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Proportionate Robust Diffusion Recursive Least Exponential Hyperbolic Cosine Algorithm: Optimum Parameter Selection and Convergence Analysis

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第2期70卷 831-835页

作者： Sadigh, Alireza Naeimi Zayyani, Hadi Semnan Univ Dept Comp Sci Semnan *** Iran Qom Univ Technol Dept Elect & Comp Engn Qom Iran

In this brief, an improved version of the proportionate robust diffusion recursive least exponential hyperbolic cosine algorithm is suggested. This improved version is obtained by optimally selecting the step-size parameter of this algorithm using a minimization of the squared norm of the error vector. Moreover, a complete theoretical analysis of the presented algorithm is performed. This theoretical analysis consists of mean-square convergence analysis, mean-square steady-state analysis, and a discussion on the forgetting factor parameter selection. Moreover, simulation experiments show that the improved algorithm is superior than the original algorithm and other state-of-the-art algorithms in the literature in terms of speed of convergence.

关键词： signal processing algorithms Convergence Cost function Steady-state Estimation Indexes Stability criteria Distributed estimation proportionate robust exponential hyperbolic cosine convergence analysis parameter estimation

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Spatiotemporal Learning via Mixture Importance Gaussian Filtering With Sparse Regularization

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IEEE signal processing LETTERS 2023年 30卷 279-283页

作者： Zhang, Hongwei Ye, Xiaoyu Hu, Qi Sun Yat Sen Univ Sch Aeronaut & Astronaut Shenzhen 518107 Peoples R China Nanjing Res Inst Elect Technol Nanjing 210012 Peoples R China

This letter proposes a novel spatiotemporal learning model via mixture importance Gaussian filtering (MIGF). In the MIGF, we explore the causal mapping between the target's true parameters and the latest measurement, and present an approach for elegantly combining the deterministic Gaussian-Hermite integral with the stochastic importance sampling method. This formulation allows for the use of soft-constrained and sparse regularization to reduce the truncation error and improve the weight adaptivity. We also provide an effective causal invariant updating rule to learn the parameters of this constrained dynamic model with a convergence guarantee. Experimental results show that MIGF can effectively eliminate the constraint uncertainty for the constrained dynamics.

关键词： Extraterrestrial measurements Spatiotemporal phenomena Estimation Noise measurement Uncertainty signal processing algorithms Probability density function Spatiotemporal learning causal mapping sparse regularization constraint uncertainty

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Quaternion Extreme Learning Machine Based on Real Augmented Representation

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IEEE signal processing LETTERS 2023年 30卷 175-179页

作者： Zhang, Huisheng Wang, Zaiqiang Chen, Dehao Zhu, Shuai Xu, Dongpo Dalian Maritime Univ Sch Sci Dalian 116026 Peoples R China Honor Network Ltd Co Beijing 101102 Peoples R China Northeast Normal Univ Sch Math & Stat Changchun 5268 Peoples R China

Widely linear modeling is an important quaternion signal processing technique for capturing the complete second-order statistics of quaternion signals. However, the algorithms based on widely linear modeling are computationally expensive due to the augmented variables and statistics. In this letter, a fast estimation technique based on a real augmented representation of widely linear modeling is proposed in the context of quaternion extreme learning machine with augmented hidden layer (QELMAH), resulting in a reduction of almost 93% of multiplications and 75% of additions. An equivalence between the proposed algorithm and the original QELMAH is theoretically established by proving that the trained networks with the proposed algorithm and the original one mathematically implement identical mapping. Such a technique is also applicable to the widely linear quaternion recursive least squares algorithm. The theoretical analysis and the effectiveness of the proposed algorithms are validated by simulations on two benchmark problems.

关键词： Quaternions signal processing algorithms Computational modeling Training Mathematical models Covariance matrices Estimation Quaternion extreme learning machine real augmented representation second-order statistics widely linear estimation

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MOV-Modified-FxLMS Algorithm With Variable Penalty Factor in a Practical Power Output Constrained Active Control System

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IEEE signal processing LETTERS 2023年 30卷 723-727页

作者： Lai, Chung Kwan Shi, Dongyuan Lam, Bhan Gan, Woon-Seng Nanyang Technol Univ NTU Sch Elect & Elect Engn Singapore 639798 Singapore

Practical Active Noise Control (ANC) systems typically require a restriction in their maximum output power, to prevent overdriving the loudspeaker and causing system instability. Recently, the minimum output variance filtered-reference least mean square (MOV-FxLMS) algorithm was shown to have optimal control under output constraint with an analytically formulated penalty factor, but it needs offline knowledge of disturbance power and secondary path gain. The constant penalty factor in MOV-FxLMS is also susceptible to variations in disturbance power that could cause output power constraint violations. This letter presents a new variable penalty factor that utilizes the estimated disturbance in the established Modified-FxLMS (MFxLMS) algorithm, resulting in a computationally efficient MOV-MFxLMS algorithm that can adapt to changes in disturbance levels in real-time. Numerical simulation with real noise and plant response showed that the variable penalty factor always manages to meet its maximum power output constraint despite sudden changes in disturbance power, whereas the fixed penalty factor has suffered from a constraint mismatch.

