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Node-Specific Distributed Generalized Sidelobe Canceler

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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Do algorithms and Barriers for Sparse Principal Component Analysis Extend to Other Structured Settings?

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 3187-3200页

作者： Wang, Guanyi Lou, Mengqi Pananjady, Ashwin Natl Univ Singapore Dept Ind Syst Engn & Management Singapore 117576 Singapore Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

We study a principal component analysis problem under the spiked Wishart model in which the structure in the signal is captured by a class of union-of-subspace models. This general class includes vanilla sparse PCA as well as its variants with graph sparsity. With the goal of studying these problems under a unified statistical and computational lens, we establish fundamental limits that depend on the geometry of the problem instance, and show that a natural projected power method exhibits local convergence to the statistically near-optimal neighborhood of the solution. We complement these results with end-to-end analyses of two important special cases given by path and tree sparsity in a general basis, showing initialization methods and matching evidence of computational hardness. Overall, our results indicate that several of the phenomena observed for vanilla sparse PCA extend in a natural fashion to its structured counterparts.

关键词： Principal component analysis Computational modeling Estimation Indexes Vectors signal processing algorithms Iterative methods structured sparsity nonconvex iterative optimization computational hardness

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Optimizing Convolutions for Deep Learning Inference on ARM Cortex-M Processors

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IEEE INTERNET OF THINGS JOURNAL 2024年第15期11卷 26203-26219页

作者： Macia-Lillo, Antonio Barrachina, Sergio Fabregat, German Dolz, Manuel F. Univ Alacant Dept Technol Informat & Computat San Vicente Del Raspeig 03690 Spain Univ Jaume 1 Dept Comp Sci & Engn Castellon de La Plana 12071 Spain

We perform a series of optimizations on the convolution operator within the ARM common microcontroller units software interface standard for neural network (CMSIS-NN) library to improve the performance of deep learning tasks on Arduino development boards equipped with ARM Cortex-M4 and M7 microcontrollers. To this end, we develop custom microkernels that efficiently handle the internal computations required by the convolution operator via the lowering approach and the direct method, and we design two techniques to avoid register spilling. We also take advantage of all the RAM on the Arduino boards by reusing it as a scratchpad for the convolution filters. The integration of these techniques into CMSIS-NN, when invoked by TensorFlow Lite for microcontrollers for quantised versions of VGG, SqueezeNet, ResNet, and MobileNet-like convolutional neural networks enhances the overall inference speed by a factor ranging from 1.13xto 1.50x .

关键词： Program processors Random access memory Registers Optimization Convolution Inference algorithms signal processing algorithms ARM Cortex-M common microcontroller units software interface standard for neural network (CMSIS-NN) convolution operator deep learning edge computing high performance microcontrollers

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Joint Spectrum Sensing and DOA Estimation Based on a Resource-Efficient Sub-Nyquist Array Receiver

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5354-5370页

作者： Liu, Liang Zhang, Zhan Zhang, Xinyun Wei, Ping An, Jiancheng Li, Hongbin Univ Elect Sci & Technol China Sch Informat & Commun Engn Natl Key Lab Blind Signal Proc Chengdu 611731 Peoples R China Singapore Univ Technol & Design SUTD Engn & Prod Dev Pillar Singapore 487372 Singapore Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA

As the demand for wireless communication continues to surge, spectrum congestion becomes more severe. Compressive spectrum sensing with joint frequency and Direction Of Arrival (DOA) estimation is instrumental to enable efficient spectrum utilization in ultra-wideband scenarios. To reduce hardware complexity, this paper proposes a resource-efficient array undersampling structure which is distinctive in that each array element only connects to one branch of the Modulated Wideband Converter (MWC), and the modulated signals in different branches have identical periods but different waveforms. The proposed structure integrates spatial sampling by array elements and temporal undersampling by the MWC. A signal model is developed for the proposed sampling structure, which can deal with more general scenarios, involving multiple subband signals, and cross-band signals. Joint spectrum sensing algorithms are proposed based on compressed sensing and subspace decomposition. Additionally, multi-resolution grid optimization strategy is designed to eliminate grid effect with low computational complexity. We also analyze the impact of structural parameters on algorithm performance, which reveals that the number of array elements determines the maximum number of signals that can be estimated within a subband, while the equivalent total number of channels of the reception system determines the maximum number of signals that the system can estimate. Our analysis shows that the proposed sampling structure offers a greater flexibility in structural parameter selection and system design. Finally, simulations show that under the condition of the same or similar average sampling rate, the proposed structure and corresponding methods can achieve higher spectrum and DOA estimation accuracy.

关键词： Estimation Sensors Receivers Hardware Arrays Direction-of-arrival estimation Costs Channel estimation signal processing algorithms Frequency estimation Spectrum sensing DOA estimation sub-Nyquist sampling

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Distributed Distortion-Aware Beamforming Designs for Cell-Free mMIMO Systems

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2025年第2期19卷 381-397页

作者： Liu, Mengzhen Li, Ming Liu, Rang Liu, Qian Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China Univ Calif Irvine Ctr Pervas Commun & Comp Irvine CA 92697 USA Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China

