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Kronecker Product Multichannel Linear filtering for Adaptive Weighted Prediction Error-Based Speech Dereverberation

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2022年 30卷 1277-1289页

作者： Huang, Gongping Benesty, Jacob Cohen, Israel Chen, Jingdong Technion Israel Inst Technol IL-3200003 Haifa Israel Univ Erlangen Nurnberg LMS D-91058 Erlangen Germany Univ Quebec INRS EMT Montreal PQ H5A 1K6 Canada Technion Israel Inst Technol Andrew & Erna Viterbi Fac Elect & Comp IL-3200003 Haifa Israel Northwestern Polytech Univ Ctr Intelligent Acoust & Immers Commun Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Shaanxi Prov Key Lab Artificial Intelligence Xian 710072 Shaanxi Peoples R China

Reverberation, whichis caused by late reflections, impairs not only speech quality but also intelligibility. Consequently, dereverberation, a process to mitigate the impact of reverberation, has attracted significant research interests. Numerous approaches have been developed in the literature, among which the weighted-prediction-error (WPE) one has demonstrated promising potential for reducing or eliminating reverberation. The WPE method has been well studied and several variants have been developed. The adaptive one, called adaptive WPE (AWPE) method, has been widely investigated for use in real applications as it can deal with reverberation in time-varying acoustic environments. However, the computational complexity of AWPE is high, which may be a problem for its implementation in real-time systems. This paper presents some new insights into AWPE-based speech dereverberation by introducing the concepts of Kronecker product and partially time-varying filtering. It then develops two algorithms for dereverberation with lower complexity than AWPE. The significant contributions of this work are as follows. First, we propose a Kronecker product filtering framework for speech dereverberation, where the linear prediction filter is formulated as the Kronecker product of two sets of shorter filters. Second, we propose a partially time-varying Kronecker product filter for dereverberation. Instead of estimating the entire linear prediction filter as in the conventional method, the proposed one only needs to update part of the filter. The proposed approaches can significantly reduce the computational complexity without sacrificing dereverberation performance as compared to AWPE. Simulation results validate the theoretical analysis and justify the advantages of the new methods.

关键词： Maximum likelihood detection Nonlinear filters filtering algorithms Speech processing Reflection Reverberation Prediction algorithms Adaptive weighted-prediction-error kronecker product filtering microphone arrays multichannel linear prediction speech dereverberation

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Pipelined RLS adaptive filtering using scaled tangent rotations (STAR)

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1996年第10期44卷 2591-2604页

作者： Raghunath, KJ Parhi, KK UNIV MINNESOTA DEPT ELECT ENGN MINNEAPOLIS MN 55455 USA

The QR decomposition-based recursive least-squares (RLS) adaptive filtering algorithm (referred to as QRD-RLS) is very popular because it has good numerical properties and can be mapped onto a systolic array, However, in this architecture, pipelining of the operations within the systolic array cells is difficult, Pipelining would be necessary to operate at high speeds or to reduce the power dissipation in a VLSI implementation, Pipelining QRD-RLS using look-ahead techniques leads to a large hardware overhead, The square-root free forms of QRD-RLS are also difficult to pipeline, In this paper, a new scaled tangent rotation (STAR) is used instead of the Givens rotations used In QRD-RLS, The STAR-based RLS algorithm (referred to as STAR-RLS) is designed such that fine-grain pipelining can be accomplished with little hardware overhead, The scaled tangent rotations are not exactly orthogonal transformations but tend to become orthogonal asymptotically, The STAR-RLS algorithm is square-root free and has less complexity and lower intercell communication than the QRD-RLS algorithm, The properties of the STAR-RLS algorithm, such as stability, numerical property, and dynamic range, are examined with and without pipelining and compared with those of QRD-RLS, Simulation results are presented to compare the performance of STAR-RLS and QRD-RLS algorithms.

