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Non-Linear Gaussian Smoothing With Taylor Moment Expansion

IEEE signal processing LETTERS 2022年 29卷 80-84页

作者： Zhao, Zheng Sarkka, Simo Aalto Univ Dept Elect Engn & Automat Helsinki 02150 Finland

This letter is concerned with solvingcontinuous-discrete Gaussian smoothing problems by using the Taylor moment expansion (TME) scheme. In the proposed smoothing method, we apply the TME method to approximate the transition density of the stochastic differential equation in the dynamic model. Furthermore, we derive a theoretical error bound (in the mean square sense) of the TME smoothing estimates showing that the smoother is stable under weak assumptions. Numerical experiments show that the proposed smoother outperforms a number of baseline smoothers.

关键词： Smoothing methods Mathematical models signal processing algorithms Numerical models Approximation algorithms Stochastic processes Frequency modulation State-space models continuous-discrete filtering and smoothing non-linear filtering and smoothing stochastic differential equations Taylor moment expansion

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Unscented Kalman Filter With General Complex-Valued signals

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IEEE signal processing LETTERS 2022年 29卷 2023-2027页

作者： Zhang, Xing Xia, Yili Li, Chunguo Yang, Luxi Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210049 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China

For the estimation of real-valued Gaussian signals, the unscented Kalman filter (UKF) can provide a state estimate with second-order accuracy. However, when a general complex-valued system is considered, a direct extension of UKF from the real domain to the complex domain is inadequate, since the complementary covariance information associated with general improper complex-valued signals has been systematically ignored. To this end, in this work, we propose a general complex-valued unscented Kalman filter (GCUKF) algorithm which can be applied for both proper and improper signals. This is achieved by first proposing a novel sigma points selection scheme for the general complex-valued case, followed by a modified state update method to fully utilize both the innovation and its conjugate. A rigorous MSE analysis illustrates the superiority of the proposed state update method, and simulations support the analysis.

关键词： Estimation signal processing algorithms Kalman filters Technological innovation Approximation algorithms State-space methods Mathematical models State-space model general complex-valued unscented Kalman filter (GCUKF) noncircular

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Distributed Learning for Wireless Communications: Methods, Applications and Challenges

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2022年第3期16卷 326-342页

作者： Qian, Liangxin Yang, Ping Xiao, Ming Dobre, Octavia A. Di Renzo, Marco Li, Jun Han, Zhu Yi, Qin Zhao, Jiarong Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Sichuan Peoples R China Mem Univ Newfoundland St John NL A1B 3X9 Canada Royal Inst Technol S-11428 Stockholm Sweden Univ Paris Saclay Cent Supelec CNRS Lab Signaux & Syst F-91192 Gif Sur Yvette France Nanjing Univ Sci & Technol Nanjing 210094 Peoples R China Univ Houston Houston TX 77004 USA

With its privacy-preserving and decentralized features, distributed learning plays an irreplaceable role in the era of wireless networks with a plethora of smart terminals, an explosion of information volume and increasingly sensitive data privacy issues. There is a tremendous increase in the number of scholars investigating how distributed learning can be employed to emerging wireless network paradigms in the physical layer, media access control layer and network layer. Nonetheless, research on distributed learning for wireless communications is still in its infancy. In this paper, we review the contemporary technical applications of distributed learning for wireless communications. We first introduce the typical frameworks and algorithms for distributed learning. Examples of applications of distributed learning frameworks in emerging wireless network paradigms are then provided. Finally, main research directions and challenges of distributed learning for wireless communications are discussed.

关键词： Servers Distance learning Computer aided instruction Wireless communication Machine learning signal processing algorithms Computer architecture Distributed learning federated learning wireless communications

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Boosting Independent Component Analysis

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IEEE signal processing LETTERS 2022年 29卷 1367-1371页

作者： Li, YunPeng Ye, ZhaoHui Swiss Fed Inst Technol D ITET CH-8092 Zurich Switzerland Tsinghua Univ Dept Automat Beijing 100190 Peoples R China

Independent component analysis is intended to recover the mutually independent components from their linear mixtures. This technique has been widely used in many fields, such as data analysis, signal processing, and machine learning. To alleviate the dependency on prior knowledge concerning unknown sources, many nonparametric methods have been proposed. In this paper, we present a novel boosting-based algorithm for independent component analysis. Our algorithm consists of maximizing likelihood estimation via boosting and seeking unmixing matrix by the fixed-point method. A variety of experiments validate its performance compared with many of the presently known algorithms.

