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Phase Compensation-Based 2D-DOA Estimation for EMVS-MIMO Radar

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第2期60卷 1299-1308页

作者： Zhang, Zhe Shi, Junpeng Wen, Fangqing China Three Gorges Univ Coll Comp & Informat Technol Yichang 443002 Peoples R China China Three Gorges Univ Hubei Prov Engn Technol Res Ctr Construction Qual Yichang 443002 Peoples R China Natl Univ Def Technol Sch Coll Elect Countermeasure Hefei 230037 Peoples R China Hubei Univ Automot Technol Inst Vehicle Informat Control & Network Technol Shiyan 442002 Peoples R China

Multiple-input-multiple-output (MIMO) sparse electromagnetic vector sensor (EMVS) arrays have brought new perspectives to signal processing due to their flexibility and higher resolution. In this study, we focus on angle estimation in a monostatic MIMO system using arbitrary geometry EMVS arrays. An improved parallel factor (PARAFAC)-based algorithm is introduced. By harnessing the natural multidimensional structure of the array output, we rearrange it into a PARAFAC model. The factor matrices are obtained using the complex parallel factor analysis (COMFAC), followed by applying vector cross-product (VCP)/phase compensation for rough/refined estimation. This approach achieves superresolution estimation while automatically pairing angles and showing low computational complexity. As a result, it outperforms existing algorithms, and numerical simulation experiments validate the improvements achieved by the proposed method.

关键词： Radar Estimation Radar antennas signal processing algorithms Phased arrays MIMO communication Array signal processing Angle estimation arbitrary geometry electromagnetic vector sensor (EMVS) arrays monostatic multiple-input multiple-output (MIMO) system phase compensation

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Transfer Diagnosis Model of Internal Combustion Engine With Embedded Vibration signal Impact Decomposition

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IEEE ACCESS 2024年 12卷 62779-62792页

作者： Li, He Dong, Liangyu Peng, Yang Dai, Zhixiang Jiang, Yang Zhang, Jinjie Beijing Univ Chem Technol Key Lab Engine Hlth Monitoring Control & Networkin Minist Educ Beijing 100029 Peoples R China Beijing Univ Chem Technol State Key Lab High End Compressor & Syst Technol Beijing 100029 Peoples R China China Ind Control Syst Cyber Emergency Response Te Beijing 100040 Peoples R China PetroChina Southwest Oil & Gasfield Co Gathering & Transportat Engn Technol Res Inst Chengdu 610051 Peoples R China Chongqing City Construct Investment Grp Co Ltd Chongqing 400015 Peoples R China

Traditional transfer diagnosis models for internal combustion engines show a decrease in generalization ability due to the multisource features aliasing in vibration signals and the effect of variable operating conditions. To address this problem, this paper proposes a transfer diagnosis model based on the deep subdomain adaptive network framework. To address feature aliasing, based on minimizing amplitude moment and reconstruction loss, a new adaptive decomposition layer is designed and embedded into the framework to decompose complex signals into single-impact components in time domain. To alleviate the effect of operating conditions, a new constraint for minimizing signal feature variance loss is designed and introduced into the framework's loss function. This constraint calculates the variance of the sample features of the same fault label under variable operating conditions, aiming to excavate invariant features of operating conditions and complete feature mapping of domain adaptation. Validation with experimental data yields an accuracy of 94.81%.

关键词： Artificial neural networks deep learning diesel engines fault detection feature extraction internal combustion engines signal processing algorithms time-domain analysis transfer learning vibrations

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A One-Shot Framework for Distributed Clustered Learning in Heterogeneous Environments

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 636-651页

作者： Armacki, Aleksandar Bajovic, Dragana Jakovetic, Dusan Kar, Soummya Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Univ Novi Sad Fac Tech Sci Novi Sad 21000 Serbia Univ Novi Sad Fac Sci Novi Sad 21000 Serbia