关键词： Resonance light scattering Adaptive filters Pipeline processing Systolic arrays Hardware filtering algorithms Power dissipation Very large scale integration Algorithm design and analysis Numerical stability

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An object-based analysis filtering algorithm for airborne laser scanning

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2012年第22期33卷 7099-7116页

作者： Yan, Menglong Blaschke, Thomas Liu, Yu Wu, Lun Chinese Acad Sci Key Lab Spatial Informat Proc & Applicat Syst Tec Beijing Peoples R China Peking Univ Inst Remote Sensing & Geog Informat Syst Beijing 100871 Peoples R China Salzburg Univ Z GIS Ctr Geoinformat A-5020 Salzburg Austria Salzburg Univ Dept Geog & Geol A-5020 Salzburg Austria

Ground filtering is a key process to derive digital terrain models from airborne laser scanning data. Although many methods have been developed to tackle the filtering problem, it has not been fully solved so far. Current algorithms mainly focus on neighbourhood-based or directional filtering approaches. A new object-based analysis (OBA) method is proposed in this article. First, a grid index algorithm accelerates access to unorganized cloud points. Then, a segmentation algorithm is deployed based on the index, and objects are obtained. A filtering logic that utilizes the objects' characteristics is designed. Following this, the performance of the method is comprehensively tested using publicly available International Society for Photogrammetry and Remote Sensing (ISPRS) test data sets for nine urban and six rural regions, and the results are compared to those of eight other algorithms. The OBA method implemented in this article reveals good results without scene-wise optimization of the parameters, and it ranks third or fourth in most of the cases.

关键词： airborne lasers Oxygen Breathing Apparatus filtering algorithms algorithms laserscan scanning Photogrammetry Filtration object orientation

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A Derivative-Free Kalman filtering Approach to State Estimation-Based Control of Nonlinear Systems

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2012年第10期59卷 3987-3997页

作者： Rigatos, Gerasimos G. Ind Syst Inst Unit Ind Automat Patras 26504 Greece

For nonlinear systems, subject to Gaussian noise, the extended Kalman filter (EKF) is frequently applied for estimating the system's state vector from output measurements. The EFK is based on linearization of the systems' dynamics using a first-order Taylor expansion. Although EKF is efficient in several problems, it is characterized by cumulative errors due to the gradient-based linearization it performs, and this may affect the accuracy of the state estimation or even risk the stability of the state estimation-based control loop. To overcome the flaws of EKF, it has been proposed to use the unscented Kalman filter (UKF) as a method for nonlinear state estimation, which does not introduce linearization errors. Aiming also at finding more efficient implementations of nonlinear Kalman filtering, this paper introduces a derivative-free Kalman filtering approach, which is suitable for state estimation-based control of a class of nonlinear systems. The considered systems are first subject to a linearization transformation, and next state estimation is performed by applying the standard Kalman filter to the linearized model. Unlike EKF, the proposed method provides estimates of the state vector of the nonlinear system without the need for derivatives and Jacobians calculation and without using linearization approximations. The proposed derivative-free Kalman filtering approach has been compared to EKF and UKF in the case of state estimation-based control for a nonlinear DC motor model.

关键词： filtering algorithms Kalman filters nonlinear control systems sensorless control state estimation

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Highly Maneuverable Target Tracking Under Glint Noise via Uniform Robust Exact filtering Differentiator With Intrapulse Median Filter

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第3期58卷 2541-2559页

作者： Antonio Aranda, Italo Perez-Zunez, Gustavo Pontifical Catholic Univ Perus Postgrad Sch Control & Automat Engn Masters Degree Program San Miguel 15088 Peru Pontifical Catholic Univ Perus Postgrad Sch Engn Dept San Miguel Peru

Highly maneuverable target tracking under glint noise and nonlinear uncertainties during course changes and terminal maneuvers has been solved suboptimally for many years via the interacting multiple model algorithm with the use of the Kalman filter (KF), unscented Kalman filter, and the extended Kalman filter (EKF). Also, nonlinear KFs such as the cubature Kalman filter have been proposed to improve nonlinear tracking without being able to filter out glint noise. To this end, the particle filter and some KF based on variational Bayesian approach have been proposed with very good results in filtering out glint noise. Nonetheless, it is difficult for state-of-the-art methods to achieve efficient filtering of glint noise and nonlinear tracking at the same time. On the other hand, robust exact differentiators, based on the super-twisting algorithm, have been used for many years in output-feedback control and state observation in order to obtain the derivatives of an input signal with theoretical finite-time exactness. However, their potential for target tracking applications has not been explored sufficiently. In this article, a uniform robust exact filtering differentiator with intrapulse median filtering is proposed to filter out glint noise at the sliding manifold, while offering nonlinear tracking robustness via high-degree super-twisting terms outside the sliding manifold. Numerical simulations comparing the proposed solution to other state-of-the-art methods were conducted, showing promising results.