关键词： Boosting signal processing algorithms Mathematical models Maximum likelihood estimation Splines (mathematics) Complexity theory Source separation Independent component analysis boosting non- parametric maximum likelihood estimation fixed-point method

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Canonical Polyadic Decomposition via the Generalized Schur Decomposition

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IEEE signal processing LETTERS 2022年 29卷 937-941页

作者： Evert, Eric Vandecappelle, Michiel De Lathauwer, Lieven Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS B-3001 Leuven Belgium KU Leuven Kulak Grp Sci Engn & Technol B-8500 Kortrijk Belgium

The canonical decomposition (CPD) is a fundamental tensor decomposition which expresses a tensor as a sum of rank one tensors. In stark contrast to the matrix case, with light assumptions, the CPD of a low rank tensor is (essentially) unique. The essential uniqueness of CPD makes this decomposition a powerful tool in many applications as it allows for extraction of component information from a signal of interest. One popular algorithm for algebraic computation of a CPD is the generalized eigenvalue decomposition (GEVD) which selects a matrix subpencil of a tensor, then computes the generalized eigenvectors of the pencil. In this article, we present a simplification of GEVD which improves the accuracy of the algorithm. Surprisingly, the generalized eigenvector computation in GEVD is in fact unnecessary and can be replaced by a QZ decomposition which factors a pair of matrices as a product of unitary and upper triangular matrices. Computing a QZ decomposition is a standard first step when computing generalized eigenvectors, so our algorithm can been seen as a direct simplification of GEVD.

关键词： Tensors Matrix decomposition signal processing algorithms Eigenvalues and eigenfunctions Standards Optimization Indexes CPD GEVD multilinear algebra QZ tensors

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Fast Adaptive Local Subspace Learning With Regressive Regularization

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IEEE signal processing LETTERS 2022年 29卷 1759-1763页

作者： Chen, Qiang Zhao, Xiaowei Nie, Feiping Wang, Rong Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Sch Comp SciMinist Ind & Informat Technol Sch Artificial Intelligence Opt & Elect iOPEN Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Minist Ind & Informat Technol Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China

Linear Discriminant Analysis (LDA) has been widely used in supervised dimensionality reduction fields. However, LDA is usually weak in tackling data with Non-Gaussian distribution due to its incapability of extracting the intrinsic structure of data. In order to learn the intrinsic information more effectively, some dimensionality reduction methods incorporate the adaptive full-connected graph into the algorithm frame, but the defect is that the calculation of each pairwise distance is very time-consuming. In this letter, we propose a novel fast adaptive local subspace learning with regressive regularization model to solve the supervised dimensional reduction problem. Firstly, the adaptive anchor point graph is used to capture local structure information, which can greatly reduce computation complexity. Secondly, by using regressive regularization, the samples from different classes can be better separated in the projected space and the workload of selecting the optimal reduced dimension is easier. Moreover, entropy regularization is used to derive more appropriate weights. Finally, extensive experiments are conducted on real world data sets to verify the superiority of our model.

关键词： Adaptation models Dimensionality reduction signal processing algorithms Data models Optics Entropy Data mining Adaptive anchor point graph fast local subspace learning linear discriminant analysis regressive regularization

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Relationship Between Criteria Based on Correntropy and Second Order Statistics for Equalization of Communication Channels

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IEEE signal processing LETTERS 2022年 29卷 1317-1321页

作者： Gois, Lucas Suyama, Ricardo Fantinato, Denis Neves, Aline Fed Univ ABC BR-09210580 Santo Andre SP Brazil

Correntropy has been successfully used for signal processing in a large variety of applications. Usually, correntropy estimator uses a Gaussian kernel, which leads to a measure that takes into account all even order statistical moments of the underlying signal. In this paper we analyze correntropy implemented with the Epanechnikov kernel, which is given by a second order polynomial. Considering an equalization scenario, we compare such criterion with the one obtained through the use of the Gaussian kernel and also the Correlation Retrieval Criterion. We discuss similarities and differences, theoretically and through simulations, between the three criteria.