The paper proposes a family of communication efficient methods for distributed learning in heterogeneous environments in which users obtain data from one of K different distributions. In the proposed setup, the grouping of users (based on the data distributions they sample), as well as the underlying statistical properties of the distributions, are apriori unknown. A family of One-shot Distributed Clustered Learning methods (ODCL- C ) is proposed, parametrized by the set of admissible clustering algorithms C , with the objective of learning the true model at each user. The admissible clustering methods include K -means (KM) and convex clustering (CC), giving rise to various one-shot methods within the proposed family, such as ODCL-KM and ODCL-CC. The proposed one-shot approach, based on local computations at the users and a clustering based aggregation step at the server is shown to provide strong learning guarantees. In particular, for strongly convex problems it is shown that, as long as the number of data points per user is above a threshold, the proposed approach achieves order-optimal mean squared error (MSE) rates in terms of the sample size. An explicit characterization of the threshold is provided in terms of problem parameters. The trade-offs with respect to selecting various clustering methods (ODCL-CC, ODCL-KM) are discussed and significant improvements over state-of-the-art are demonstrated. Numerical experiments illustrate the findings and corroborate the performance of the proposed methods.

关键词： Clustering algorithms Computational modeling signal processing algorithms Training Costs Statistics Sociology Distributed learning communication efficiency one-shot methods statistical heterogeneity clustered learning mean-squared analysis order-optimality

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Barycenter Calibration With High Order Spectra of Windowed Delay-Doppler signals for OTFS Based ISAC Systems

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 2450-2466页

作者： Yang, Yiran Pan, Yuchen Liu, Xiqing Peng, Mugen Beijing Univ Posts & Telecommun Sch Informat & Commun Engn State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Integrated sensing and communication (ISAC) technology stands as a pivotal enabler for the sixth-generation mobile communication systems (6G) to enable high data rate communication while ensuring accurate target sensing. Orthogonal time-frequency space (OTFS) modulation has emerged as a promising candidate for ISAC, owing to its remarkable performance in doubly-selective channels. However, the sensing resolutions of OTFS are limited by the available bandwidth and frame time. In cases of insufficient delay and Doppler resolutions, the channel response is spread along delay and Doppler axes, resulting in substantial sensing inaccuracies. In our previous research, we introduced the amplitude barycenter calibration (A-BC) algorithm as an effort to improve the accuracy of parameter estimation, albeit without a strategic design for OTFS windows. In this work, we delve into the receive window design of the OTFS-ISAC system and develop a barycenter calibration algorithm that explores the high-order spectra (HOS) of the DD domain channels, called the HOS-BC algorithm. The simulation results indicate that the proposed algorithm not only improves the sensing resolution under the given bandwidth and frame time but also achieves a higher sensing accuracy at a low pilot signal-to-noise ratio compared with the A-BC algorithm.

关键词： Sensors Channel estimation signal processing algorithms Delays OFDM Receivers Time-frequency analysis High order spectrum barycenter calibration orthogonal time frequency space integrated sensing and communication window design estimation error

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Online Optimization Under Randomly Corrupted Attacks

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 2160-2172页

作者： Qu, Zhihai Li, Xiuxian Li, Li Yi, Xinlei Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Coll Elect & Informat Engn Dept Control Sci & Engn Shanghai 201804 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 201804 Peoples R China MIT Lab Informat & Decis Syst Cambridge MA USA

Existing algorithms in online optimization usually rely on trustful information, e.g., reliable knowledge of gradients, which makes them vulnerable to attacks. To take into account the security issue in online optimization, this paper investigates the effect of randomly corrupted attacks, which can corrupt gradient information arbitrarily. To conquer the randomly corrupted attack, an onLine muLtiple normaLized Gradient Descent (L3GD) algorithm is proposed. Under mild conditions, the algorithm is proven to achieve satisfactory expected dynamic regret, i.e,O({P-T*+T-3/4,S-T*+Sigma(T )(t=1)||( )del( )f(t)(xt*)||(2)}) and O(F-T+T-3/4), without convex assumption, where P-T*, S-T*, and F-T denote the path-length, squared path-length, and the functional variation, respectively. The results are comparable to state-of-the-art algorithms in the absence of randomly corrupted attacks. To our best knowledge, this paper is the first to consider randomly corrupted attacks in online optimization. Simulations conducted on both synthetic examples and real-world datasets, namely MNIST and CIFAR-10, corroborate the resilience of L3GD.