关键词： signal processing algorithms Finite impulse response filters Power generation Filtering algorithms Power amplifiers Optimal control Convergence Active noise control (ANC) output power constraint Index Terms minimum output variance (MOV) modified filtered reference least mean square (Modified-FxLMS) algorithm

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Performance Analysis of the Standard Constrained Maximum Versoria Criterion Based Adaptive Algorithm

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IEEE signal processing LETTERS 2023年 30卷 125-129页

作者： Abdelrhman, Omer M. Dou, Yuzi Li, Sen Dalian Maritime Univ Informat Sci & Technol Coll Dalian 116026 Peoples R China

Recently, robust adaptive filtering approaches relied on the Maximum Versoria Criterion (MVC) have gained the attention of researchers and have been widely studied. In this brief, with the energy conservation approach, transient and steady-state mean-square deviation (MSD) analysis of the standard constrained MVC (CMVC) are derived under both Gaussian and non-Gaussian noise distributions. For a Gaussian noise condition, an accurate solution is obtained, while for non-Gaussian noise conditions, we used approximate Taylor's expansion to derive the transient and steady-state MSD. Finally, we evaluated the theoretical analysis with some numerical simulations of system identification in different Gaussian and non-Gaussian noise scenarios to validate the finding.

关键词： Steady-state Standards Transient analysis signal processing algorithms Gaussian noise Adaptive filters Performance analysis Robust constrained filtering algorithm non-Gaussian noises gradient decent mean-square deviation

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Augmented Least Lncosh Conjugate Gradient Adaptive Filtering

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IEEE signal processing LETTERS 2023年 30卷 718-722页

作者： Zhang, Qiangqiang Lin, Dongyuan Xiao, Yingying Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

The online conjugate gradient (CG) algorithm with error-correction learning discards the error after each iteration, which shows low filtering accuracy in prediction tasks. Moreover, the CG based on the mean-square error (MSE) criterion suffers from poor performance when dealing with impulsive noise. To address these issues, we first construct a novel batched augmented least lncosh model (ALLM) based on the least lncosh (Llncosh) criterion by fully using the discarded errors. Then, a new structure of adaptive filters is designed under the framework of ALLM for online application. Using the least lncosh CG (LLCG) algorithm for the design of controller, a novel robust augmented LLCG (ALLCG) adaptive filter is finally developed, which achieves better filtering performance than the traditional CG and LLCG. Simulations demonstrate the advantages of ALLCG on robustness and prediction accuracy.

关键词： Prediction algorithms Training signal processing algorithms Adaptation models Testing Robustness Estimation Augmented space linear model conjugate gradient Index Terms error-correction learning least lncosh criterion

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Online Edge Flow Imputation on Networks

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IEEE signal processing LETTERS 2023年 30卷 115-119页

作者： Money, Rohan Krishnan, Joshin Beferull-Lozano, Baltasar Isufi, Elvin Univ Agder Dept ICT WISENET Lab N-4879 Grimstad Norway Simula Metropolitan Ctr Digital Engn SIGIPRO Dept N-0167 Oslo Norway Univ Agder WISENET Ctr Dept ICT N-4879 Grimstad Norway Delft Univ Technol Fac Elect Engn Math & Comp Sci NL- 2628 CD Delft Netherlands

An online algorithm for missing data imputation for networks with signals defined on the edges is presented. Leveraging the prior knowledge intrinsic to real-world networks, we propose a bi-level optimization scheme that exploits the causal dependencies and the flow conservation, respectively via (i) a sparse line graph identification strategy based on a group-Lasso and (ii) a Kalman filtering-based signal reconstruction strategy developed using simplicial complex (SC) formulation. The advantages of this first SC-based attempt for time-varying signal imputation have been demonstrated through numerical experiments using EPANET models of both synthetic and real water distribution networks.

