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Joint Learning Spatial-Temporal Attention Correlation Filters for Aerial Tracking

IEEE signal processing LETTERS 2024年 31卷 686-690页

作者： Zhao, Bo Ma, Sugang Zhao, Zhixian Zhang, Lei Hou, Zhiqiang Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Peoples R China Changan Univ Sch Informat Engn Xian 710064 Peoples R China Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent Pr Xian 710121 Peoples R China Xian Univ Posts & Telecommun Xian 710121 Peoples R China

Discriminative correlation filter (DCF)-based UAV tracking algorithms have drawn much attention due to their outstanding robustness and high computational efficiency. However, these algorithms are easily disturbed by background noise and abrupt changes in target appearance, leading to tracking failure. To address the issues above, we propose a real-time UAV object tracking algorithm with adaptive spatial-temporal attention. Specifically, we construct two filters with different roles based on the training sample's target foreground and environmental background. The spatial attention filter is implemented by incorporating a spatial context regularizer into the traditional DCF paradigm, which fully utilizes background environmental information to suppress background environmental noise and effectively distinguish between the target and the background. The temporal attention filter focuses on the continuity of the target samples, modeling only the target patch samples during the training process and introducing a temporal context regularizer, which substantially enhances the tracker's robustness against target occlusions and deformations. The two are jointly optimized by the Alternating Direction Method of Multipliers (ADMM) algorithm, which is mutually constrained during training and complemented during detection. Extensive experiments on three mainstream UAV benchmarks demonstrate the tracking advantages of the proposed algorithm.

关键词： Target tracking Filtering algorithms Information filters Training signal processing algorithms Correlation Autonomous aerial vehicles Discriminative correlation filter unmanned aerial vehicle temporal context regularization spatial context regularization dual regularization

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A Density Clustering-Based CFAR Algorithm for Ship Detection in SAR Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Li, Yang Wang, Zeyu Chen, Hongmeng Li, Yachao Beijing Univ Posts & Telecommun Sch Informat & Commun Engn State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Inst Radio Measurement Beijing 100854 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

The clutter selection strategy based on sliding window in the conventional constant false alarm rate (CFAR) algorithm leads to different clutter qualities between pixels of the same target in a complex environment. To solve the problem, this letter proposes an improved CFAR algorithm based on density clustering. First, a two-parameter CFAR is used to detect ship targets. Then, density clustering is performed on each detected target pixel based on spatial distance and detection threshold to improve the target detection accuracy. Finally, false alarms caused by speckle noise are eliminated by using the number of times a pixel is clustered. The experimental results show that compared with the conventional CFAR algorithm and the superpixel-level CFAR detectors for ship detection in synthetic aperture radar (SAR) imagery (SP-CFAR), the proposed algorithm achieves a detection accuracy improvement of over 14.8% in heterogeneous clutter scenarios and dense target scenarios, while maintaining a low false alarm rate no higher than 0.13% in strong noise environments.

关键词： Clustering algorithms Clutter Marine vehicles Radar polarimetry signal processing algorithms Detectors Noise Constant false alarm rate (CFAR) density clustering ship detection synthetic aperture radar (SAR)

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DMRA: An Adaptive Line Spectrum Estimation Method Through Dynamical Multiresolution of Atoms

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 1759-1774页

作者： Han, Mingguang Zeng, Yi Li, Xiaoguang Li, Tiejun Peking Univ Sch Math Sci Lab Math & Appl Math Beijing 100871 Peoples R China Hunan Normal Univ Sch Math & Stat MOE LCSM Changsha 410081 Hunan Peoples R China

We proposed a novel dense line spectrum super-resolution algorithm, the DMRA, that leverages dynamical multi-resolution of atoms technique to address the limitation of traditional compressed sensing methods when handling dense point-source signals. The algorithm utilizes a smooth tanh relaxation function to replace the l(0) norm, promoting sparsity and jointly estimating the frequency atoms and complex gains. To reduce computational complexity and improve frequency estimation accuracy, a two-stage strategy was further introduced to dynamically adjust the number of the optimized degrees of freedom. Theoretical analysis were provided to validate the proposed method for multi-parameter estimations. DMRA presents excellent performance for the super-resolution of cluster-sparse signals, which is a typical scenario in different practical applications. It also outperforms the state-of-the-art methods in terms of frequency estimation accuracy and computational efficiency.

