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Adaptive Attention Based on Mixture Distribution for Zero-Shot Non-Line-of-Sight Imaging

IEEE signal processing LETTERS 2025年 32卷 1690-1694页

作者： Zhang, Qinghua Liu, Jun Duan, Yuping Beijing Normal Univ Sch Math Sci Lab Math & Complex Syst Minist Educ China Beijing 100875 Peoples R China

Non-line-of-sight (NLOS) imaging is an ill-posed problem to reconstruct hidden 3D scenes by leveraging photon time-of-flight information from diffusely reflected light. In the existing regularization models, the spatial residuals were handled by a single distribution, failing to account for the distinct characteristics of background and target objects. In this paper, we propose a novel NLOS reconstruction method that models the non-Gaussian residuals with a mixture distribution. Through a dual method, we derive an adaptive weighted residual model, where the weights generated in the dual space act as a zero-shot attention mechanism to control the contributions of different regions. The corresponding optimization problem can be effectively solved using the alternating minimization algorithm. Numerical experiments on both synthetic and real-world datasets demonstrate that our method surpasses the related existing approaches, achieving state-of-the-art performance.

关键词： Image reconstruction Imaging Gold Three-dimensional displays Attention mechanisms Vectors Probability density function Training signal processing algorithms Lighting Non-line-of-sight imaging mixture distribution duality theory attention mechanism

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SCA and IBCD Hybrid Algorithm Based Secure Beamforming Optimization for IRS-Assisted Multiuser CR-SWIPT System

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IEEE signal processing LETTERS 2025年 32卷 336-340页

作者： Xu, Xiaorong Yang, Shuo Liu, Zhaoting Wu, Jun Bao, Jianrong Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China

This letter investigates the design and optimization of secure beamforming in an intelligent reflecting surface (IRS)-assisted multiuser cognitive radio simultaneous wireless information and power transfer (CR-SWIPT) system. The proposed method leverages IRS to address cognitive energy harvesting nodes as potential eavesdroppers (Eve). The objective is to maximize the achievable secrecy rate while satisfying multiple constraints, such as transmit power control, energy harvesting, phase shifts, and maximum tolerable interference power. To solve this highly non-convex optimization problem, we propose a hybrid algorithm that combines successive convex approximation (SCA) and inexact block coordinate descent (IBCD). By decomposing the problem into three sub-problems, local optimal beamforming matrices and phase-shift matrices are obtained using the SCA method and complex circle manifold (CCM) method, respectively. Simulation results show that the secrecy rate at the SWIPT information decoding node in the IRS-assisted multiuser CR-SWIPT system improves significantly, with an approximate 40% enhancement compared to the random phase shift scheme with maximum transmit power. The effectiveness of the proposed algorithm is further validated through secrecy rate performance evaluations under different system configurations.

关键词： Array signal processing Optimization Energy harvesting Interference Decoding signal processing algorithms Computational complexity Transmitting antennas Three-dimensional displays Receiving antennas Intelligent reflecting surface (IRS) CR-SWIPT system secure beamforming optimization successive convex approximation (SCA) inexact block coordinate descent (IBCD)

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Generating Image Counterfactuals in Deep Learning Models Without the Aid of Generative Models

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IEEE signal processing LETTERS 2025年 32卷 1495-1499页

作者： Xu, Ao Li, Zihao Zhang, Yukai Wu, Tieru Jilin Univ Sch Artificial Intelligence Changchun 130000 Peoples R China

With the rapid development of artificial intelligence, particularly the rise of deep learning, the importance of Explainable Artificial Intelligence has become increasingly prominent. Among its key techniques, counterfactual explanation plays a crucial role in understanding the decision-making mechanisms of opaque models. However, the high dimensionality and complex feature patterns of image data pose significant challenges for the task of generating counterfactuals for images. Existing literature has proposed various algorithms based on different assumptions, many of which rely on the existence of appropriate generative models. Some of these assumptions, particularly the assumption regarding the existence of generative models, may be overly stringent. To address this issue, this letter introduces a novel assumption-free image counterfactual generation algorithm, DFO-S, based on Score Matching and gradient-free optimization techniques. The proposed method achieves high-quality counterfactual generation without relying on generative models. Through extensive empirical analysis, we demonstrate the significant superiority of our method in terms of performance.

