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Swarm Sparse Range Profile Recovery From Fewer Measurements of FMCW Radar

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第5期60卷 7056-7074页

作者： Wang, Longhui Ai, Zhanyang Wang, Qiexiang Wang, Jian Zhang, Xudong Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Dept Space Microwave Remote Sensing Syst Beijing 100190 Peoples R China

In this article, we propose an algorithm for recovering swarm sparse range profiles from fewer measurements of frequency-modulated continuous-wave (FMCW) radar. First, we give the FMCW radar signal model and show the drawbacks of existing algorithms for the swarm sparse range profile recovery. Next, we propose an algorithm, called swarm orthogonal matching pursuit (sOMP), which recovers swarm sparse signals by exploiting the cluster feature of swarm targets in radar scenes. The highlights of sOMP lie in the two element index selection modes and the likelihood-ratio-based mode selection strategy. The probability distributions involved in the likelihood ratio are derived from the Gaussian approximation of the sensing matrix taken from the complex uniform spherical ensemble or the complex Gaussian ensemble. For the application of sOMP in FMCW radar, we propose two schemes to address the issue of Gaussian approximation of the sensing matrix taken from the Fourier ensemble. Then, the performance of sOMP is analyzed theoretically. In particular, under equivalent parameter settings, the sufficient conditions derived for sOMP can also be regarded as those for generalized orthogonal matching pursuit (gOMP). Compared with latest sufficient conditions for gOMP, our results are looser in terms of the constraint of the minimum magnitude of nonzero elements. Numerical simulations confirm that sOMP in both schemes outperforms the state-of-the-art recovery algorithms. Moreover, real FMCW radar experiments with drone swarms also demonstrate the superior performance of sOMP.

关键词： Radar Matching pursuit algorithms Drones signal processing algorithms Radar signal processing Radar measurements Airborne radar Drone swarm frequency-modulated continuous-wave (FMCW) radar orthogonal matching pursuit (OMP) range profile swarm sparse recovery

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Adaptive-Wavelet-Threshold-Function-Based M2M Gaussian Noise Removal Method

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IEEE INTERNET OF THINGS JOURNAL 2024年第20期11卷 33177-33192页

作者： Li, Shuang Liu, Shuai Wang, Jingjing Yan, Shefeng Liu, Jiahao Du, Zehua Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China Univ Chinese Acad Sci Inst Acoust Beijing 100190 Peoples R China Ocean Univ China Fac Informat Sci & Engn Qingdao 266100 Shandong Peoples R China Ocean Univ China Fac Informat Sci & Engn Qingdao Peoples R China

With little or no human intervention, almost every object in Internet of Things (IoT) has ability to communicate, sense, and process information to make everything connected. Noise in complex environment has a great impact on machine-to-machine (M2M) interaction in IoT. Adaptive wavelet threshold function (AWTF)-based M2M Gaussian Noise Removal Method is proposed in this article. First, a bilateral enhanced wavelet threshold function is derived based on the adjustable zeroing window. Further, threshold is used to construct a bilateral enhanced wavelet threshold function, which can eliminate the oscillations in the existing wavelet threshold function. This ensures that there is no break in wavelet coefficients during the reconstruction process and that stable wavelet decomposition and reconstruction can be achieved. The signal-to-noise ratio (SNR) of a signal is estimated based on the variance of the noise-containing signal, and the zeroing window parameters are adjusted adaptively according to the SNR value to eliminate the noisy wavelet coefficients and improve the denoising performance. In addition, when the wavelet coefficients are reconstructed, the proposed algorithm can select a suitable threshold function according to a particular threshold value, which improves the robustness of the algorithm. "Doppler" and "Bumps" standard test signals are used as interactive signals to simulate the proposed algorithms. For "Doppler" signals, the SNR, root mean square error (RMSE), and noise suppression ratio (NSR) of AWTF increased by 8.48%, 42.59%, and 1.69%, respectively, compared with the GDES+ABC algorithm. For "Bumps" signal, the SNR, RMSE, and NSR of AWTF are improved by 11.67%, 24.46%, and 2.99%, respectively, compared with GDES+ABC algorithm. In addition, we also use different modulated signals to carry out real field experiments in Qingdao cruise ship home port, which proves the effectiveness of the proposed algorithm.

