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A Delta Operator State Estimation Algorithm for Discrete-Time Systems With State Time-Delay

IEEE signal processing LETTERS 2025年 32卷 391-395页

作者： Xu, Ling Zhang, Xiao Ding, Feng Zhu, Quanmin Changzhou Univ Sch Microelect & Control Engn Changzhou 213159 Peoples R China Jiangnan Univ Sch Internet Things Engn Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Univ West England Dept Engn Design & Math Bristol BS16 1QY England

This letter presents a state estimation algorithm for linear discrete-time systems with state-delay. In order to overcome the difficulty that the traditional Kalman filter cannot estimate the states of the systems with state-delay, a state estimation strategy is developed by combining the auxiliary model with the delta operator. Then, by constructing and minimizing the covariance matrix of the state reconstruction errors, a delta operator state estimation algorithm is derived and it can fulfill effective state estimation for the linear discrete-time system with state-delay. Moreover, the convergence proof is provided by means of stochastic stability theory. Finally, the experimental results demonstrate that the developed state estimation method is effective.

关键词： State estimation Vectors signal processing algorithms Mathematical models Delays Covariance matrices Convergence Stochastic processes Nonlinear systems Kalman filters Kalmam filtering state-delay state estimation signal processing

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Onset-and-Offset-Aware Sound Event Detection via Differentiable Frame-to-Event Mapping

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IEEE signal processing LETTERS 2025年 32卷 186-190页

作者： Yoshinaga, Tomoya Tanaka, Keitaro Bando, Yoshiaki Imoto, Keisuke Morishima, Shigeo Natl Inst Adv Ind Sci & Technol Tokyo 1350064 Japan Waseda Univ Tokyo 1698555 Japan Doshisha Univ Kyoto 6028580 Japan

This paper presents a sound event detection (SED) method that handles sound event boundaries in a statistically principled manner. A typical approach to SED is to train a deep neural network (DNN) in a supervised manner such that the model predicts frame-wise event activities. Since the predicted activities often contain fine insertion and deletion errors due to their temporal fluctuations, post-processing has been applied to obtain more accurate onset and offset boundaries. Existing post-processing methods are, however, non-differentiable and prohibit end-to-end (E2E) training. In this paper, we propose an E2E detection method based on a probabilistic formulation of sound event sequences called a hidden semi-Markov model (HSMM). The HSMM is utilized to transform frame-wise features predicted by a DNN into posterior probabilities of sound events represented by their class labels and temporal boundaries. We jointly train the DNN and HSMM in a supervised E2E manner by maximizing the event-wise posterior probabilities of the HSMM. This objective is a differentiable function thanks to the forward-backward algorithm of the HSMM. Experimental results with real recordings show that our method outperforms baseline systems with standard post-processing methods.

关键词： Training Posterior probability Artificial neural networks Probabilistic logic Predictive models Filtering Event detection Fluctuations signal processing algorithms Bayes methods End-to-end training hidden semi-Markov model (HSMM) neural probabilistic modeling sound event detection (SED)

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Near-Field Sensing: A Low-Complexity Wavenumber-Domain Positioning Method

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第4期14卷 994-998页

作者： Jiang, Hao Wang, Zhaolin Liu, Yuanwei Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Kyung Hee Univ Dept Elect Engn Yongin 17104 Gyeonggi Do South Korea

A low-complexity wavenumber-domain positioning method is proposed for near-field sensing. Specifically, in the wavenumber domain, the power-concentrated region is sparse and closely related to the target's position. However, this relationship is complex and implicit. To address this, a bi-directional convolutional neural network (BiCNN) architecture is employed to capture the underlying relationship, enabling low-complexity, gridless target positioning. The simulation results reveal that the BiCNN method significantly reduces the computational complexity compared to the existing on-grid multiple signal classification (MUSIC) algorithm while achieving high accuracy.

关键词： Location awareness Vectors Antennas Antenna arrays Sensors Multiple signal classification signal processing algorithms Radio frequency Noise Accuracy Deep learning near-field sensing (NISE) target localization

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Sensing Within Ultra-Short Duration: Extended Subspace algorithms With Insufficient Snapshots

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IEEE signal processing LETTERS 2025年 32卷 1490-1494页

作者： Ma, Teng Feng, Yuxuan Xiao, Yue Lei, Xia Poulkov, Vladimir Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 611731 Peoples R China Tech Univ Sofia Fac Telecommun Sofia 1000 Bulgaria

In pursuit of real-time sensing within ultra-short duration, conventional sensing algorithms are gradually failing to fulfill the stringent latency demands. Specifically, traditional subspace-based methods such as multiple signal classification (MUSIC) are hindered by their need for an extensive number of snapshots to accumulate the rank of the spatial covariance matrix (SCM), resulting in poor real-time performance. Moreover, advanced techniques like compressed sensing and machine learning are constrained by requirements for high signal sparsity or suffer from limited generality. To handle these challenges, this paper proposes an innovative extension of subspace theory tailored to insufficient-snapshot scenarios, leveraging the concept of spatio-temporal exchangeability. Based on the defined spatio-temporal correlation predicated on the space translation invariance characteristic of uniform linear arrays, we engineer a pseudo SCM that inherently possesses sufficient rank. This methodology not only resolves the rank-deficiency issue but also fully exploits the array aperture and significantly reduces the noise level. Simulation results are presented, substantiating the feasibility and enhanced performance of the proposed algorithms, marking a significant advancement over existing methodologies.