关键词： signal processing algorithms Artificial intelligence Closed box Deep learning Vectors Optimization Data models Computational modeling Training Smoothing methods explainable artificial intelli- gence image counterfactual explanation

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Follow the Approximate Sparse Leader for No-Regret Online Sparse Linear Approximation

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IEEE signal processing LETTERS 2025年 32卷 951-955页

作者： Mukhopadhyay, Samrat Mukherjee, Debasmita Indian Sch Mines Dhanbad Indian Inst Technol Dept Elect Engn Dhanbad 826004 India

We consider the problem of online sparse linear approximation, where a learner sequentially predicts the best sparse linear approximations of an as yet unobserved sequence of measurements in terms of a few columns of a given measurement matrix. The inherent difficulty of offline sparse recovery makes the online problem challenging as well. In this letter, we propose Follow-The-Approximate-Sparse-Leader, an efficient online meta-policy to address this online problem. Through a detailed theoretical analysis, we prove that under certain assumptions on the measurement sequence, the proposed policy enjoys a data-dependent sublinear upper bound on the static regret, which can range from logarithmic to square-root. Extensive numerical simulations are performed to corroborate the theoretical findings and demonstrate the efficacy of the proposed online policy.

关键词： Vectors signal processing algorithms Optimization Linear approximation Computational complexity Numerical models Loss measurement Approximation algorithms Upper bound Training Sparse approximation static regret follow-the-leader (FTL)

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DoA Estimation for Hybrid Receivers: Full Spatial Coverage and Successive Refinement

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 4730-4744页

作者： Abdelbadie, Ali Mostafa, Mona Bameri, Salime Gohary, Ramy H. Thomas, Dimple Ciena Canada Inc Ottawa ON K2K 3E8 Canada Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada Ericsson Canada Inc Ottawa ON K2K 2V6 Canada

We develop two novel algorithms for estimating the direction of arrival (DoA) of multiple sources in fully-connected and partially-connected hybrid analog/digital (HAD) receivers. The first algorithm is based on the observation that the analog combiner projects received signals on a particular subspace, causing the signals corresponding to particular DoAs to be heavily attenuated. Thus, an analog combiner defines spatial sectors, beyond which the DoAs are practically undetectable. To address this difficulty, we perform DoA estimation over an exhaustive set of analog combiners spanning distinct subspaces. To refine the estimates generated by this algorithm, we develop an exponentially-converging algorithm wherein the search window is successively narrowed until convergence. Cram & eacute;r-Rao lower bounds on the root-mean-square error of the proposed algorithms are derived and the superiority of these algorithms over their existing counterparts is established through numerical simulations.

关键词： Direction-of-arrival estimation signal processing algorithms Radio frequency Estimation Receiving antennas Antennas signal to noise ratio DoA estimation large-scale antenna array hybrid analog/digital architectures DFT matrix CRLB

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A Real-Time Subaperture Preprocessing for Multireceiver Wide-Beam SAS Imaging

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Zhang, Jiafeng Cheng, Guangli Tang, Jinsong Wu, Haoran Naval Univ Engn Inst Elect Engn Wuhan 430000 Peoples R China

The multireceiver synthetic aperture sonar (SAS) data are usually converted to equivalent monostatic data through the displaced phase center approximation (DPCA) before the monostatic imaging. However, the DPCA error is azimuth-variant in the wide-beam case, resulting in the traditional algorithms compensating for the DPCA error in the extended Doppler domain of each receiver, which obviously increases the computational complexity. To solve the problem, this letter analyzes the space-variant characteristics of the DPCA error and discovers that the DPCA error exhibits significant receiver-variant and weak azimuth-variant characteristics. Based on this, a subaperture preprocessing is proposed to reduce computational complexity without sacrificing imaging accuracy. The proposed algorithm compensates for the DPCA error uniformly within the subaperture using the DPCA error at the center of the subaperture and then superposes all subapertures coherently to obtain equivalent monostatic data. The weak azimuth-variant characteristic ensures that the subapertures are very sparse, Additionally, the algorithm allows data recording and preprocessing to be synchronized, significantly reducing the imaging waiting time further. The simulated data and actual data experiments verify the effectiveness of the proposed algorithm.