关键词： Noise reduction signal processing algorithms Internet of Things Gaussian noise Wavelet transforms signal to noise ratio Wavelet domain Adaptive algorithm machine-to-machine interaction threshold function wavelet transform (WT)

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Sparse Training for Federated Learning With Regularized Error Correction

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2024年第6期18卷 1085-1099页

作者： Greidi, Ran Cohen, Kobi Bengur Univ Negev Sch Elect & Comp Engn IL-8410501 Beer Sheva Israel

Federated Learning (FL) is an emerging paradigm that allows for decentralized machine learning (ML), where multiple models are collaboratively trained in a privacy-preserving manner. However, training DNN models in FL systems face challenges such as elevated computational and communication costs in complex tasks. Sparse training schemes gain increasing attention in order to scale down the dimensionality of each client transmission. Specifically, sparsification with error correction methods is a promising technique, where only important updates are sent to the parameter server (PS) and the rest are accumulated locally. While error correction methods have shown to achieve a significant sparsification level without harming convergence, pushing sparsity further remains unresolved due to the staleness effect. In this paper, we propose a novel algorithm, dubbed Federated Learning with Accumulated Regularized Embeddings (FLARE), to overcome this challenge. FLARE presents a novel sparse training approach via accumulated pulling of the updated models with regularization on the embeddings in the FL process, providing a powerful solution to the staleness effect, and pushing sparsity to an exceptional level. Our theoretical analysis demonstrates that FLARE not only matches state-of-the-art performance in terms of convergence rate with time, but also achieves significant improvements in scalability with sparsity parameter. The empirical performance of FLARE is validated through extensive experiments on diverse and complex models, achieving a remarkable sparsity level (10 times and more beyond the current state-of-the-art) along with significantly improved accuracy. Additionally, an open-source software package has been developed for the benefit of researchers and developers in related fields.

关键词： Optimization Training Computational modeling Data models Error correction Convergence signal processing algorithms Deep learning deep neural network (DNN) communication-efficiency federated learning (FL) sparse training

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algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative Values

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5187-5197页

作者： Green, Dylan Bailey, Stephen Univ Calif Irvine Dept Phys & Astron Irvine CA 92697 USA Lawrence Berkeley Natl Lab Berkeley CA 94720 USA

Non-negative matrix factorization (NMF) is a dimensionality reduction technique that has shown promise for analyzing noisy data, especially astronomical data. For these datasets, the observed data may contain negative values due to noise even when the true underlying physical signal is strictly positive. Prior NMF work has not treated negative data in a statistically consistent manner, which becomes problematic for low signal-to-noise data with many negative values. In this paper we present two algorithms, Shift-NMF and Nearly-NMF, that can handle both the noisiness of the input data and also any introduced negativity. Both of these algorithms use the negative data space without clipping or masking and recover non-negative signals without any introduced positive offset that occurs when clipping or masking negative data. We demonstrate this numerically on both simple and more realistic examples, and prove that both algorithms have monotonically decreasing update rules.

关键词： Noise Noise measurement signal processing algorithms Solids Vectors Standards Fluctuations Fitting Contracts Codes Non-negative matrix factorization (NMF) dimension reduction noisy data weighted NMF negative data

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Main-Lobe Beamwidth Constrained Target Localization Using Bistatic Range Measurements

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 289-304页

作者： Mao, Zhi Su, Hongtao Shen, Lu Jing, Xinchen Jia, Congyue Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

This paper addresses the localization of a target in 3D scenarios using bistatic range measurements with a single passive receiver and multiple transmitters. The main-lobe beamwidth of the receiving antenna contains valuable a-priori information about the target position but is commonly overlooked. This study introduces an angular constraint to incorporate the beamwidth information into the elliptic localization, resulting in a Quadratically Constrained Quadratic Programming (QCQP) problem. A novel Two-Stage Main-lobe beamwidth Constrained Estimation (TSMCE) algorithm is proposed to solve this challenging localization problem. Theoretical analysis demonstrates that the algorithm can converge to the candidate solutions that satisfy the Karush-Kuhn-Tucker (KKT) optimality conditions. The corresponding covariance can achieve the Cramer-Rao Lower Bound (CRLB) under a small noise assumption. Numerical simulations confirm that the proposed algorithm achieves superior localization accuracy compared to other methods, particularly in the low signal to Noise Ratio (SNR) regime. Additionally, the algorithm demonstrates fast convergence and robustness to variations in localization geometry.