关键词： Superresolution signal processing algorithms Atoms Dictionaries Heuristic algorithms Optimization Vectors Compressed sensing Spectral analysis Minimization relaxation of l(0)norm Line spectrum super-resolution dynamical multi-resolution dynamical multi-resolution

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Exploring the Impact of Bistatic Target Reflectivity in ISAC-Enabled V2V Setup Across Diverse Geometrical Road Layouts

IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY

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IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY 2025年 6卷 948-968页

作者： Khan, Aamir Ullah Mint, Saw James Shah, Syed Najaf Haider Schneider, Christian Robert, Joerg Tech Univ Ilmenau Elect Measurements & Signal Proc Grp D-98693 Ilmenau Germany

Integrated Sensing and Communication (ISAC) is an intriguing emerging research area that combines radar sensing and communication functionalities in a unified platform, capitalizing on shared aspects of signal processing, spectrum utilization, and system design. For sensing applications, the reflectivity of objects between Transmitter (TX) and Receiver (RX) is crucial. It is normally modeled as a uniform scatterer or a group of uniform scatterers in wireless channels. These models do not take into account the dependence of reflectivity on the aspect angles of incident and scattering waves, the composed material, and the geometry of the objects. Therefore, we model the reflectivity of target vehicles using their bistatic Radar Cross Section (RCS), as in radar sensing, within a Vehicle to Vehicle (V2V) setup under the Integrated Sensing and Communication (ISAC) framework. Moreover, we consider constant and variable bistatic Target Reflectivity (TR) integrated setups with two diverse traffic scenarios. These traffic scenarios are modeled to be realistic, with diverse geometrical road layouts, variable vehicle velocities, distinct vehicle positions, and the presence of Diffuse (DI) scattering components. Then, we inspect the impact of the bistatic TR on the behavior of the wireless channel and target detection capability. The variable TR integrated setup leads to a more accurate realization of the scenario, leading to outcomes that closely resemble real-world conditions. The results show the substantial impact of the geometrical setup on the distribution of TR, which emphasizes the need to integrate TR into ISAC-enabled V2V channel models.

关键词： Roads Channel models Radar Integrated sensing and communication Scattering Layout Wireless communication Reflectivity Object detection Radar cross-sections Autonomous driving channel models radar cross-section radar sensing radar clutter signal processing algorithms vehicle-to-vehicle communication

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On the Strong Convexity of PnP Regularization Using Linear Denoisers

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IEEE signal processing LETTERS 2024年 31卷 2790-2794页

作者： Sinha, Arghya Chaudhury, Kunal N. Indian Inst Sci Dept Computat & Data Sci Bangalore 560012 India Indian Inst Sci Dept Elect Engn Bangalore 560012 India

-In the Plug-and-Play (PnP) method, a denoiser is used as a regularizer within classical proximal algorithms for image reconstruction. It is known that a broad class of linear denoisers can be expressed as the proximal operator of a convex regularizer. Consequently, the associated PnP algorithm can be linked to a convex optimization problem P . For such a linear denoiser, we prove that P exhibits strong convexity for linear inverse problems. Specifically, we show that the strong convexity of P can be used to certify objective and iterative convergence of any PnP algorithm derived from classical methods.

关键词： signal processing algorithms Convergence Kernel Standards Convex functions Superresolution Inverse problems Indexes Image reconstruction Symmetric matrices proximal operator plug-and-play (PnP) regularization strong convexity

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Estimating Channels With Hundreds of Sub-Paths for MU-MIMO Uplink: A Structured High-Rank Tensor Approach

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IEEE signal processing LETTERS 2024年 31卷 2320-2324页

作者： Chen, Panqi Cheng, Lei Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Harbin Engn Univ Natl Key Lab Underwater Acoust Technol Harbin Peoples R China

This letter introduces a structured high-rank tensor approach for estimating sub-6G uplink channels in multi-user multiple-input and multiple-output (MU-MIMO) systems. To tackle the difficulty of channel estimation in sub-6G bands with hundreds of sub-paths, our approach fully exploits the physical structure of channel and establishes the link between sub-6G channel model and a high-rank four-dimensional (4D) tensor Canonical Polyadic Decomposition (CPD) with three factor matrices being Vandermonde-constrained. Accordingly, a stronger uniqueness property is derived in this work. This model supports an efficient one-pass algorithm for estimating sub-path parameters, which ensures plug-in compatibility with the widely-used baseline. Our method performs much better than the state-of-the-art tensor-based techniques on the simulations adhering to the 3GPP-R18 5G protocols.

关键词： Channel estimation Tensors Uplink Channel models signal processing algorithms Matrix decomposition Indexes MU-MIMO structured high-rank tensor decomposition uplink channel estimation

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RNN Beamforming Optimizer for Rate-Splitting Multiple Access and Cell-Free Massive MIMO

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第5期73卷 3579-3592页

作者： Johnston, Jeremy Wang, Xiaodong Columbia Univ Elect Engn Dept New York NY 10027 USA

Next-generation wireless technologies such as rate-splitting multiple access (RSMA) and massive MIMO are characterized by optimization problems too complex to solve in real-time, hence suboptimal heuristics are adopted in practice. As we explore in this paper, machine learning techniques have the potential to upend this paradigm, offering new algorithms customized for a particular distribution of problems. We consider MISO downlink beamforming optimization for NOMA, SDMA, and RSMA with sum rate and min rate criteria. We apply the framework of learning to optimize to learn an RNN optimizer that produces beamformers with much less computation than existing optimization algorithms such as weighted-MMSE. The RNN inference complexity scales linearly with the size of the antenna array and therefore is suitable for massive MIMO. We show that the learned optimizer is also compatible with a distributed beamforming scenario such as cell-free massive MIMO with information exchange facilitated by a central processor. Our simulation results show that the learned optimizer is competitive with state-of-the-art optimization methods, but requires a fraction of the computational cost.

