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Complex-Valued Neural Network With Multistep Training for Single-Snapshot DOA Estimation

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 5-5页

作者： Zhang, Yuheng Zeng, Rong Zhang, Shuchen Wang, Jiachen Wu, Yifeng Sun Yat sen Univ Sch Elect & Commun Engn Shenzhen 518107 Peoples R China

Estimating the direction of arrival (DOA) for single snapshot signals is paramount in practical applications. In this letter, we present a method for single snapshot DOA estimation based on a neural network, namely, the complex-valued residual angle estimation network (CVRAEN). First, the CVRAEN network uses the initial feature extraction module including complex linear layer and complex convolutional layer to process the input signal and decompose it into multiple complex-valued feature signals for obtaining preliminary signal feature extraction. Subsequently, to extract depth features, the depth feature extraction module is used to further process the extracted features. Finally, the mapping module combines and maps the extracted features for DOA estimation. This letter uses a series of training steps, including pretraining, signal-to-noise ratio (SNR) stratified training, and SNR adaptive training, to continuously optimize the network model. The main simulation results indicate that within the simulated SNR range, the proposed method exhibits a decrease of approximately 30% in root mean square error compared with traditional methods.

关键词： Direction-of-arrival estimation Estimation Training Feature extraction signal processing algorithms signal to noise ratio Matching pursuit algorithms Complex-valued network direction of arrival (DOA) multistep training residual block single snapshot signal

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A Novel Image Preprocessing Algorithm for Doppler Wind Spectrum Inversion: Principle, Method, and Performance

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Zhao, Mingguang Yu, Zhibin Li, Mengpei He, Chunhui Wang, Shihai Harbin Inst Technol Shenzhen Sch Aerosp Sci Shenzhen 518071 Peoples R China State Key Lab Solar Act & Space Weather Beijing 100190 Peoples R China

We propose a novel image preprocessing algorithm (IPA) for coherent Doppler wind lidar (CDWL) wind spectrum inversion under low signal-to-noise ratio (SNR) conditions. By utilizing prior knowledge such as the spatial continuity of wind spectra, this new algorithm significantly improves the reliability of the peak frequency estimation and enhances the wind inversion accuracy and precision. The theoretical analysis shows that the IPA can improve the accuracy and precision of the wind spectrum inversion under low SNR, through preselecting and weighting the Doppler wind spectra. Verified by simulation and real CDWL measurements, the IPA effectively enhances the accuracy and reliability of lidar wind spectrum inversion under low SNR conditions. Specifically, the performance enhancement is equivalent to an approximate improvement in the SNR of the data by 2.5 and 5 dB. The measurements with real CDWL measurements show the maximum effective detection range can be increased by more than 2 km. The IPA is low complexity, demanding low computation resources, and can be applied to high temporal resolution Doppler wind estimation.

关键词： Laser radar Doppler effect signal to noise ratio Noise Accuracy signal processing algorithms Estimation Logic gates Optical fiber amplifiers Interference Coherent Doppler wind lidar (CDWL) doppler wind spectrum estimation image preprocessing algorithm (IPA) low signal-to-noise ratio (SNR)

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Fast Gridless DOA Estimation Algorithm for MA-ANS Scenarios Using a Modified FastIPM

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IEEE signal processing LETTERS 2024年 31卷 1845-1849页

作者： Gao, Yiding Wu, Min Li, Zihao Hao, Chengpeng Orlando, Danilo Chinese Acad Sci Inst Acoust Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310000 Peoples R China Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy

This letter presents a fast gridless direction-of-arrival (DOA) estimation algorithm that improves the Fast Interior-Point Method (FastIPM). The proposed algorithm effectively achieves a significant reduction in computational load for large-scale arrays while maintaining an accurate estimation. It reduces the complexity of gridless DOA estimation to $\mathcal {O}(N<^>{2})$ per iteration (according to the Landau notation). Compared to the original FastIPM, we extend the received signal data model to account for more general scenarios that include missing array elements and arbitrary number of snapshots. By formulating the problem as an optimization with an obstacle function, we iteratively minimize the dual gap to obtain the optimal solution for atomic norm minimization problem. Extensive numerical simulations demonstrate the algorithm's computational superiority over the state-of-the-art gridless DOA estimation methods, providing excellent resolution and accuracy.