关键词： Estimation Multiple signal classification Sensors signal processing algorithms Real-time systems Training Matrix converters Eigenvalues and eigenfunctions Classification algorithms Translation Angle estimation spatio-temporal exchangeability multiple signal classification (MUSIC) insufficient snapshots

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Enhanced Batch Adaptive Filter Based on Fractional-Order Generalized Cauchy Kernel Loss

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IEEE signal processing LETTERS 2025年 32卷 1201-1205页

作者： Cui, Mingjing Jiang, Yunxiang Lin, Dongyuan Wang, Shiyuan He, Fuliang Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Changsha Jinwei Integrated Circuit Co Ltd Changsha 410000 Peoples R China

Adaptive filters utilizing the low-order moments hidden in robust loss functions have achieved desirable performance under Gaussian input and impulsive noises. However, when the input cannot be modeled by Gaussian process and is simultaneously contaminated by outliers, these filters may suffer from misalignment. To this end, applying fractional-order calculus in stochastic gradient descent method, this letter proposes a fractional-order generalized Cauchy kernel loss (FoGCKL) algorithm to model complex alpha-stable process input. The mean square deviation (MSD) is calculated to evaluate the steady-state performance of FoGCKL. To further avoid steady-state jitters and improve filtering accuracy, an enhanced batch method is constructed in FoGCKL using optimized weighted term, generating another enhanced batch FoGCKL (EBFoGCKL) algorithm. Simulations on system identification verify the correctness of theoretical analysis and demonstrate the superiorities of FoGCKL and EB-FoGCKL.

关键词： Steady-state Noise signal processing algorithms Accuracy Kernel Filters Gaussian processes Convergence Adaptation models Training Fractional-order filter symmetric alpha-stable process enhanced batch method steady-state analysis

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Maximum-Likelihood Active Device Detection and Channel Estimation in One-Bit MIMO System

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IEEE signal processing LETTERS 2025年 32卷 141-145页

作者： Ge, Mingye Liu, Yatao Shao, Mingjie Zhang, Haixia Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China Shanghai Huawei Technol Co Ltd Shanghai 200000 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Shandong Univ Shandong Key Lab Intelligent Commun & Sensing Comp Jinan 250061 Peoples R China

The future machine-type communication in internet-of-things (IoT) systems involves a massive number of devices sporadically communicating with a base station (BS) equipped with multiple antennas. Detecting active devices and estimating their associated channels are crucial but challenging due to the large number of potential devices and the small fraction of active devices. Existing studies assume high-resolution analog-to-digital converters (ADCs) at the BS, while there is a growing interest in implementing low-resolution ADCs, particularly one-bit ADCs, in massive multiple-input multiple-output (MIMO) systems. This paper focuses on the joint one-bit active device detection and channel estimation problem. We consider the maximum-likelihood approach and propose a novel expectation maximization (EM) algorithm with acceleration. On the theoretical aspect, we provide the convergent computational complexity analysis for the accelerated EM algorithm. The proposed method, evaluated through numerical simulations, outperforms benchmark algorithms in terms of both estimation accuracy and computational complexity.

关键词： Channel estimation Maximum likelihood estimation signal processing algorithms Convergence Computational complexity Maximum likelihood detection Quantization (signal) Optimization Electronic mail Antennas Active device detection channel estimation maximum likelihood expectation-maximization

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Independent Components Time-Frequency Purification With Channel Consensus Against Adversarial Attack in SSVEP-Based BCIs

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IEEE signal processing LETTERS 2025年 32卷 116-120页

作者： Yi, Hangjie Qian, Jingsheng Ming, Yuhang Kong, Wanzeng Dianzi Univ Sch Comp Sci Key Lab Brain Machine Collaborat Intelligence Zhej Hangzhou 310005 Peoples R China

The Steady State Visual Evoked Potential (SSVEP) paradigm has been widely employed in various Brain-Computer Interface (BCI) systems. However, recent studies indicate that SSVEP is vulnerable to adversarial attacks, resulting in manipulated results and drastic degradation in recognition performance, which pose inconveniences and even risks to users. Noticing the fact that the adversarial attack on SSVEP is done by adding subtle waveform perturbations into random EEG channels, we propose Independent Components Time-Frequency Purification with Channel Consensus (ICTFP-CC) as a defensive strategy. In particular, we first detect and remove suspicious perturbations with independent component analysis from the time and frequency domain, and then reconstruct the purified EEG signals. Additionally, we introduce a voting mechanism to achieve channel consensus and enhance overall robustness. We conducted experiments on two public datasets and three SSVEP recognition algorithms. The results demonstrate that our method can significantly improve the classification accuracy and information transfer rate of attacked SSVEP signals by a maximum of 46.79 (%) and 62.87 (bits/min).