关键词： Location awareness signal processing algorithms Antenna measurements Receiving antennas Mathematical models Three-dimensional displays signal to noise ratio Passive localization bistatic range main-lobe beamwidth angular constraint two-stage algorithm

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Iterative Closest Point via MultiKernel Correntropy for Point Cloud Fine Registration

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IEEE signal processing LETTERS 2025年 32卷 766-770页

作者： Yi, Hao Hu, Limei Chen, Feng Ren, Xiaoping Duan, Shukai Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China

The Iterative Closest Point (ICP) method, primarily used for transformation estimation, is a crucial technique in 3D signal processing, especially for point cloud fine registration. However, traditional ICP is prone to local optima and sensitive to noise, especially when there is no good initialization. Based on the observation that registration errors typically exhibit a multimodal distribution under large rotational offsets and noisy environments, the MultiKernel Correntropy (MKC), which can estimate the registration error distribution, is introduced to provide global information for ICP. Moreover, since MKC consists of multiple Gaussian kernels, it can effectively resist most of the noise. A MultiKernel Correntropy based Iterative Closest Point (MKCICP) is proposed. Extensive experiments on both simulated and real-world datasets show that MKCICP achieves better performance compared to other related methods in challenging scenarios involving large rotational angles, low partial overlap, and high noise levels.

关键词： Point cloud compression Noise Iterative methods Optimization Kernel Vectors signal processing algorithms Mathematical models Loss measurement Linear programming MultiKernel Correntropy (MKC) Iterative Closest Point (ICP) transformation estimation signal processing global optimum point cloud registration

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Resource Allocation for Near-Field Communications: Fundamentals, Tools, and Outlooks

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IEEE WIRELESS COMMUNICATIONS 2024年第5期31卷 42-50页

作者： Xu, Bokai Zhang, Jiayi Du, Hongyang Wang, Zhe Liu, Yuanwei Niyato, Dusit Ai, Bo Letaief, Khaled B. Beijing Jiaotong Univ Beijing Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China Nanyang Technol Univ Interdisciplinary Grad Program Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Queen Mary Univ London London England Hong Kong Univ Sci & Technol Hong Kong Peoples R China

Extremely large-scale multiple-input-multiple-out-put (XL-MIMO) is a promising technology to achieve high spectral efficiency (SE) and energy efficiency (EE) in future wireless systems. The larger array aperture of XL-MIMO makes communication scenarios closer to the near-field region. Therefore, near-field resource allocation is essential in realizing the above key performance indicators (KPIs). Moreover, the overall performance of XL-MIMO systems heavily depends on the channel characteristics of the selected users, eliminating interference between users through beamforming, power control, and so on. The above resource allocation issue constitutes a complex joint multi-objective optimization problem since many variables and parameters must be optimized, including the spatial degree of freedom, rate, power allocation, and transmission technique. In this article, we review the basic properties of near-field communications and focus on the corresponding "resource allocation" problems. First, we identify available resources in near-field communication systems and highlight their distinctions from far-field communications. Then, we summarize optimization tools, such as numerical techniques and machine learning methods, for addressing near-field resource allocation, emphasizing their strengths and limitations. Finally, several important research directions of near-field communications are pointed out for further investigation.

关键词： Resource management Optimization Antenna arrays signal processing algorithms Communication systems Channel estimation Array signal processing

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Centroid-Free K-Means With Balanced Clustering

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IEEE signal processing LETTERS 2025年 32卷 1191-1195页

作者： Meng, Bin Li, Fangfang Yang, Fan Gao, Quanxue Jilin Police Coll Dept Informat Secur Technol Changchun 130117 Peoples R China Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Changchun Humanities & Sci Coll Dept Chinese Language & Literature Changchun 130117 Peoples R China

