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l0 Penalized Maximum Likelihood Estimation of Sparse Covariance Matrices

IEEE signal processing LETTERS 2025年 32卷 66-70页

作者： Fatima, Ghania Stoica, Petre Babu, Prabhu Indian Inst Technol Delhi CARE New Delhi 110016 India Uppsala Univ Dept Informat Technol S-75105 Uppsala Sweden

In this letter we present a framework for estimating sparse covariance matrices, wherein we solve the l(0)-norm penalized maximum likelihood estimation problem using the extended Bayesian information criterion (EBIC), a high dimensional model selection rule. The framework combines choosing the sparsity pattern and estimating the covariance matrix in a single step, eliminating the need for any hyper-parameter tuning. Using the framework we propose a cyclic majorization-minimization based technique and apply it to synthetic data to evaluate its performance in terms of normalized root mean square error (NRMSE) and Kullback Leibler (KL) divergence.

关键词： Covariance matrices Maximum likelihood estimation Sparse matrices Minimization Tuning signal processing algorithms Optimization Bayes methods Wireless communication Taylor series Covariance matrix estimation EBIC l(0) penalized maximum likelihood

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FastPSA: A Fast Version of the Principal Skewness Analysis

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IEEE signal processing LETTERS 2025年 32卷 1226-1230页

作者： Gao, Jingyu Geng, Xiurui Ji, Luyan Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Chinese Acad Sci Key Lab Technol Geospatial Informat Proc & Applica Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Recently, principal skewness analysis (PSA) has been introduced into the domain of feature extraction. It is equivalent to the skewness version of Fast independent component analysis (FastICA). Unlike FastICA, PSA does not require all sample points when searching for the projection directions, making it faster. However, for the data of dimension L, PSA needs to calculate the eigenvectors of L tensors, each of size LxLxL. When L is large, PSA still requires significant computational time to find all projection directions. In this letter, we find that the (m+1)th projection direction in PSA can be obtained by calculating the eigenvector of a tensor of size (L-m) x (L-m) x (L-m). Furthermore, we propose a fast version of PSA (FastPSA) that is mathematically equivalent to PSA. The experimental results demonstrate that FastPSA has lower computational complexity than PSA.

关键词： Tensors Principal component analysis Feature extraction Computational complexity Vectors signal processing algorithms Training Hyperspectral imaging Data mining Computational efficiency FastICA principal skewness analysis hyperspectral image

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A Robust Adaptive Beamforming Algorithm for Mismatch and Impulsive Noise Circumstance

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Zhang, Xuejun Feng, Dazheng Wang, Zhonggen Gan, Fubao Anhui Univ Sci & Technol Sch Elect & Informat Huainan 232001 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

This letter provides a solution to a robust adaptive beamformer used in remote sensing systems under the effect of two factors: 1) the impulsive noise satisfying Gaussian mixture distribution and 2) unknown model mismatches, such as look direction errors and sensor location perturbations. First, a beamformer output-based cost function is designed to effectively suppress the influence of impulsive noise. Second, the weight vector of the beamformer is regularized through norm constraints to reduce the impact of model mismatches. Furthermore, an efficient iterative scheme is established to quickly minimize the designed cost function based on their properties, and a robust adaptive beamformer can be obtained. Finally, computer simulations are presented to demonstrate the robust performance and fast convergence of the novel algorithm in the presence of impulsive noise and model mismatches.

关键词： Noise Cost function Array signal processing Adaptation models Vectors Loading Interference signal to noise ratio Costs signal processing algorithms impulsive noise model mismatch regularization robust adaptive beamformer

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A Two-Level Weighted Low-Complexity Adaptive Beamforming Method

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IEEE signal processing LETTERS 2025年 32卷 1246-1250页

作者： Du, Yuxi Cui, Weijia Ba, Bin Chen, Yangyang Zhang, Long PLA Strateg Support Informat Engn Univ Zhengzhou 450001 Peoples R China

Aiming at the problem of high computational complexity of adaptive beamforming techniques in array radar systems, a two-level weighted low-complexity adaptive beamforming method is proposed in this letter. First, the uniform linear array is divided into subarrays and each subarray has the same elements. The desired signal received by each array element in the subarray is then superimposed by compensating for the delay using a first level weighting. Finally, the interference signals and noise are suppressed using a second level weighting to obtain the ideal output signal to interference plus noise ratio. Simulation results verify the effectiveness and reliability of the proposed method.