关键词： signal processing algorithms Laplace equations Optimization Time series analysis signal reconstruction Reactive power Kalman filters Topological signal processing missing flow imputation line graph simplicial complex

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Long-Frame-Shift Neural Speech Phase Prediction With Spectral Continuity Enhancement and Interpolation Error Compensation

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IEEE signal processing LETTERS 2023年 30卷 1097-1101页

作者： Ai, Yang Lu, Ye-Xin Ling, Zhen-Hua Univ Sci & Technol China Natl Engn Res Ctr Speech & Language Informat Proc Hefei 230027 Peoples R China

Speech phase prediction, which is a significant research focus in the field of signal processing, aims to recover speech phase spectra from amplitude-related features. However, existing speech phase prediction methods are constrained to recovering phase spectra with short frame shifts, which are considerably smaller than the theoretical upper bound required for exact waveform reconstruction of short-time Fourier transform (STFT). To tackle this issue, we present a novel long-frame-shift neural speech phase prediction (LFS-NSPP) method which enables precise prediction of long-frame-shift phase spectra from long-frame-shift log amplitude spectra. The proposed method consists of three stages: interpolation, prediction and decimation. The short-frame-shift log amplitude spectra are first constructed from long-frame-shift ones through frequency-by-frequency interpolation to enhance the spectral continuity, and then employed to predict short-frame-shift phase spectra using an NSPP model, thereby compensating for interpolation errors. Ultimately, the long-frame-shift phase spectra are obtained from short-frame-shift ones through frame-by-frame decimation. Experimental results show that the proposed LFS-NSPP method can yield superior quality in predicting long-frame-shift phase spectra than the original NSPP model and other signal-processing-based phase estimation algorithms.

关键词： Interpolation signal processing algorithms Predictive models Prediction algorithms Estimation Training signal processing Error compensation interpolation neural network spectral continuity speech phase prediction

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Deep Image Registration With Depth-Aware Homography Estimation

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IEEE signal processing LETTERS 2023年 30卷 6-10页

作者： Huang, Chenwei Pan, Xiong Cheng, Jingchun Song, Jiajie Beihang Univ Inst Opt & Elect Beijing Peoples R China

Image registration is a basic task in computer vision, for its wide potential applications in image stitching, stereo vision, motion estimation, and etc. Most current methods achieve image registration by estimating a global homography matrix between candidate images with point-feature-based matching or direct prediction. However, as real-world 3D scenes have point-variant photograph distances (depth), a unified homography matrix is not sufficient to depict the specific pixel-wise relations between two images. Some researchers try to alleviate this problem by predicting multiple homography matrixes for different patches or segmentation areas in images;in this letter, we tackle this problem with further refinement, i.e. matching images with pixel-wise, depth-aware homography estimation. Firstly, we construct an efficient convolutional network, the DPH-Net, to predict the essential parameters causing image deviation, the rotation ($R$) and translation ($T$) of cameras. Then, we feed-in an image depth map for the calculation of initial pixel-wise homography matrixes, which are refined with an online optimization scheme. Finally, with the estimated pixel-specific homography parameters, pixel correspondences between candidate images can be easily computed for registration. Compared with state-of-the-art image registration algorithms, the proposed DPH-Net has the highest performance of 0.912 EPE and 0.977 SSIM, demonstrating the effectiveness of adding depth information and estimating pixel-wise homography into the image registration process.

关键词： Image registration Estimation Cameras Training signal processing algorithms Optimization Mathematical models Homography estimation image matching depth-aware homography pixel-wise image registration

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Beamforming Designs for Hybrid Relaying in mmWave Systems Based on Deep Unfolding

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IEEE signal processing LETTERS 2025年 32卷 2050-2054页

作者： Luo, Zhen Xie, Fu Zhang, Rongbin Liu, Hongqing Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Shantou Univ Dept Elect & Informat Engn Shantou 515063 Peoples R China

In this paper, an efficient hybrid beamforming scheme is proposed for the relay node in mmWave relay systems based on deep unfolding. The optimal digital beamformer at the relay node is decomposed into an analog-digital-analog (A-D-A) hybrid structure, which can be formulated as a matrix factorization problem. The analog beamforming matrices and digital beamforming matrix in the A-D-A hybrid structure are efficiently solved by a deep unfolding neural network, which is unfolded based on the projected gradient descent algorithm. Numerical results verify that performance of the proposed scheme is close to the fully-digital solution, which prevails traditional model-based algorithms with a fast runtime.

关键词： Relays Matrix decomposition Millimeter wave communication Array signal processing signal processing algorithms Artificial neural networks Transmitting antennas Receiving antennas Optimization Training mmWave hybrid beamforming relay deep unfolding

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