关键词： Kernel Equalizers Cost function Delays signal processing algorithms Estimation Binary phase shift keying Correntropy correlation retrieval Epanechnikov kernel second order statistics

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Generalized Spatial Modulation in Full-Duplex Two-Way Relay Channel

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IEEE COMMUNICATIONS LETTERS 2022年第8期26卷 1918-1922页

作者： Shafrin, K. S. Rajamohan, Neelakandan Indian Inst Technol Sch Elect Sci Veling 403401 Goa India

In this letter, we propose generalized spatial modulation (GSM) in full duplex system equipped with two way relay channel (FD-GSM-TWRC). We propose a couple of receiver algorithms, structured complex orthogonal matching pursuit (S-COMP) and structured ordered block OMP (S-OBOMP) by casting the observation model suitably to apply compressive sensing framework. We substantiate that FD-GSM-TWRC with the proposed receiver algorithms, offer an optimal trade-off among bit-error rate (BER) performance, throughput and complexity.

关键词： Relays Transmitting antennas Matching pursuit algorithms Symbols Receiving antennas Throughput signal processing algorithms Two way relay channel full duplex spatial modulation compressive sensing

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Identification of Power System Oscillation Modes Using Empirical Wavelet Transform and Yoshida-Bertecco Algorithm

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IEEE ACCESS 2022年 10卷 48927-48935页

作者： Philip, Joice G. Yang, Yejin Jung, Jaesung Ajou Univ Dept Energy Syst Res Suwon 16499 South Korea

Oscillations occurring in the power system are one of the biggest threats to its secure operation. Although they occur rarely, these oscillations can cause severe damage to the power system if they are not detected at the earliest. Hence, this work focuses on identifying the parameters of oscillations in the power system using a combination of Empirical Wavelet Transform and Yoshida-Bertecco algorithm. As these oscillations occur rarely, a preprocessing method based on Teager Kaiser Energy Operator is used to check whether the signal under consideration contains any oscillation modes. The effectiveness of the proposed method is tested using a test signal, simulated power system signal, and PMU data from an actual power system under various levels of noise contamination. Further, the performance of the proposed method is compared with a VMD-Hilbert transform-based, Prony-based, and SSI-based methods in the literature. Results reveal the superiority of the proposed method irrespective of the parameters of the signal under consideration.

关键词： Oscillators Power systems Wavelet transforms Phasor measurement units Wavelet analysis signal to noise ratio signal processing algorithms Empirical wavelet transform power system oscillations Prony's method stochastic subspace identification Teager Kaiser energy operator variational mode decomposition

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Deep Constraints Space via Channel Alignment for Visible-Infrared Person Re-identification

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IEEE signal processing LETTERS 2022年 29卷 2672-2676页

作者： Qin, Wencheng Huang, Baojin Huang, Zhiyong Tahsin, Lamia Sun, Daming Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Engn Res Ctr Med Elect & informat techno Chongqing 400044 Peoples R China Univ Asia Pacific Dept Comp Sci & Engn Dhaka 1205 Bangladesh

Reducing the inter-modality gap has been the core of visible-infrared person re-identification (VI-ReID). Most of the existing methods directly constrain the features to reduce the gap between the different modalities. However, the consistency of channel semantic information across different modalities is ignored. In this letter, channel alignment along with severe semantic constraints is proposed to alleviate the significant distribution differences between modalities. To obtain the optimum channel matching mode, a novel channel alignment mechanism termed Channel Instance Level Alignment (CILA) is proposed at the shallow layer, and matched channel features are constrained by the proposed Channel Instance Alignment (CIA) Loss. In the middle layer, we divide the features into multiple hierarchically-aware channel clusters and align the channel clusters by Channel Cluster Alignment (CCA) Loss. The proposed method is validated on SYSU-MM01 and RegDB, and extensive experiments show that the proposed method achieves competitive performance compared with the state of the arts.

关键词： Semantics Residual neural networks signal processing algorithms Training Indexes Clustering algorithms Task analysis Visible-Infrared person re-identification channel alignment hierarchically-aware channel clusters inter-modality

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