关键词： Optimization Array signal processing NOMA Multiaccess communication Interference Decoding Massive MIMO Symbols signal processing algorithms Downlink Recurrent neural network (RNN) learning to optimize rate splitting multiple access (RSMA) cell-free massive MIMO downlink beamforming sum-rate max-min fairness

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Frequency Diverse RIS (FD-RIS) Enhanced Wireless Communications via Joint Distance-Angle Beamforming

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第12期13卷 3459-3463页

作者： Xiao, Han Hu, Xiaoyan Wang, Wenjie Wong, Kai-Kit Yang, Kun Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China UCL Dept Elect & Elect Engn London WC1E 7JE England Changchun Inst Technol Sch Comp Technol & Engn Changchun 130012 Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ England

The conventional reconfigurable intelligent surface (RIS) assisted far-field communication systems can only implement angle beamforming, which actually limits the capability for reconfiguring the wireless propagation environment. To overcome this limitation, this letter proposes a newly designed frequency diverse RIS (FD-RIS), which can achieve joint distance-angle beamforming with the assistance of the time modulation technology. The signal processing model for FD-RIS-aided wireless communications is first derived. Then, an optimization problem aimed at maximizing the achievable rate is formulated where the frequency-time modulations are jointly optimized to achieve distance-angle beamforming. Furthermore, a novel iterative algorithm based on the cross-entropy optimization (CEO) framework is proposed to effectively handle the non-convex optimization problem. The numerical results validate that the proposed FD-RIS assisted communication scheme can achieve a notable performance improvement compared with the baseline scheme utilizing traditional RIS. In addition, the effectiveness of the proposed CEO algorithm is further verified by comparing with the benchmark using the genetic algorithm (GA).

关键词： Array signal processing Frequency diversity Optimization Modulation Frequency modulation Time-frequency analysis signal processing algorithms Phased arrays Fourier series Massive MIMO Frequency diverse RIS (FD-RIS) time modulation distance-angle beamforming cross-entropy optimization (CEO)

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Widely Linear Momentum LMS Algorithm for Second Order Noncircular signals and Performance Analysis

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IEEE signal processing LETTERS 2024年 31卷 2235-2239页

作者： Shi, Wanting Xia, Yili Pei, Wenjiang Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China

In this letter, we propose a widely linear momentum least mean squares (WLMLMS) algorithm by incorporating the momentum term into the widely linear framework. This approach effectively handles noncircular signals and achieves faster convergence compared to the conventional augmented complex LMS, with minimal additional computational cost. Since the lag term of momentum complicates the analysis, we perform the Schur-Cohn test in z domain, and the direct bound without mixing the momentum factor is derived, for the first time, which further simplifies the parameter selection. Our derived expressions elucidate that, although augmented statistics integrate into mean-square error (MSE) iterations, the stability threshold is predominantly influenced by the covariance of the input signal and the momentum factor. Additionally, the upper bound does not exhibit a monotonic decrease with the momentum factor. Finally, the theoretical findings of this study are corroborated through numerical simulations.

关键词： Convergence Vectors Covariance matrices signal processing algorithms Steady-state Upper bound Standards Convergence condition second order noncircularity widely linear momentum LMS (WLMLMS)

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Simultaneous Deghosting Framework for Virtual-Shot Gathers Based on Advanced RED Inversion

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Hu, Bin Zhang, Junming Gong, Xiangbo Peng, Guangshuai Jilin Univ Coll GeoExplorat Sci & Technol State Key Lab Deep Earth Explorat & Imaging Changchun 130026 Peoples R China Jilin Univ Key Lab Geophys Explorat Equipment Minist Educ Changchun 130026 Peoples R China Sinopec Geophys Res Inst Co Ltd Nanjing 211103 Peoples R China

This study addresses the challenges in seismic interferometry (SI), particularly the deghosting of virtual-shot gathers affected by nonphysical reflections in marine seismic exploration. SI's effectiveness largely depends on the distribution of seismic sources, in which field situation often leads to complex wavefield interactions and nonphysical reflections, adversely impacting data processing. Traditional methods are limited with these nonphysical reflections, and similarity-based approaches have shown the potential in overcoming this challenge. This article introduces an innovative deghosting framework based on regularization by denoising (RED). This approach effectively mitigates the influence of nonphysical reflections and enhances the accuracy of deghosting. We propose a simultaneous multishot deghosting framework that integrates RED, focusing on optimizing data similarity and computational efficiency. This is complemented by a novel denoising engine based on an enhanced block matching and 3D filtering (BM3D) method, specifically tailored for virtual-shot gathers. The efficacy of our framework is demonstrated through synthetic and field data, showcasing significant improvements in seismic data processing and offering a new paradigm for the application of SI. This article contributes not only to the advancement of deghosting techniques, but also to broadening the scope of seismic exploration.

关键词： Noise Noise reduction Reflection Data processing Receivers Accuracy signal processing algorithms Interferometry Engines Delays Multishot simultaneous deghost regularization by denoising (RED)

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