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Efficient Recovery of Sparse Graph Signals From Graph Filter Outputs

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 5550-5566页

作者： Morgenstern, Gal Routtenberg, Tirza Ben Gurion Univ Negev Sch Elect & Comp Engn IL-84105 Beer Sheva Israel

This paper investigates the recovery of a node-domain sparse graph signal from the output of a graph filter. This problem, which is often referred to as the identification of the source of a diffused sparse graph signal, is seminal in the field of graph signal processing (GSP). Sparse graph signals can be used in the modeling of a variety of real-world applications in networks, such as social, biological, and power systems, and enable various GSP tasks, such as graph signal reconstruction, blind deconvolution, and sampling. In this paper, we assume double sparsity of both the graph signal and the graph topology, as well as a low-order graph filter. We propose three algorithms to reconstruct the support set of the input sparse graph signal from the graph filter output samples, leveraging these assumptions and the generalized information criterion (GIC). First, we describe the graph multiple GIC (GM-GIC) method, which is based on partitioning the dictionary elements (graph filter matrix columns) that capture information on the signal into smaller subsets. Then, the local GICs are computed for each subset and aggregated to make a global decision. Second, inspired by the well-known branch and bound (BNB) approach, we develop the graph-based branch and bound GIC (graph-BNB-GIC), and incorporate a new tractable heuristic bound tailored to the graph and graph filter characteristics. In addition, we propose the graph-based first order correction (GFOC) method, which improves existing sparse recovery methods by iteratively examining potential improvements to the GIC cost function by replacing elements from the estimated support set with elements from their one-hop neighborhood. Simulations on stochastic block model (SBM) graphs demonstrate that the proposed sparse recovery methods outperform existing techniques in terms of support set recovery and mean-squared-error (MSE), without significant computational overhead. In addition, we investigate the application of our graph-ba

关键词： Information filters Deconvolution filtering theory Sparse matrices Vectors filtering algorithms Brain modeling Dictionaries Spectral analysis Signal reconstruction Graph signal processing (GSP) support set recovery double sparsity generalized information criterion (GIC) graph signals graph filters

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Research on Anomaly Suppression Correlation filtering Algorithm

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IEEE ACCESS 2022年 10卷 2457-2468页

作者： Ren, Hongge Cui, Yin 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 target occlusion, illumination change, and fast motion in target tracking, an anomaly suppression correlation filtering algorithm is proposed. The histogram of oriented gradient (HOG) feature and color name (CN) feature are extracted to construct the target appearance model, and an abnormal suppression correlation filter is constructed to suppress sudden changes in the filter response graph. The alternating direction method of multipliers (ADMM) method is used to speed up the calculation of the filter. A scale filter is added in the article to solve the problem of inaccurate tracking caused by a single scale. In the model update process, the three-layer rotating circle memory model is used to update, which improves the tracking ability of the algorithm. It is better to update the target model in the scene where the target is occluded. The proposed algorithm and BACF, KCC, KCF, MKCFup, ARCF are tested in the OTB50, OTB100, UAV123, TC128 experimental data sets. The results show that the visual tracking algorithm proposed in the article has a high success rate and accuracy, and has certain research value.

关键词： Target tracking filtering algorithms Correlation Feature extraction Training Information filters Real-time systems Computer vision target tracking correlation filtering abnormal suppression ADMM method memory model

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

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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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Augmented Affine Projection Algorithm Based on Error Nonlinearity and Its Simplified Version

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 2767-2782页

作者： Gu, Yanglong Ni, Jingen Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Peoples R China

The augmented affine projection algorithm (AAPA) has a faster convergence rate than the augmented complex-valued LMS (ACLMS) algorithm for correlated inputs. However, they are both not robust against impulsive interference. This work proposes an improved AAPA based on error nonlinearity, which is implemented with the derivative of the logistic distance metric. To reduce its computational complexity, this work also proposes a simplified version of the improved AAPA using the first-order Taylor approximation of the exponential function that is included in the derivative of logistic distance metric. To study the stochastic behaviors of the improved AAPA and its simplified version, their stability conditions and transient and steady-state performance are analyzed. Simulations are performed in the context of system identification and stereo acoustic echo cancellation to show the merits of the two proposed algorithms and to verify the accuracy of the theory on stability conditions and transient and steady-state performance.