关键词： Interference Antenna arrays signal to noise ratio Array signal processing Noise Vectors Computational complexity signal processing algorithms Covariance matrices Receiving antennas Two-level weighted adaptive beamforming signal to interference plus noise ratio

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Improved Free-of-CPP ADMM-Based Iterative Decoding Algorithm of Binary LDPC Codes

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IEEE signal processing LETTERS 2025年 32卷 211-215页

作者： Bai, Jing An, Zedong Chi, Yuhao Song, Guanghui Yuen, Chau Shijiazhuang Tiedao Univ Sch Informat Sci & Technol Shijiazhuang 050043 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 487372 Singapore

Iterative decoding algorithms based on the alternating direction method of multipliers (ADMM) decoding of low density parity check (LDPC) codes has emerged as an alternating decoding method and bringed a boom of research on drawing upon mathematical optimization to LDPC decoding. Improving error-correcting performance is a key issue to enhance the superiority of ADMM decoding. In this letter, we investigate an efficient ADMM-based iterative decoder for binary LDPC codes. First, we build an mathematical programming equivalence of the maximum likelihood (ML) decoding problem by transforming parity-check constraints to multiple equivalent linear constraints and eliminating check-polytope projection (CPP). Then, an iterative algorithm based on ADMM technique is developed to solve this free-of-CPP (FCPP) equivalence and each ADMM update can be computed efficiently. Moreover, the proposed ADMM-FCPP decoding algorithm is analyzed to display a linear complexity to the length of the LDPC code at each iteration. Finally, simulation results demonstrate the superiority of the proposed decoder in error-correcting performance compared with the state-of-the-art ADMM-based decoders.

关键词： Maximum likelihood decoding Iterative decoding Convex functions Vectors signal processing algorithms Optimization Complexity theory Simulation Programming profession Low-density parity-check (LDPC) codes alternating direction method of multipliers (ADMM) check- polytope projection (CPP) mathematical programming mathematical programming

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Channel Estimation and Beamforming Design for MF-RIS-Aided Communication Systems

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IEEE signal processing LETTERS 2025年 32卷 916-920页

作者： Pan, Zaihao Wang, Wen Nie, Gaofeng Zheng, Ailing Ni, Wanli Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Pervas Commun Ctr Purple Mt Labs Nanjing 211111 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

In this letter, we study the beamforming design for channel estimation of multi-functional reconfigurable intelligent surface (MF-RIS)-aided multi-user communications that supports simultaneous signal reflection, refraction, and amplification. A least square (LS) based channel estimator is proposed for MF-RIS by considering both the coupled MF-RIS beams and the introduced thermal noise. With the discrete fourier transform (DFT)-matrix, the MF-RIS beamforming design problem is simplified under the proposed LS channel estimator. The optimal MF-RIS beamforming design that achieves the Cram & eacute;r-Rao lower bound (CRLB) of channel estimator is obtained with the proposed alternating optimization algorithm. Simulation results demonstrate the effectiveness of the proposed beamforming design in reducing the impact of thermal noise.

关键词： Channel estimation Array signal processing Reconfigurable intelligent surfaces Noise Thermal noise signal processing algorithms Reflection Estimation error Optimization Lower bound Beamforming design channel estimation least square estimator multi-functional RIS

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Edge-Preserving Consensus by Exploring the Feedback-Based Masking Method

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IEEE signal processing LETTERS 2025年 32卷 936-940页

作者： He, Wei Jiang, Jinyang Liu, Tianyue Aikoun, Yves Fidele Mao, Yi Fudan Univ Acad Engn & Technol Shanghai Engn Res Ctr AI & Robot Shanghai 200433 Peoples R China Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence Nanjing 210023 Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Hohai Univ Key Lab Maritime Intelligent Network Informat Tec Minist Educ Changzhou 213200 Peoples R China

This brief deals with the edge-preserving consensus in networked agent groups, which seeks to keep the information of the state trajectories of agents private by utilizing an edge-based decentralized protocol design. A novel type of decentralized feedback-based masking approach is adopted for designing local channel filters such that the agents reach consensus under the heterogenous channel filters, where the information of the filter associated with a specific communication link can only be known by the agents who share this communication link. Our design guarantees asymptotical convergence to the consensus value for both the cases of the non-recursive and recursive nominal filters. Moreover, the algorithms for designing the masking elements are provided, by using the parallel realization of several stable fundamental elements and the bisection method for searching the gain.