关键词： Optimization signal processing algorithms Heuristic algorithms Machine learning algorithms Robustness Noise Security Online optimization corrupted gradients resilient online optimization algorithm

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Range-Ambiguous Clutter Separation via Reweighted Atomic Norm Minimization With EPC-MIMO Radar

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IEEE signal processing LETTERS 2025年 32卷 146-150页

作者： Gao, Jie Zhu, Shengqi Lan, Lan Sui, Jinxin Li, Ximin Xidian Univ Sch Elect Engn Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

The existence of range ambiguity and range dependence will seriously deteriorate the performance of space-time adaptive processing (STAP). In this regard, an adaptive range-ambiguous clutter separation method suitable for the element-pulse coding (EPC)-multiple-input multiple-output (MIMO) radar is developed in this letter. By introducing the EPC factor in both transmit elements and pulses, the clutter located in different range-ambiguous regions can be distinguished in the transmit spatial frequency dimension. Particularly, to ensure the separated performance of range-ambiguous clutter, the EPC factor is designed. Moreover, an approach on the basis of reweighted atomic norm minimization (RANM) is developed to separate the range-ambiguous clutter, leveraging the transmit spatial frequencies of clutter located in various range ambiguity areas. Furthermore, after clutter separation, the clutter is canceled via STAP individually in each range ambiguous region. A series of simulation results validate the efficacy of the proposed approach.

关键词： Clutter Radar Radar clutter signal processing algorithms Silicon Frequency modulation Doppler effect Vectors Object detection Minimization EPC-MIMO radar range-ambiguous clutter separation RANM STAP

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Federated Learning from Heterogeneous Data via Controlled Air Aggregation with Bayesian Estimation

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 1928-1943页

作者： Gafni, Tomer Cohen, Kobi Eldar, Yonina C. Ben Gurion Univ Negev Sch Elect & Comp Engn IL-8410501 Beer Sheva Israel Weizmann Inst Sci Math & Comp Sci Fac IL-7610001 Rehovot Israel

Federated learning (FL) is an emerging machine learning paradigm for training models across multiple edge devices holding local data sets, without explicitly exchanging the data. Recently, over-the-air (OTA) FL has been suggested to reduce the bandwidth and energy consumption, by allowing the users to transmit their data simultaneously over a Multiple Access Channel (MAC). However, this approach results in channel noise directly affecting the optimization procedure, which may degrade the accuracy of the trained model. In this paper we jointly exploit the prior distribution of local weights and the channel distribution, and develop an OTA FL algorithm based on a Bayesian approach for signal aggregation. Our proposed technique, dubbed Bayesian Air Aggregation Federated learning (BAAF), is shown to effectively mitigate noise and fading effects induced by the channel. To handle statistical heterogeneity of users data, which is a second major challenge in FL, we extend BAAF to allow for appropriate local updates by the users and develop the Controlled Bayesian Air Aggregation Federated-learning (COBAAF) algorithm. In addition to using a Bayesian approach to average the channel output, COBAAF controls the drift in local updates using a judicious design of correction terms. We analyze the convergence of the learned global model using BAAF and COBAAF in noisy and heterogeneous environments, showing their ability to achieve a convergence rate similar to that achieved over error-free channels. Simulation results demonstrate the improved convergence of BAAF and COBAAF over existing methods in machine learning tasks.

关键词： Convergence Servers Bayes methods Noise measurement signal processing algorithms Data models Wireless communication Federated learning stochastic gradient descent Bayesian inference wireless communication

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Node-Specific Distributed Generalized Sidelobe Canceler

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2025年 33卷 211-223页

作者： Li, Shiqin Zhao, Zhao Xu, Zhiyong Rong, Yingjiao Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China Sci & Technol Near Surface Detect Lab Wuxi 214035 Peoples R China

With booming growth of Internet of Things technique, acoustic sensor networks comprised of microphone array nodes have been increasingly applied to multi-source speech enhancement (SE). Compared with conventional centralized SE algorithms, distributed approaches achieve comparable performance with many advantages such as convenience, low cost and scalability. However, existing distributed methods either need to run once for each desired source independently, or require the number of channels transmitted by each node to increase proportionally with the number of desired sources. Furthermore, it is also challenging for each node to acquire all the steering vectors as demanded by existing methods. To address these issues, a node-specific distributed generalized sidelobe canceler (NS-DGSC) algorithm is proposed in this article. Here, "node-specific" refers to scenarios where each node estimates individual desired source. First, microphone signals at each node are pre-filtered by a local generalized sidelobe canceler (GSC), yielding preliminary enhancement for individual desired source. Then, for each node, preliminary results exchanged from other nodes are utilized to expand the lower branch of local GSC. Finally, the expanded lower branch is employed to calculate a new noise canceler, leading to the final output at each node. Compared with state-of-the-art distributed algorithms, our approach only requires single-channel transmission at each node as well as available local steering vectors for simultaneously enhancing multiple desired sources. Simulations across various signal-to-interference-plus-noise ratio (SINR) input, reverberation time, source-to-node distance, and steering vector estimation error conditions reveal that the proposed approach considerably outperforms existing methods in terms of signal-to-distortion ratio (SDR) and robustness in practice, while provides comparable SINR improvement. Real-world experiments also verify its effectiveness.