关键词： Receivers Synthetic aperture sonar signal processing algorithms Imaging Doppler effect Azimuth Approximation algorithms Apertures Transmitters Geoscience and remote sensing Azimuth-variant characteristic displaced phase center approximation (DPCA) preprocessing real time subaperture synthetic aperture sonar (SAS) wide-beam

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Low-Complexity SVD Precoding for Faster-Than-Nyquist signaling Using High-Order Modulations

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第1期60卷 591-603页

作者： Li, Qiang Li, Liping Li, Yingsong Han, Wenjing Li, Xingwang da Costa, Daniel Benevides Anhui Univ Sch Elect & Informat Engn Hefei 230601 Peoples R China Henan Polytech Univ Sch Phys & Elect Informat Engn Jiaozuo 454000 Peoples R China Technol Innovat Inst Abu Dhabi U Arab Emirates

To address the low spectrum efficiency of satellite communications, faster-than-Nyquist (FTN) signaling has proven a promising technology to improve the spectrum efficiency without requiring additional bandwidth or antennas. However, FTN signaling violates the Nyquist criterion, thereby resulting in intersymbol interference (ISI). While existing singular value decomposition (SVD) precoding can eliminate FTN-induced ISI, it ignores interblock interference, leading to low estimation accuracy. Besides, existing SVD precoding requires high complexity due to the lack of efficient and convenient implementation method. Replacing the linear convolution in FTN shaping by the circular convolution, we construct the circular FTN (CFTN) signaling and propose a CFTN-SVD precoding, which offers several advantages over the existing SVD precoding. First, the proposed CFTN-SVD precoding does not require the transmitter to acquire any accurate information about the receiver, streamlining the transmission process. Second, the proposed CFTN-SVD precoding is designed with low implementation complexity, leveraging fast Fourier transform (FFT) and inverse FFT (IFFT) to facilitate the practical implementation. Last but not least, the proposed CFTN-SVD precoding takes all FTN-induced ISI into consideration, resulting in improved estimation accuracy and making it suitable for high-order modulations. Simulation results show that compared with the bit error rate (BER) of Nyquist signaling, the BER loss of the proposed CFTN-SVD precoding is about 0.8 and 0.25 dB for uncoded and coded FTN signaling, respectively, when adopting 256-amplitude phase shift keying.

关键词： Precoding Symbols Complexity theory Estimation Bit error rate signal processing algorithms Modulation Faster-than-Nyquist (FTN) intersymbol interference (ISI) precoding satellite communication spectrum efficiency

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Low-Rank EM-Based Imaging for Large-Scale Switched Interferometric Arrays

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IEEE signal processing LETTERS 2025年 32卷 41-45页

作者： Wang, Jianhua El Korso, Mohammed Nabil Bacharach, Lucien Larzabal, Pascal Univ Paris Saclay SATIE F-91190 Gif Sur Yvette France Univ Paris Saclay L2S F-91190 Gif Sur Yvette France

Interferences and computational cost pose significant challenges in large-scale interferometric sensing, impacting the accuracy and numerical efficiency of imaging algorithms. In this letter, we introduce an imaging algorithm using antenna switching based on expectation-maximization (EM) to address both challenges. By leveraging the low-rank noise model, our approach effectively captures interferences in interferometric data. Additionally, the proposed switching strategy between different sub-arrays reduces significantly the computational complexity during image restoration. Through extensive experiments on simulated datasets, we demonstrate the superiority of the low-rank noise model over the Gaussian noise model in the presence of interferences. Furthermore, we show that the proposed switching approach yields similar imaging performance with fewer antennas compared to the full array configuration, thereby reducing computational complexity, while outperforming non-switching configurations with the same number of antennas.

