm against impulsive interference

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IET COMMUNICATIONS 2021年第17期15卷 2151-2158页

作者： Liu, Pingchuan Fan, Kuangang Qiu, Haiyun Jiangxi Univ Sci & Technol Sch Mech & Elect Engn Ganzhou Peoples R China Jiangxi Univ Sci & Technol Magnet Suspens Technol Key Lab Jiangxi Prov Ganzhou Peoples R China Jiangxi Univ Sci & Technol Sch Elect Engn & Automat Hongqi St 86 Ganzhou 34100 Peoples R China

The well-known affine projection sign algorithm is one of the classic adaptive filtering methods for denoising and channel equalisation, which can achieve robust performance against coloured input and impulse noise. This work proposes the affine projection Weibull M-transform least mean square algorithm to improve the steady-state performance of affine projection algorithms. The proposed algorithm combines the Weibull M-transform cost function with the L2-norm constraint of weight vector to improve the performance of existing affine projection algorithms and achieve a better convergence rate and better misalignment. This novel algorithm also preserves good performance against impulsive interference and coloured input. Simulation results demonstrated that the proposed algorithm has a remarkable improvement in convergence performance compared with other algorithms.

关键词： Integral transforms Other topics in statistics Interpolation and function approximation (numerical analysis) filtering methods in signal processing Integral transforms Other topics in statistics signal processing theory Interpolation and function approximation (numerical analysis) Digital signal processing

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Multiple extended stealth target tracking from image observation based on non-linear sub-random matrices approach

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IET RADAR SONAR AND NAVIGATION 2021年第3期15卷 274-293页

作者： Barbary, Mohamed Abd ElAzeem, Mohamed H. Alexandria Univ Dept Elect Engn Alexandria Egypt Egyptian Tech Res & Dev Ctr Cairo Egypt Arab Acad Sci Technol & Maritime Transport Dept Elect & Commun Cairo Egypt

The detection and tracking of extended stealth targets (ESTs) is a challenging task in radar technology, especially if from image observations because of the fluctuations of radar cross section. To overcome this challenge, multi-Bernoulli (MB) filter can be used to extract the extended target (ET) states in efficient and reliable manner. Recently, the MB-filter-based random matrices model (RMM) approach has been proposed for ellipsoidal ET tracking with additional state variables. However, RMM-MB filter is demonstrated with known detection profile, which is unsuitable for EST tracking. Thus, a joint detection and tracking of multiple ESTs based on track-before-detect (TBD) approach, which is an efficient way to track low-observable ESTs, is proposed. In EST-RMM-TBD scenarios, although the extension ellipsoid is efficient, it may not be accurate because of some missing useful information, such as size, shape, and orientation. To address this, a EST-sub-RMM-TBD composed of sub-ellipses is introduced, each representing an RMM. Based on such models, a sub-RMM-MB-TBD filter is used to estimate the kinematic states and extensions of sub-objects for each EST. Furthermore, a sequential Monte Carlo (SMC) implementation to estimate non-linear kinematic EST state is applied. The results indicate that the proposed SMC-sub-RMM-MB-TBD filter has more accurate cardinality estimation and smaller optimal sub-pattern assignment errors than the recent extended tracking filters.

关键词： Computer vision and image processing techniques nonlinear sub-random matrices approach nonlinear kinematic EST state extension ellipsoid recent extended tracking filters multiple ESTs image observation EST tracking Optical, image and video signal processing matrix algebra track-before-detect approach extended target states extended stealth targets Algebra Radar equipment, systems and applications MB-filter-based random matrices model approach SMC-sub-RMM-MB-TBD filter stealth target signal detection efficient manner filtering methods in signal processing low-observable ESTs EST-RMM-TBD scenarios tracking filters known detection profile multiBernoulli filter RMM-MB filter additional state variables Integral transforms EST-sub-RMM-TBD radar technology Interpolation and function approximation (numerical analysis) object detection ellipsoidal ET tracking Image recognition radar cross-sections target tracking radar cross section Monte Carlo methods kinematic states filtering theory Other topics in statistics

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Medical image fusion and noise suppression with fractional-order total variation and multi-scale decomposition

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IET IMAGE processing 2021年第8期15卷 1688-1701页

作者： Zhang, Xuefeng Yan, Hui Northeastern Univ Coll Sci Shenyang 110819 Peoples R China

Fusion and noise suppression of medical images are becoming increasingly difficult to be ignored in image processing, and this technique provides abundant information for the clinical diagnosis and treatment. This paper proposes a medical image fusion and noise suppression model in pixel level. This model decomposes the original image into a noiseless base layer, a large-scale noiseless detail layer and a small-scale detail layer which contains details and noise information. The fractional-order derivative and saliency detection are used to construct the weight functions to fuse the base layers. The proposed total variation model combines the fractional-order derivative to fuse the small-scale detail layers. The mathematical properties and time complexity of the total variation model are also analysed. And choose-max method is used to fuse the large-scale detail medical layers simply. Our approach is based on fractional-order derivative, which enables keep more information and decrease blocky effects more effectively compared with the integer-order derivative. To verify the validity, the proposed method is compared with some fusion methods in the subjective and objective aspects. Experiments show that the proposed model fuses the source information fully and decreases noise cleanly.