关键词： signal processing algorithms Estimation Direction-of-arrival estimation Vectors Array signal processing Optimization Convex functions atomic norm minimization barrier function fast gridless DOA estimation interior point method

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Tampering Attack Detection for Remote State Estimation With Quantized Observations

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IEEE signal processing LETTERS 2024年 31卷 2185-2189页

作者： Li, Mengqi Hu, Yanpeng Guo, Jin Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China

Aiming at the security problem of cyber physical system (CPS), this paper designs and evaluates a tampering attack detection scheme for remote state estimation with quantized observations. When the transmitted information is quantized and the attacker randomly tampers it with a certain probability, we design a detection algorithm with a given error threshold based on the received data. Moreover, we proposes detection performance indexes, leakage detection rate and false detection rate, for the above algorithm, which are used to measure the algorithm performance to discriminate the tampered data, and further analyzes the impact of the attack strategy and the error threshold on performance indexes. Finally, simulations are designed to verify the theoretical results.

关键词： Estimation State estimation Technological innovation Detectors signal processing algorithms Sensors Cyberattack Attack detection binary quantized observations data tampering performance evaluation state estimation

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Sparse Regularization With Reverse Sorted Sum of Squares via an Unrolled Difference-of-Convex Approach

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 57-67页

作者： Sasaki, Takayuki Hayase, Kazuya Kitahara, Masaki Ono, Shunsuke NTT Corp Yokosuka 2390847 Japan Inst Sci Tokyo Yokohama 2260026 Japan

This paper proposes a sparse regularization method with a novel sorted regularization function. Sparse regularization is commonly used to solve underdetermined inverse problems. Traditional sparse regularization functions, such as L1-norm, suffer from problems like amplitude underestimation and vanishing perturbations. The reverse ordered weighted L1-norm (ROWL) addresses these issues but introduces new challenges. These include developing an algorithm grounded in theory, not heuristics, reducing computational complexity, enabling the automatic determination of numerous parameters, and ensuring the number of iterations remains feasible. In this study, we propose a novel sparse regularization function called the reverse sorted sum of squares (RSSS) and then construct an unrolled algorithm that addresses both the aforementioned two problems and these four challenges. The core idea of our proposed method lies in transforming the optimization problem into a difference-of-convex programming problem, for which solutions are known. In experiments, we apply the RSSS regularization method to image deblurring and super-resolution tasks and confirmed its superior performance compared to conventional methods, all with feasible computational complexity.

关键词： Inverse problems Perturbation methods Vectors Computational complexity Programming Convergence signal processing algorithms Optimization Convex functions signal processing Deep unrolling difference-of-convex inverse problem non-convex optimization proximal splitting method sparse regularization

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Unified Analysis of Decentralized Gradient Descent: A Contraction Mapping Framework

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 507-529页

作者： Larsson, Erik G. Michelusi, Nicolo Linkoping Univ Dept Elect Engn ISY S-58183 Linkoping Sweden Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85281 USA

The decentralized gradient descent (DGD) algorithm, and its sibling, diffusion, are workhorses in decentralized machine learning, distributed inference and estimation, and multi-agent coordination. We propose a novel, principled framework for the analysis of DGD and diffusion for strongly convex, smooth objectives, and arbitrary undirected topologies, using contraction mappings coupled with a result called the mean Hessian theorem (MHT). The use of these tools yields tight convergence bounds, both in the noise-free and noisy regimes. While these bounds are qualitatively similar to results found in the literature, our approach using contractions together with the MHT decouples the algorithm dynamics (how quickly the algorithm converges to its fixed point) from its asymptotic convergence properties (how far the fixed point is from the global optimum). This yields a simple, intuitive analysis that is accessible to a broader audience. Extensions are provided to multiple local gradient updates, time-varying step sizes, noisy gradients (stochastic DGD and diffusion), communication noise, and random topologies.

关键词： Topology Noise measurement Convergence Noise Vectors Symmetric matrices signal processing algorithms Network topology Optimization Heuristic algorithms Decentralized machine learning decentralized optimization decentralized gradient descent (DGD) diffusion stochastic DGD and diffusion federated learning communication noise random topology link failures convergence contractions fixed points mean Hessian theorem

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Analysis of Quantization Noise in Fixed-Point HDFT algorithms

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IEEE signal processing LETTERS 2024年 31卷 756-760页

作者： Alrwashdeh, Monther Czifra, Balazs Kollar, Zsolt Budapest Univ Technol & Econ H-1111 Budapest Hungary

The Discrete Fourier Transform (DFT) algorithm is widely used in signal processing and communication systems to transform the signal to the frequency-domain. As real-time signal analysis is required for fast processing, several recursive algorithms were proposed to perform the calculation with overlapping sequences in a sliding manner. One Sliding DFT (SDFT) method is the Hopping DFT (HDFT), where the DFT calculations are not evaluated sample-by-sample but with longer steps, thus further reducing the computational complexity compared to the other SDFT algorithms. This letter analyses the effect of fixed-point roundoff error in the HDFT algorithm, including the Updating Vector Transform (UVT) block. A closed-form expression for the resulting quantization noise power at the output of the HDFT algorithm is provided, which is validated through simulations. The results show that the roundoff error can be determined based on the number and size of the hops, the window size, and the number of fractional bits used in the quantization process.