Cell-free massive multi-input multi-output (CF-mMIMO) systems have emerged as a promising paradigm for next-generation wireless communications, offering enhanced spectral efficiency and coverage through distributed antenna arrays. However, the non-linearity of power amplifiers (PAs) in these arrays introduce spatial distortion, which may significantly degrade system performance. This paper presents the first investigation of distortion-aware beamforming in a distributed framework tailored for CF-mMIMO systems, enabling pre-compensation for beam dispersion caused by nonlinear PA distortion. Using a third-order memoryless polynomial distortion model, the impact of the nonlinear PA on the performance of CF-mMIMO systems is firstly analyzed by evaluating the signal-to-interference-noise-and-distortion ratio (SINDR) at user equipment (UE). Then, we develop two distributed distortion-aware beamforming designs based on ring topology and star topology, respectively. In particular, the ring-topology-based fully-distributed approach reduces interconnection costs and computational complexity, while the star-topology-based partially-distributed scheme leverages the superior computation capability of the central processor to achieve improved sum-rate performance. Extensive simulations demonstrate the effectiveness of the proposed distortion-aware beamforming designs in mitigating the effect of nonlinear PA distortion, while also reducing computational complexity and backhaul information exchange in CF-mMIMO systems.

关键词： Nonlinear distortion Array signal processing Matrix decomposition Vectors signal processing algorithms Interference Backhaul networks Analytical models Radio frequency Phase distortion Cell-free nonlinear distortion beamforming distributed signal processing

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An Efficient and Accurate RCS Reconstruction Technique Using Adaptive TLS-ESPRIT Algorithm

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2024年第1期23卷 49-53页

作者： Zhu, Fang-Yin Chai, Shui-Rong Zou, Yu-Feng He, Zhen-Xiang Guo, Li-Xin Xidian Univ Sch Phys Xian 710071 Peoples R China

In this letter, an adaptive total least squares-estimating signal parameter via rotational invariance techniques (TLS-ESPRIT) algorithm based on the analytical perspective is proposed to efficiently construct the scattering center model (SCM) and reconstruct the radar cross section (RCS) of a perfect electrical conductor (PEC) target. Compared with existing research, the proposed method does not require knowing the number of scattering centers, which means that it can be applied to the construction of SCMs for noncooperative targets. Compared with inverse synthetic aperture radar (ISAR) and statistical methods, this work strikes a balance between accuracy and efficiency through optimization of the core technology in the RCS reconstruction process. The effectiveness of the algorithm is verified by comparing the results derived by our method with those from the measurement.

关键词： Scattering Adaptation models signal processing algorithms Radar cross-sections Radar scattering Image reconstruction Statistical analysis Adaptive TLS-ESPRIT algorithm Hankel matrix one-dimensional (1-D) scattering center radar cross section (RCS) reconstruction

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Cooperative Positioning of Wireless Networks in Complex Propagation Environments

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第10期42卷 2877-2889页

作者： Jiang, Peiyue Gu, Xiaobo Zhou, Haibo Guangdong Univ Technol Sch Integrated Circuits Guangzhou 510006 Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing 210008 Peoples R China

Cooperative positioning in wireless networks has attracted great attention in recent years, as many applications require the exact location of all member nodes. The pairwise distance between the member nodes is conventionally constructed as an Euclidean Distance Matrix (EDM) for subsequent location estimation. In this paper, we address the problem of cooperative positioning in complex propagation environments, which results in an incomplete EDM. We proposed an improved EDM recovery algorithm based on low tank matrix completion (LRMC), which makes use of the sensor correlation by Laplacian and trace minimization. In addition, we derive a semi-definite relaxation estimator to localize the unknown sensors. Simulations are conducted to evaluate the performance of the proposed algorithm and the results show that the proposed method outperforms existing ones in both matrix completion and positioning accuracy.

关键词： Distance measurement Correlation Wireless sensor networks signal processing algorithms Position measurement Minimization Accuracy Cooperative positioning graph Laplacian low rank matrix completion manifold learning semi-definite programming

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Robust CFAR Detector Based on CLEAN for Sidelobe Suppression

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IEEE SENSORS JOURNAL 2024年第9期24卷 14930-14942页

作者： Zhu, Jiachen Huang, Xiaohong Liu, Jiayu Deng, Zhenmiao North China Univ Sci & Technol Sch Artificial Intelligence Tangshan 063210 Peoples R China Hebei Key Lab Ind Intelligent Percept Tangshan 063210 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 518107 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Guangzhou 510275 Peoples R China

In response to challenges posed by abnormal interference, such as target sidelobes within the reference window, this article proposes a sidelobe suppression-constant false alarm rate (SS-CFAR) detector. Drawing on the CLEAN concept, the SS-CFAR is designed for target detection in indoor environments with multiple targets using millimeter (mm)-wave radar. It uses second-order difference statistics combined with the Monte Carlo (MC) algorithm and iterative censoring (IC) to separate target signals from background clutter in radar echoes. In addition, sidelobe suppression (SS) for the target is achieved by reconstructing the target's echo information and the CLEAN concept. The simulation and actual experimental results demonstrate that SS-CFAR delivers superior detection performance, obviating the need for prior knowledge of anomalies or manual setting of parameters such as the number of reference and guard cells, which traditionally depend on experiential adjustments. Compared with mean class and ordered statistics (OS) class CFAR detectors, SS-CFAR transcends reference window constraints. It accurately estimates background clutter, enhancing detection capabilities in multitarget environments. This innovative approach not only showcases superior detection performance and adaptability but also paves the way for the broader application of mm-wave radar in complex indoor scenarios.

关键词： Clutter signal processing algorithms Object detection Radar detection Detectors Millimeter wave radar Sensors CLEAN constant false alarm rate (CFAR) interfering targets millimeter (mm)-wave radar reference window sidelobe suppression (SS) target detection

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