关键词： Optical, image and video signal processing filtering methods in signal processing Computer vision and image processing techniques Patient diagnostic methods and instrumentation Biology and medical computing

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Auto-calibration Kalman filters for non-linear systems with direct feedthrough

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IET CONTROL THEORY AND APPLICATIONS 2021年第6期15卷 890-899页

作者： Cui, Yi Wang, Zhihua Beihang Univ Res Ctr Small Sample Technol Beijing 100191 Peoples R China

The problem of state estimation for non-linear systems with unknown inputs is discussed. The objective is to construct a non-linear filter where the unknown input affects both the state equation and measurement equation. An auto-calibration Kalman filter is proposed with three stages, where the first and second stages are the extension of the two-stage Kalman filter in non-linear system cases, and the third stage is the extended Kalman filter with correlated noise. Compared with the two-stage extended Kalman filter, the presented algorithm conducts a more reasonable linearization of the measurement equation in the third stage. As a result, auto-calibration is realized and more accurate and more stable state estimation can be obtained. Simulative and practical examples both demonstrate the validity and superiority of the presented auto-calibration Kalman filter. Furthermore, positioning experiments illustrate that the auto-calibration Kalman filter can obtain an accurate long-term heading estimation in pedestrian navigation and is easy to apply in engineering applications.

关键词： Measurement standards and calibration filtering methods in signal processing Measurement standards and calibration Simulation, modelling and identification signal processing theory Control system analysis and synthesis methods Stability in control theory Multivariable control systems Self-adjusting control systems Spatial variables control Biological and medical control systems Robotics Digital signal processing

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A reliable and energy efficient dual prediction data reduction approach for WSNs based on Kalman filter

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IET COMMUNICATIONS 2021年第18期15卷 2285-2299页

作者： Wang, Haibin Yemeni, Zaid Ismael, Waleed M. Hawbani, Ammar Alsamhi, Saeed H. Hohai Univ Coll IoT Engn Changzhou Jiangsu Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Athlone Inst Technol Software Res Inst Athlone Ireland

Wireless sensor networks (WSNs) are critically resource-constrained due to wireless sensor nodes' tiny memory, low processing units, power limitations, and narrow communication bandwidth. The data reduction technique is one of the most widely used techniques to reduce transmitted data over the wireless sensor networks and to minimize the sensor nodes' energy consumption, particularly, the entire network in general. This paper proposes a reliable dual prediction data reduction approach for WSNs. This approach performs data reduction through two phases: the data reduction phase (DRP) and data prediction phase (DPP). The DRP is mainly to decrease the number of transmissions between the sensor node and the sink node, thereby minimizing energy consumption. It also detects faulty data and discards them at the sensor node. The discarded faulty data at the sensor nodes are replaced by estimated values at the sink node to maintain data reliability. DPP runs at the sink node or base station, which works in synchronization with the sensor nodes. This phase is responsible for predicting the non-transmitted data based on the Kalman filter. The simulation results demonstrate that the proposed approach is efficient and effective in data reduction, data reliability, and energy consumption.

关键词： filtering methods in signal processing Wireless sensor networks Telecommunication systems (energy utilisation)

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SINR improvement based on joint design of transmit covariance matrix and receive filter design for colocated MIMO radar

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IET COMMUNICATIONS 2021年第4期15卷 603-612页

作者： Imani, Sadjad Feraidooni, Mohammad Mahdi Gharavian, Davood Nayebi, Mohammad Mahdi Shahid Beheshti Univ GC Fac Elect Engn Dept Telecommun Tehran Iran Shahid Beheshti Univ GC Dept Elect Engn Tehran Iran

This paper examines we aim to improve the performance of target detection in the presence of signal-dependent interference in multiple-input multiple-output radar through the joint design of the receiver spatial filter and the covariance matrix of waveforms. For this purpose, the signal-to-interference-plus-noise ratio is considered as the design criterion. The proposed method is a sequential algorithm that calculates the receiver filter coefficients through a closed form in each step of the algorithm. The correlation of transmit waveforms is also calculated utilizing the coordinate descent method. The covariance matrix is designed in two forms. In the first form, it is assumed that the optimal matrix is a Toeplitz matrix which leads to a simpler design. In the second form, the covariance matrix is calculated by combining orthogonal waveforms, which yields better performance for interfere reduction. Simulation results show that the proposed method has more appropriate performance in comparison with other methods.

关键词： Algebra Electromagnetic compatibility and interference filtering methods in signal processing signal detection Radar equipment, systems and applications

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Unsupervised change detection method in SAR images based on deep belief network using an improved fuzzy C-means clustering algorithm

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IET IMAGE processing 2021年第4期15卷 974-982页

作者： Attioui, Sanae Najah, Said Univ Sidi Mohamed Ben Abdellah Dept Comp Sci Fac Sci & Technol Fes Morocco

Deep learning methods have recently displayed ground-breaking results for synthetic aperture radar image change detection problem. However, they still face the challenges of intrinsic noise and the difficulty of acquiring labeled data. To sort out these issues, we aim to develop a change detection approach specifically designed for analyzing synthetic aperture radar images based on Deep Belief Network as the deep architecture which includes unsupervised feature learning and supervised network fine-tuning. The deep neural networks can reach the final change maps directly from the two original images. A pre-classification based on Morphological Reconstruction and Membership filtering is employed in order to minimize the effect of noise. Appropriate diversity samples are provided by a virtual sample generation method in order to mitigate overfitting raised by limited synthetic aperture radar data. Visual and quantitative analysis as well as comparisons with advanced algorithms show that our algorithm not only achieves better results but also requires less implementation time.

关键词： Algebra, set theory, and graph theory Data and information acquisition, processing, storage and dissemination in geophysics Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research Image recognition filtering methods in signal processing Radar theory Radar equipment, systems and applications Geophysical techniques and equipment Computer vision and image processing techniques Combinatorial mathematics Combinatorial mathematics Geophysics computing Knowledge representation Unsupervised learning

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