关键词： Target tracking Radar tracking filtering Kalman filters filtering algorithms Robustness Noise measurement Differentiation glint noise median filtering target tracking variable structure systems

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Research on Background Learning Correlation filtering Algorithm With Multi-Feature Fusion

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IEEE ACCESS 2023年 11卷 32895-32906页

作者： Ren, Hongge Xing, Leigang Shi, Tao Tianjin Chengjian Univ Sch Control & Mech Engn Tianjin 300384 Peoples R China North China Univ Sci & Technol Coll Elect Engn Tangshan 063210 Peoples R China Tianjin Univ Technol Sch Elect Engn & Automat Tianjin 300384 Peoples R China

Aiming at the problems of occlusion, drift, and background change in visual image tracking, a background learning correlation filtering algorithm based on multi-feature fusion is proposed. In the framework of correlation filtering, multi-feature fusion, multi-template update, and background learning regularization are used to improve the performance of the filter in the problem of template contamination and object occlusion. The fast directional gradient histogram (FHOG), color feature (CN), and texture feature (ULBP) were extracted, and the feature channels were connected in series. Then the depth features of Conv4-4 and Conv5-4 layers were extracted through the VGG-19 network, and the appearance model of the target was constructed. To reduce the sensitivity of the filter to the sudden change of background, a background learning filter is constructed, and the alternate direction multiplier method (ADMM) is used to speed up the calculation of the filter. In the model update stage, aiming at the problem of pollution of the original template caused by target occlusion, a high-confidence multi-template fusion update strategy is proposed by fusing the template with the highest confidence in the current frame, the previous frame, and the history frame. Finally, the proposed algorithm is tested on OTB50, OTB100, UAV123, and TC128 experimental data sets, and some classical and latest algorithms. The experimental results show that the tracking accuracy and robustness of the correlation filtering algorithm are improved.

关键词： Target tracking filtering algorithms Feature extraction Correlation Information filters Solid modeling Predictive models machine vision correlation filtering background learning more templates

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A Novel Adaptive filtering for Cooperative Localization Under Compass Failure and Non-Gaussian Noise

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第4期71卷 3737-3749页

作者： Xu, Bo Wang, Xiaoyu Zhang, Jiao Guo, Yu Razzaqi, Asghar A. Harbin Engn Univ Dept Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Harbin Engn Univ Engn Res Ctr Nav Instruments Harbin 150001 Peoples R China

Multi-autonomous underwater vehicles (AUVs) cooperative localization has become a research hotspot in the marine navigation field. In this paper, a filtering algorithm for slave AUV with compass failure is proposed based on 'two-master-one slave' cooperative localization model. In this algorithm, the course angle is unknown, and non-Gaussian measurement noise caused by measurement outliers is considered. The cooperative localization model is reconstructed, and a filtering algorithm is derived whose performance will not be affected by the unknown course angle. Moreover, the maximum information potential (MIP) criterion is introduced into a recursive model to deal with the non-Gaussian measurement noise in a complicated underwater environment. Simultaneously, the kernel bandwidth is adaptively adjusted by the innovation matrix and the measurement error covariance matrix. The proposed algorithm can accurately estimate the variation of the unknown course angle without expanding the dimension of state under the condition of non-Gaussian measurement noise. Finally, the proposed algorithm is verified through field experiments. The results show that the proposed algorithm has higher accuracy and robustness than other existing algorithms.