Currently, a wide array of clustering algorithms have emerged, yet many approaches rely on K-means to detect clusters. However, K-means is highly sensitive to the selection of the initial cluster centers, which poses a significant obstacle to achieving optimal clustering results. Moreover, its capability to handle nonlinearly separable data is less than satisfactory. To overcome the limitations of traditional K-means, we draw inspiration from manifold learning to reformulate the K-means algorithm into a new clustering method based on manifold structures. This method not only eliminates the need to calculate centroids in traditional approaches, but also preserves the consistency between manifold structures and clustering labels. Furthermore, we introduce the l(2,1)-norm to naturally maintain class balance during the clustering process. Additionally, we develop a versatile K-means variant framework that can accommodate various types of distance functions, thereby facilitating the efficient processing of nonlinearly separable data. The experimental results of several databases confirm the superiority of our proposed model.

关键词： Clustering algorithms Manifolds Manifold learning signal processing algorithms Optimization Linear matrix inequalities Kernel Training Geometry Data mining Clustering l(2,1)-norm manifold learning K-means

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Joint Pulsed and Continuous Wave Interference Suppression for Dual-Antenna GNSS Receivers

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第2期60卷 2319-2330页

作者： Wang, Wenyi Chen, Haojie Lu, Xuyang Civil Aviat Univ China Tianjin Key Lab Adv Signal Proc Tianjin 300300 Peoples R China

Array antenna is an effective way to improve the interference suppression capability of global navigation satellite System (GNSS) receivers. However, more antennas mean higher costs, which may lead to unacceptable costs in the civil fields. Dual-antenna provides a better compromise between cost and interference suppression capability for those applications in the civil fields. This article proposes a joint-pulsed and continuous wave (CW) interference suppression algorithm for dual-antenna GNSS receivers. To avoid the lack of freedom of the dual antenna, pulsed and CW interference will be suppressed separately. First, in order to avoid the impact of pulses on CW interference suppression capability, a robust covariance matrix estimation method and space-time adaptive processing power inversion algorithm are combined to suppress CW interference. In this stage, the pulses will be deliberately preserved as much as possible. Second, the residual pulses will be suppressed by utilizing a robust pulsed interference suppression method. Simulations and experiments demonstrate that the proposed algorithm can simultaneously suppress joint pulsed and CW interference for a dual-antenna GNSS receiver.

关键词： Interference Interference suppression Estimation Covariance matrices signal processing algorithms Global navigation satellite system Satellites

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2-D Autofocus for High-Squint SAR Based on Affine Coordinate Back-Projection Algorithm

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Lou, Yishan Lin, Hao Chen, Quan Xing, Mengdao Zhou, Shengwei Sun, Guang-Cai Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Shanghai Inst Satellite Engn Shanghai 200240 Peoples R China Xidian Univ Acad Adv Interdisciplinary Res Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

When synthetic aperture radar (SAR) works in high-squint (HS) mode, the interpolation and scaling operation of traditional frequency domain imaging algorithms will change the original structure of motion error, and lead to imaging difficulties. As a classical time-domain imaging algorithm, the back-projection (BP) algorithm is linear processing with a high tolerance for motion error. Therefore, the BP algorithm is very suitable for HS SAR imaging. To further improve the estimation accuracy of motion error, an innovative affine coordinate (AC) system is introduced into the BP algorithm. Based on this AC system, a novel 2-D autofocus algorithm is proposed, which can more accurately estimate and correct the 2-D phase error of the HS SAR BP image. The proposed algorithm has the following advantages: 1) the AC imaging grid is established according to the proposed resolution calculation method based on the BP image spectrum. Under this imaging grid, the nonsystem range cell migration (NsRCM) and the range defocus term of the BP image are significantly reduced, making the phase error estimation more accurate;2) a spectrum alignment processing for BP image in the AC system is proposed to remove the spectrum aliasing so that the azimuth phase error (APE) can be accurately estimated;and 3) the spectrum of the BP image is orthogonal in the AC system, which makes the 2-D phase error compensation based on the established prior phase error structure more accurate. Simulation and real data experiments validate the performance of the proposed algorithm.

关键词： Imaging Synthetic aperture radar Accuracy signal processing algorithms Radar polarimetry Interpolation Radar imaging Frequency-domain analysis Time-domain analysis Image resolution 2-D autofocus affine coordinate (AC) system back-projection (BP) algorithm high-squint (HS) synthetic aperture radar (SAR)

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