关键词： Adaptive filters Vectors Logistics Measurement Signal processing algorithms filtering algorithms Noise Affine projection algorithm augmented linear model impulsive interference logistic distance metric

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Cubic Polishing Spline-Based algorithms for Industrial Image Processing

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第1期20卷 314-326页

作者： Sun, Fangli Cai, Zhanchuan Macau Univ Sci & Technol Sch Comp Sci & Engn Macau 999078 Peoples R China

Industrial image processing is a major technology in the fourth industrial revolution. In recent years, the spline method has gained more and more attention in image processing. To explore an efficient industrial image processing algorithm, this article proposes novel cubic polishing spline algorithms. The cubic polishing spline is a high precision spline algorithm that brings more degrees of freedom by adding nodes to better balance the local and global aspects. First, the direct and indirect cubic polishing spline transforms are used for the image interpolation filter. Then, we construct the least squares cubic polishing spline and pyramid approximation method with application to the image reconstruction and image scaling (image upscaling or downscaling). Next, the frequency domain cubic polishing spline lowpass filter and highpass filter are proposed, and the proposed schemes are applied to image smoothing and image sharpening, respectively. Finally, the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) and root mean square error (RMSE) evaluation metrics are used to illustrate the reconstruction effects, and experiments show that the algorithms proposed in this article can achieve better reconstruction results with better fidelity and competitiveness compared with other schemes.

关键词： Splines (mathematics) Image reconstruction Image processing Interpolation Signal processing algorithms Approximation algorithms filtering algorithms Approximation method image reconstruction image scaling interpolation polishing spline

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A Novel TPMBM Filter for Partly Resolvable Multitarget Tracking

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IEEE SENSORS JOURNAL 2024年第10期24卷 16629-16646页

作者： Xue, Xirui Wei, Daozhi Huang, Shucai Air Force Engn Univ Air & Missile Def Coll Xian 710051 Shaanxi Peoples R China

Due to the limitation of sensor resolution, multiple target locations in close proximity can result in merged measurements, which makes it difficult for traditional measurement association assumptions to be met and makes the filter unable to resolve single target trajectories. In this article, we use random finite set (RFS) theory to develop a novel trajectory Poisson multi-Bernoulli mixture (TPMBM) filter for partly resolvable multitarget (PRM) tracking, which is called the TPMBM-PRMT filter. First, we improve the likelihood model for the unresolved measurements and use the breadth-first search (BFS) algorithm to partition the predicted set of detected trajectories. In addition, a trajectory subset-measurement association update method is proposed, where all trajectories within the subset share the same measurement, and a trajectory Poisson multi-Bernoulli (TPMB) approximation is designed to generate multiple high-quality association hypotheses and ensure the feasibility of the filter. Finally, the Gaussian L-scan approximation is used in the filter implementation, which reduces the computational cost while ensuring the filtering efficiency. Simulation results show that the filter in this article can effectively handle the merging of multitarget measurements and achieve stable tracking of PRM.

关键词： Target tracking Trajectory Sensors Information filters filtering algorithms filtering theory Radar tracking Breadth-first search (BFS) Gaussian L-scan approximation partly resolvable multitarget (PRM) tracking Poisson multi-Bernoulli mixture filter trajectory Poisson multi-Bernoulli (TPMB) approximation

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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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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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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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OBMA: Scalable Route Lookups With Fast and Zero-Interrupt Updates

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第6期32卷 4842-4854页

作者： Zhang, Chuwen Feng, Yong Song, Haoyu Wan, Ying Xu, Wenquan Liu, Bin Tsinghua Univ Dept Comp Sci Beijing 100190 Peoples R China Futurewei Technol San Jose CA 95131 USA Tsinghua Univ Dept Comp Sci & Technol Beijing 100190 Peoples R China

Software-based IP route lookup is a key component for packet forwarding in Software Defined Networks. Running lookup algorithms on commodity CPUs is flexible and scalable, which shows advantages on cost and power consumption over the hardware-based forwarding engines. However, dynamic network functions and services make route updates more frequent than ever. Existing algorithms often fall short of the incremental update requirements. In this paper, we propose the Overlay BitMap Algorithm (OBMA), which contains several variations, to support extraordinary update performance while maintaining the highest-in-class lookup speed and storage efficiency. Starting from the basic OBMA_B, we develop two variations with different tradeoffs for different application scenarios. OBMA_L supports faster lookups than OBMA_B at a small cost of update speed. OBMA_S achieves better storage efficiency than OBMA_B at a small cost of lookup throughput. We run our algorithms on a commodity CPU and evaluate them with real-world route tables and traces. The experiments show that OBMA achieves the lowest memory footprint, the highest update speed, and over 200 Mpps lookup throughput. Specifically, OBMA_S reduces the memory footprint to 3.98 bytes/prefix which is 25.33% smaller that of the state-of-the-art Poptrie;OBMA_L supports 252.02 Mpps lookup throughput with a single thread, and more than 600 Mpps with multiple parallel threads in a single CPU, significantly outperforming the state-of-the-art Poptrie and SAIL;OBMA_B supports updates at a rate of 14.58M updates/s which is 15 times faster than Poptrie. The tests show that the update process has little interference with the lookup process for OBMA, and achieves zero-interrupt to lookups with multiple threads.

关键词： Throughput Software algorithms Memory management Data structures Costs IP networks Graphics processing units filtering algorithms Instruction sets filtering theory IP lookup bitmap parallel techniques

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