关键词： Navigation Compass Noise measurement Location awareness Mathematical models filtering algorithms Sea measurements AUV cooperative localization compass failure non-Gaussian noise

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CEPSTRAL filtering ON A COLUMNAR IMAGE ARCHITECTURE - A FAST ALGORITHM FOR BINOCULAR STEREO SEGMENTATION

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1989年第7期11卷 759-767页

作者： YESHURUN, Y SCHWARTZ, EL NYU MED CTR DEPT PSYCHIATCOMPUTAT NEUROSCI LABSNEW YORKNY 10016 NYU COURANT INST MATH SCIDEPT COMP SCINEW YORKNY 10012

Many primate visual cortex architectures have a prominent feature responsible for the mixing of left and right eye visual data: ocular dominance columns represent thin (about 5-10 minutes of arc) strips of alternating left and right eye input to the brain. It is shown that such an architecture, when operated upon with a cepstral filter, provides a strong cue for binocular stereopsis. Specifically, the vector of binocular disparity may be easily identified in the output of the (columnar based) cepstral filter. This algorithm is illustrated with application to a random dot stereogram and to natural images. The authors suggest that this provides a fast algorithm for stereo segmentation, in a machine vision context. In a biological context, it may provide a computational rationale for the existence of columnar systems with regard to both ocular mixing and other visual modalities that have a columnar architecture.

关键词： Cepstral analysis filtering algorithms Image segmentation Humans Filters Layout Machine vision Laboratories Psychiatry Biomedical imaging INSPEC: Controlled Indexing visual perception computer vision filtering and prediction theory pattern recognition INSPEC: Non-Controlled Indexing machine vision pattern recognition computer vision columnar image architecture binocular stereo segmentation primate visual cortex architectures ocular dominance columns brain cepstral filter binocular stereopsis binocular disparity random dot stereogram natural images stereo segmentation

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Reverse Image filtering Using Total Derivative Approximation and Accelerated Gradient Descent

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IEEE ACCESS 2022年 10卷 124928-124944页

作者： Galetto, Fernando J. J. Deng, Guang La Trobe Univ Dept Engn Melbourne Vic 3086 Australia

In this paper, a new problem of reverse image filtering is addressed. The problem is to reverse the effect of an image filter given the observation b = g(x). The filter g is modelled as an available black box. An inverse method is proposed to recover the input image x. The key idea is to formulate this inverse problem as minimizing a local patch-based cost function. To use gradient descent for the minimization, total derivative is used to approximate the unknown gradient. This paper presents a study of factors affecting the convergence and quality of the output in the Fourier domain when the filter is linear. It discusses the convergence property for nonlinear filters by using contraction mapping as a tool. It also presents applications of the accelerated gradient descent algorithms to three gradient-free reverse filters, including the one proposed in this paper. Results from extensive experiments are used to evaluate the complexity and effectiveness of the proposed algorithm. The proposed algorithm outperforms the state-of-the-art in two aspects. (1) It is at the same level of complexity as that of the fastest reverse filter, but it can reverse a larger number of filters. (2) It can reverse the same list of filters as that of a very complex reverse filter, but its complexity is much lower.

关键词： Maximum likelihood detection Inverse problems Nonlinear filters filtering algorithms Minimization Cost function Image filtering Inverse filtering optimization accelerated gradient descent total derivative

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CONJUGATE-GRADIENT TECHNIQUES FOR ADAPTIVE filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS 1992年第1期39卷 1-10页

作者： BORAY, GK SRINATH, MD SO METHODIST UNIV DEPT ELECT ENGNDALLASTX 75275

Recent advances in hardware technology have made possible the implementation of sophisticated algorithms for adaptive filtering. The least mean squares (LMS) method, which has found widespread use owing to its simplicity, has poor convergence properties. The recursive least squares (RLS) method possesses superior convergence properties, but it is computationally intensive and has high storage requirements for matrix manipulations. Modifications of the RLS technique to reduce complexity have resulted in "fast" RLS methods, many of which unfortunately tend to be numerically unstable. In this paper the technique of conjugate gradients is applied for the adaptive filtering problem. The choice of the gradient average window in the algorithm provides one with a trade-off between computational complexity and convergence performance. The method is capable of providing convergence comparable to RLS schemes at a computational complexity that is intermediate between the LMS and the RLS methods and does not suffer from any known instability problems.

关键词： Adaptive filters Resonance light scattering Signal processing algorithms Least squares approximation Convergence Algorithm design and analysis Circuits Hardware filtering algorithms Least squares methods

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