关键词： Perturbation methods Electroencephalography Brain modeling Time-frequency analysis Purification signal processing algorithms Transforms Correlation Computational modeling Benchmark testing Adversarial attack electroencephalogram purification steady state visual evoked potential (SSVEP)

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Full-Duplex Beyond Self-Interference: The Unlimited Sensing Way

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IEEE COMMUNICATIONS LETTERS 2025年第1期29卷 165-169页

作者： Liu, Ziang Bhandari, Ayush Clerckx, Bruno Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

The success of full-stack full-duplex communication systems depends on how effectively one can achieve digital self-interference cancellation (SIC). Towards this end, in this letter, we consider unlimited sensing framework (USF) enabled full-duplex system. We show that by injecting folding non-linearities in the sensing pipeline, one can not only suppress self-interference but also recover the signal of interest (SoI). This approach leads to novel design of the receiver architecture that is complemented by a modulo-domain channel estimation method. We then demonstrate the advantages of M-lambda -ADC by analyzing the relationship between quantization noise, quantization bits, and dynamic range. Numerical experiments show that the USF enabled receiver structure can achieve up to 40 dB digital SIC by using as few as 4-bits per sample. Our method outperforms the previous approach based on adaptive filters when it comes to digital SIC performance, SoI reconstruction, and detection.

关键词： Interference cancellation Channel estimation Quantization (signal) Receivers Noise Hardware Uplink signal processing algorithms Full-duplex system signal resolution Digital self-interference cancellation full duplex unlimited sampling quantization noise

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Modified PUMA/EPUMA Based on Forward and Backward Linear Prediction for DOA Estimation

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Ma, Biyun Zhu, Fu Wang, Yide Zhu, Qingqing Liu, Jiaojiao South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China Minist Nat Resources Key Lab Marine Environm Survey Technol & Applicat Guangzhou 510300 Peoples R China Nantes Univ Inst Elect & Technol NumeR IETR CNRS UMR 6164 F-44306 Nantes France

The principal-singular-vector utilization for modal analysis (PUMA) and its modification (Mod-PUMA), which utilize forward linear prediction (FLP) to process the signal subspace, experience significant performance degradation if there are multiple coherent sources and such a performance degradation will be further aggravated in low-SNR regions, which is primarily attributed to the outliers arising from inaccurate estimations of the signal subspace. To address these issues, we propose an extension version of PUMA-related algorithms, called FBLP-Mod-PUMA/enhanced-PUMA (EPUMA). The proposed algorithms improve the threshold performance by refining the signal subspace through forward and backward linear prediction (FBLP), effectively mitigating subspace leakage when dealing with coherent sources. The number of resolvable coherent sources has been theoretically derived and simulation results are provided to show the performance of the proposed algorithms.

关键词： Covariance matrices Eigenvalues and eigenfunctions Vectors Prediction algorithms Direction-of-arrival estimation Smoothing methods Simulation signal to noise ratio signal processing algorithms Sensor arrays Coherent sources direction-of-arrival (DOA) estimation forward and backward linear prediction (FBLP) principal-singular-vector utilization for modal analysis (PUMA)/enhanced-PUMA (EPUMA)

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Multiple Dimensional Correntropy Kalman Filter

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IEEE signal processing LETTERS 2025年 32卷 1011-1015页

作者： Wang, Guoqing Zhu, Zhaolei Yang, Chunyu Ma, Lei China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

The letter addresses the robust state estimation problem of non-Gaussian systems disturbed by outliers. Unlike the existing correntropy-based state estimation framework, which uses a uniform weight for the evaluated error vectors and solely relies on inaccurate nominal covariance matrices for estimating the system state, this work proposes a novel maximum-correntropy Kalman filter. This new approach utilizes multiple dimensional correntropy to assess the similarity between vectors across different dimensions. Additionally, it adjusts the covariance matrices simultaneously by utilizing the adopted matrix similarity measure within the modified correntropy framework. Simulations on target tracking demonstrate that our proposed algorithm exhibits excellent estimation accuracy and robustness while possessing adaptive capability even under time-varying heavy-tailed noises.

关键词： Vectors Noise Covariance matrices signal processing algorithms Kernel Pollution measurement Target tracking Noise measurement Kalman filters Accuracy Multiple dimension correntropy non-Gaussian noise robust Kalman filter covariance estimation

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