关键词： Imaging Switches Antennas Computational modeling Antenna arrays Vectors signal processing algorithms Radio interferometry Correlation Computational complexity Antenna array processing Barankin bound Cramer-Rao bound EM algorithm interferometric array

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Joint Optimization of Spectrum Recovery and Target Scattering Parameter Estimation in Microwave Photonic Radar

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第6期60卷 8100-8117页

作者： Hai, Yu Wu, Junjie Pu, Wei Shao, Zhaoyi Huang, Peng Li, Zhongyu Wang, Anle Wang, Dangwei Huang, Yulin Yang, Jianyu Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Early Warning Acad Microwave Photon Ctr Wuhan 430019 Peoples R China

The accurate estimation of target scattering characteristics is deemed crucial in modern remote sensing technology, wherein valuable prior information is provided for radar applications, such as target recognition and auxiliary imaging. Ultra-wideband signals with high signal-to-noise ratios can be generated by microwave photonic radar (MWP), allowing for the precise estimation of target scattering characteristics. Due to the requirements of MWP radar in completing tasks, such as antiinterference and resource management, it usually emits radar signals in discrete subbands. However, discrete spectra lead to a significant reduction in the accuracy of target scattering parameter estimates. An algorithm that combines low-rank and sparse priors for the estimation of target attribute scattering parameters, along with the simultaneous recovery of the missing spectrum through a joint solution model, is introduced in this article. First, the impact of the missing spectrum on parameter estimation is analyzed by employing the MWP echo model with discrete subbands. Subsequently, a joint model for spectrum restoration and target parameter estimation, featuring low-rank and sparse priors, is constructed. An improved alternating direction method of multipliers algorithm is proposed for the joint solution of the problems related to missing spectrum recovery and scattering characteristics estimation. In contrast to traditional algorithms, the robustness of the optimization algorithm's solution is enhanced in this article through the introduction of an equilibrium threshold factor, thus preventing the global optimization problem from being trapped in local optima. Finally, the structural enhancement of imaging results by the estimated target scattering centers is demonstrated, thereby improving the interpretability of remote sensing images. The effectiveness of the algorithm proposed in this article is validated using measured MWP radar data.

关键词： Scattering Estimation Radar imaging Radar Radar scattering signal processing algorithms Accuracy Imaging enhancement microwave photonic (MWP) radar spectrum recovery target scattering characteristics

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Model-Based Online Learning for Active ISAC Waveform Optimization

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2024年第5期18卷 737-751页

作者： Pulkkinen, Petteri Koivunen, Visa Aalto Univ Dept Informat & Commun Engn FI-02150 Espoo Finland Saab Finland Oy FI-00100 Helsinki Finland

This paper proposes a Model-Based Online Learning (MBOL) framework for waveform optimization in integrated sensing and communications (ISAC) systems. In particular, the MBOL framework is proposed to enhance the ISAC performance under dynamic environmental conditions. Unlike Model-Free Online Learning (MFOL) methods, our approach leverages a rich structural knowledge of sensing, communications, and radio environments, offering better explainability and sample efficiency. This paper establishes a theoretical analysis of the proposed class of MBOL methods, showing essential performance conditions and convergence rates. This theoretical analysis is critical for understanding the potential of MBOL in active waveform optimization tasks. We demonstrate the proposed MBOL framework in multicarrier ISAC systems, focusing on the sub-carrier selection and power allocation problem. Via numerical experiments, we show that the proposed MBOL method outperforms the MFOL method in terms of sample efficiency. The results underline the potential of MBOL for improving the active waveform optimization performance in ISAC systems, particularly when sample efficiency and explainability are critical.

关键词： Sensors Resource management Radar Task analysis signal processing algorithms Markov decision processes Interference Joint radar-communications systems reinforcement learning online convex optimization model-based learning resource allocation waveform optimization

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