关键词： Vectors signal to noise ratio Microphone arrays Speech enhancement Bandwidth Wireless sensor networks Wireless communication Wiener filters signal processing algorithms Internet of Things Distributed processing generalized sidelobe canceler (GSC) node-specific multi-source speech enhancement

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Clutter Suppression in Passive ISAR Using Compressive Sensing

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第2期60卷 2210-2223页

作者： Azimifar, Morteza Sebt, Mohammad Ali KN Toosi Univ Technol Dept Elect & Comp Engn Tehran 19697 Iran

This article proposes a method to remove clutter in passive bistatic inverse synthetic aperture radar (ISAR) based on low-rank and sparse matrix recovery algorithms. Previously, the clutter removal process was done mainly in the preprocessing stages. Still, our clutter removal and ISAR processing are done concurrently using the low-rank plus sparse model. It is shown that the proposed method can effectively remove both stationary and nonstationary clutter from ISAR images while maintaining information on slow-moving targets. Also, given the knowledge of the clutter spectrum, one can use this information to remove clutter more effectively. The superior performance of the proposed technique is validated using computer simulation and experimental data.

关键词： Clutter Passive radar Surveillance Radar imaging Filtering Doppler effect signal processing algorithms Clutter suppression compressive sensing (CS) digital video broadcast terrestrial (DVB-T) inverse synthetic aperture radar (ISAR) low-rank and sparse decomposition low-rank matrix recovery passive radar sparse recovery

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Mixed Source Separation of Partially Correlated Source Based on Analytic CMA

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IEEE SENSORS JOURNAL 2024年第22期24卷 37209-37217页

作者： Tan, Peiao Fan, Kuangang Chen, Yuhang Jiangxi Univ Sci & Technol Sch Mech & Engn Ganzhou 341000 Peoples R China Jiangxi Univ Sci & Technol Sch Elect Engn & Automat Ganzhou 341000 Jiangxi Peoples R China Mengyin Cty Pengcheng Wanli Vehicle Co Ltd Gaodu Econ Dev Area Linyi 276200 Shandong Peoples R China Jiangxi Prov Key Lab Multidimens Intelligent Perce Ganzhou 341000 Jiangxi Peoples R China Chinese Acad Sci Ganjiang Innovat Acad Acad Sci Ganzhou 341000 Jiangxi Peoples R China Jiangxi Univ Sci & Technol Magnet Suspens Technol Key Lab Jiangxi Prov Ganzhou 341000 Peoples R China

The analytical constant modulus algorithm (ACMA) is an effective tool for separating and recovering statistically independent constant modulus (CM) signals. In previous ACMA studies, source signals are usually restricted to be statistically independent or statistically orthogonal. In this article, we no longer impose these restrictions on CM signals, but only require the CM signals to be partially independent or orthogonal. However, this will lead to the inability of ACMA to successfully separate these partially correlated source signals from the observed data. This article introduces a new method for separating partially correlated sources based on ACMA. We first analyzed that the biggest shortcoming of the original algorithm when dealing with partially correlated sources was that it could not estimate the number of source signals. Later, we found that the best range of the threshold value was a curve about SNR, which would not change greatly with the increase in the correlation coefficient. Therefore, we adopted the method of mixed estimation of the SNR of source signals and improved the threshold formula. The threshold performance of estimating the number of partially correlated sources is improved, and the ability of ACMA to estimate the number of partially correlated constant modulus sources is improved. We then estimate the source signal by reconstructing the covariance matrix of the signal to eliminate the associated effects. Simulation results show that this method can be used for nonindependent constant modulus signals with a correlation coefficient of less than 0.5.

关键词： Vectors signal processing algorithms Sensors Matrix decomposition Antenna arrays Correlation Analytical models Antenna array mixed signal recovery (MSS) constant modulus algorithm (CMA) partially correlated source

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