关键词： Privacy Protocols Transfer functions Eigenvalues and eigenfunctions signal processing algorithms Multi-agent systems Design methodology Artificial intelligence Wireless sensor networks Vehicle dynamics Edge-preserving consensus security distributed networks channel filter

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High-Accuracy DOA Estimation for Non-Collinear Sparse Uniform Array

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IEEE signal processing LETTERS 2025年 32卷 206-210页

作者： Wang, Hongyong Chen, Xiaolong Deng, Weibo Zhang, Caisheng Xue, Yonghua Naval Aviat Univ Yantai 264001 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China

Conventional sparse uniform arrays (SUAs) is composed of multiple identical and rigorously collinear uniform linear arrays. By adjusting the baseline length between the subarrays, the array aperture can be arbitrarily large, thus substantially improving the accuracy of the direction-of-arrival (DOA) estimation. However, in practical applications, it is challenging to meet the strict collinearity requirement due to geographical constraints. In this letter, to address this problem, we propose the non-collinear sparse uniform array (NCSUA) model to mitigate the influence of the non-ideal terrain and enhance the practicality of the SUA. A novel estimation algorithm is then proposed to resolve the angle ambiguity in NCSUA and effectively achieve high-accuracy DOA estimation. Compared with the conventional SUA, numerical simulation results demonstrate the superiority of NCSUA employing the new de-ambiguity algorithm in DOA estimation performance and practical applications.

关键词： Estimation signal processing algorithms Direction-of-arrival estimation Array signal processing Antenna arrays Accuracy Vectors Receiving antennas Radar Sparse matrices High-accuracy DOA estimation non-collinear array sparse uniform array

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Optimal Sample Acquisition for Optimally Weighted PCA From Heterogeneous Quality Sources

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IEEE signal processing LETTERS 2025年 32卷 1425-1429页

作者： Hong, David Balzano, Laura Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

Modern high-dimensional datasets are often formed by acquiring samples from multiple sources having heterogeneous quality, i.e., some sources are noisier than others. Collecting data in this manner raises the following natural question: what is the best way to collect the data (i.e., how many samples should be acquired from each source) given constraints (e.g., on time or energy)? In general, the answer depends on what analysis is to be performed. In this paper, we study the foundational signal processing task of estimating underlying low-dimensional principal components. Since the resulting dataset will be high-dimensional and will have heteroscedastic noise, we focus on the recently proposed optimally weighted PCA, which is designed specifically for this setting. We develop an efficient method for designing sample acquisitions that optimize the asymptotic performance of optimally weighted PCA given resource constraints, and we illustrate the proposed method through various case studies.

关键词： Principal component analysis Noise signal processing algorithms Costs Soft sensors Software algorithms Optimization Data models Training Image sensors Heterogeneous quality large-dimensional data principal component analysis sample acquisition design

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DPO: Discrete Prompt Optimization for Vision-Language Models

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IEEE signal processing LETTERS 2025年 32卷 671-675页

作者： Liang, Nanhao Liu, Yong Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China

In recent years, the emergence of large vision-language models (VLMs) has catalyzed the development of prompt learning, where networks are trained to enhance VLM performance by learning continuous prompts. However, traditional continuous prompt learning often struggles with challenges like overfitting to Base classes and a lack of interpretability due to the nature of prompt parameterization. To overcome these limitations, we introduce Discrete Prompt Optimization (DPO), a method that optimizes text prompts in discrete word-space. During training, scores are assigned to token embeddings, which are then used to select the most effective token sequence for the downstream task. DPO was tested across 11 diverse datasets, consistently outperforming baseline methods like CLIP and CoOp on Novel classes in most cases. This discrete approach not only reduces overfitting but also enhances transparency and model interpretability, enabling the learning of dataset-specific text prompts that are easily understandable.

关键词： Training Optimization Adaptation models Visualization Overfitting Vectors Vocabulary signal processing algorithms Stochastic processes Standards Prompt learning vision-language model

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