关键词： Discrete Fourier transforms Quantization (signal) signal processing algorithms Roundoff errors Indexes Transforms Technological innovation Fixed-point hopping DFT quantization roundoff error sliding DFT UVT

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On the Convergence of Block Majorization-Minimization algorithms on the Grassmann Manifold

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IEEE signal processing LETTERS 2024年 31卷 1314-1318页

作者： Lopez, Carlos Alejandro Riba, Jaume Univ Politecn Cataluna Dept Teoria Senyal & Comunicac Signal Proc & Commun Grp Barcelona 08034 Spain

The Majorization-Minimization (MM) framework is widely used to derive efficient algorithms for specific problems that require the optimization of a cost function (which can be convex or not). It is based on a sequential optimization of a surrogate function over closed convex sets. A natural extension of this framework incorporates ideas of Block Coordinate Descent (BCD) algorithms into the MM framework, also known as block MM. The rationale behind the block extension is to partition the optimization variables into several independent blocks, to obtain a surrogate for each block, and to optimize the surrogate of each block cyclically. However, known convergence proofs of the block MM are only valid under the assumption that the constraint sets are closed and convex. Hence, the global convergence of the block MM is not ensured for non-convex sets by classical proofs, which is needed in iterative schemes that naturally emerge in a wide range of subspace-based signal processing applications. For this purpose, the aim of this letter is to review the convergence proof of the block MM and extend it for blocks constrained in the Grassmann manifold.

关键词： Manifolds Convergence signal processing algorithms Cost function signal processing Principal component analysis Minimization Non-convex optimization majorization-minimization Riemannian optimization Grassmann manifold geodesically convex optimization convergence

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Robust Delimitation of Aggregated Packets Based on Viterbi Decoding With Natural Redundancy in Protocols

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IEEE signal processing LETTERS 2024年 31卷 1970-1974页

作者： Li, Yongbin Yu, Hongyi Du, Jianping Zhang, Xia Liu, Guangyi Informat Engn Univ Informat Engn Coll Zhengzhou 450000 Henan Peoples R China

This letter provides a cross-layer perspective for the de-aggregation of aggregated packets. First, we analyze the natural redundancy in protocols that can be leveraged for delimitation. Second, we propose a new path metric, focusing on maximizing the likelihood of all potential redundant protocol fields. Subsequently, a Viterbi-based path search algorithm is developed to achieve the theoretical optimum. When repeated fields are available in the protocol stack, we introduce a decision feedback mechanism to further enhance performance. Tests conducted on aggregated High-Level Data Link Control (HDLC) protocol packets demonstrate that our method can approach the approximate performance upper limit with significantly reduced complexity and improved operability.

关键词： Measurement Protocols Decoding signal processing algorithms Complexity theory Tail Cross layer design Robust delimitation packet aggregation natural redundancy in protocols Viterbi decision feedback

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A Low-Complexity Chinese Remainder Theorem Based Multi-Carrier Delay Estimation Approach

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 1643-1657页

作者： Zhang, Yuxiao Wang, Shuai Yang, Xuanhe Zhang, Jiahao Pan, Gaofeng An, Jianping Niyato, Dusit Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore

With the rapid development of cluster systems, such as unmanned aerial vehicle (UAV) networks and satellite constellations, multi-node collaboration has emerged as a critical requirement. Accurate time synchronization, a cornerstone of such collaborative systems, heavily relies on high-precision delay estimation. Traditional multi-carrier delay estimation methods face an inherent trade-off between estimation accuracy and unambiguous range. While the Chinese Remainder Theorem (CRT)-based approach resolves this dilemma by enabling high-precision estimation without sacrificing range, its computational complexity remains prohibitively high for practical implementations. To address this challenge, we propose a novel low-complexity CRT algorithm based on remainder reconstruction (RR-CRT). By introducing an auxiliary phase to reconstruct erroneous remainders, our method reduces the computational complexity from O(K-2) to O(K), where K denotes the number of subcarriers. Crucially, this reduction in complexity only marginally impacts the algorithm's performance, including the phase error tolerance range, the probability of correctly solving phase ambiguity, and the root mean square error (RMSE) of delay estimation. Numerical simulations validate the effectiveness and robustness of the proposed algorithm, demonstrating its superiority in balancing computational efficiency and estimation performance.

关键词： Estimation Delay estimation Symbols Accuracy Computational complexity signal processing algorithms Frequency estimation Synchronization Frequency modulation Bandwidth Chinese remainder theorem multi-carrier low-complexity

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