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SENSOR ARRAY signal-processing VIA A PROCRUSTES ROTATIONS BASED EIGENANALYSIS OF THE ESPRIT DATA PENCIL

IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND signal processing 1989年第6期37卷 832-861页

作者： ZOLTOWSKI, MD STAVRINIDES, D School of Electrical Engineering Purdue University West Lafayette IN USA

The authors propose the procrustes-rotation-based ESPRIT algorithm, PRO-ESPRIT, for estimating characteristic parameters associated with a scene of radiating sources, given data derived from two identical sensor arrays, the X and Y arrays, which are displaced relative to one another. PRO-ESPRIT uses the inherent redundancy built into the ESPRIT array system to compensate for deviations from the ideal array model. This redundancy is manifested by two fundamental properties of the noiseless X and Y data matrices comprising the ESPRIT data pencil: (a) they have the same row space, the signal subspace; and (2) they have the same column space, the source subspace. These properties are utilized by invoking two primary tools: (1) the solution to the procrustes problem for optimally approximating an invariant subspace rotation; and (2) the total-least-squares method for perturbing each of two estimates of a common subspace in a minimal fashion until the two perturbed spaces are the same.< >

关键词： Sensor arrays Array signal processing signal processing algorithms Sensor phenomena and characterization Multiple signal classification Least squares methods Parameter estimation Least squares approximation Linear systems Differential equations

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Efficient Residual Shrinkage CNN Denoiser Design for Intelligent signal processing: Modulation Recognition, Detection, and Decoding

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2022年第1期40卷 97-111页

作者： Zhang, Lin Yang, Xiaoling Liu, Heng Zhang, Haotian Cheng, Julian Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai 519000 Peoples R China Univ British Columbia Sch Engn Kelowna BC V1V 1V7 Canada

The noises embedded in signals will degrade the signal processing quality. Traditional denoising algorithms might not work in practical systems since the statistical characteristics of noises might not be learned. To address this issue, we propose an efficient residual shrinkage convolutional neural network (RSCNN) aided denoiser based on the principle of the domain transformation, shrinking and inverse transforming operations conducted by the traditional denoiser. The proposed RSCNN is composed by the batch normalization layer, domain transformation layers, the shrinkage module and inverse transformation layers, wherein transformation layers consist of convolutional layers and the nonlinear activation function. Moreover, we propose a thresholds learning subnetwork to automatically determine the thresholds, so as to enhance noise suppressing performances. Furthermore, we compose the data set by preprocessing the received signals, and design the loss function according to different denoising requirements. To validate the efficiency and universality of the RSCNN aided denoiser, we apply the proposed RSCNN denoiser to three different application scenarios, including the modulation recognition, detection and decoding. After the offline training, at the online deployment stage, we utilize the RSCNN denoiser to reduce the noise power and improve the signal to noise ratios. Simulation results demonstrate that the proposed intelligent denoiser can efficiently improve the signal processing capabilities to achieve higher modulation recognition accuracy, better detection and decoding performances with lower complexity than benchmark schemes.

关键词： Noise reduction Decoding Modulation Feature extraction Convolutional neural networks signal processing algorithms Training Noise suppression residual shrinkage convolutional neural network aided denoiser modulation recognition detection belief propagation decoding

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A Ground-Penetrating Radar Clutter Suppression Algorithm Integrating signal processing and Image Fusion

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Tang, Xiao-Song Yang, Feng Qiao, Xu Liu, Jia-Lin Li, Fan-Ruo Wen, Yong-Liang Fan, Zhi-Hai Qi, Zhen-Hong Yang, Zhi-Hua China Univ Min & Technol Beijing Sch Artificial Intelligence Beijing 100083 Peoples R China Guangzhou Railway Grp Corp Guangzhou 510000 Peoples R China Shaanxi Shaanmei Tongchuan Min Co Ltd Tongchuan 727000 Shaanxi Peoples R China Shanxi Huayang Grp New Energy Co Ltd Yangquan 045000 Shanxi Peoples R China

This study presents a clutter suppression algorithm for underground mine ground-penetrating radar (GPR) B-scan profiles, integrating the sparrow search algorithm (SSA), variational mode decomposition (VMD), and image fusion via the improved nonsubsampled shearlet transform (INSST), aimed at addressing clutter under strict explosion-proof conditions. Initially, the raw B-scan data undergoes preprocessing to derive a grayscale profile. Using SSA, an envelope entropy fitness function is introduced to automatically optimize VMD with respect to the average trajectory of the grayscale profile. Finally, enhanced non-subsampled shearlet transform (NSST) processing is applied to the synthesized profile using a scale-direction adaptive threshold with an L2 norm square constraint and a flexible threshold function. This approach reduces Gibbs artifacts and oversmoothing while retaining valid signals within the threshold range. In simulated profiles, the proposed algorithm demonstrates strong robustness across different noise standard deviations, significantly improving image quality metrics such as peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM), while reducing the root-mean-square error (RMSE). Notably, in measured mining profiles, the proposed algorithm outperforms eight other methods by better preserving edge details. Compared to the suboptimal algorithm, the target-to-noise ratio (TNR) improves by 36% in mining fault profile I and by 17% in coal seam floor profile II. Finally, a sensitivity analysis of the proposed algorithm's parameters was conducted.

关键词： Clutter signal processing algorithms Geoscience and remote sensing Radar Noise reduction Wavelet transforms Sensitivity analysis Location awareness Geology Entropy Clutter suppression ground-penetrating radar (GPR) improved nonsubsampled shearlet transform (INSST) sparrow search algorithm (SSA) variational mode decomposition (VMD)

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Networked signal and Information processing: Learning by multiagent systems

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IEEE signal processing MAGAZINE 2023年第5期40卷 92-105页

作者： Vlaski, Stefan Kar, Soummya Sayed, Ali H. Moura, Jose M. F. Imperial Coll London SW7 2AZ England Ecole Polytech Fed Lausanne Adapt Syst Lab Lausanne Switzerland Princeton Univ Elect Engn Dept Princeton NJ USA Ecole Polytech Fed Lausanne CH-1015 Lausanne Switzerland Univ Calif Los Angeles Elect Engn Los Angeles CA USA US Natl Acad Engn Washington DC USA World Acad Sci Trieste Italy Carnegie Mellon Univ Pittsburgh PA 15213 USA Amer Assoc Advancement Sci Washington DC USA Natl Acad Inventors Tampa FL USA Portugal Acad Sci Lisbon Portugal Natl Acad Engn New Delhi India

This article reviews significant advances in networked signal and information processing (SIP), which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decisions and actions. Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions while offering the potential for improved privacy, increased resilience, and conserved resources. A longer version of this manuscript, with examples and illustrative applications, is available at https://***/abs/2210.13767.

关键词： Privacy Protocols Network topology Scalability signal processing algorithms Information processing Robustness

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Channel Capacity Estimation of Communication Systems with Digital Antenna Arrays for Nonlinear Model of Low-Frequency Noises

Channel Capacity Estimation of Communication Systems with Di...

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Digital signal processing and its Applications (DSPA), International Conference on

作者： Alexander Parshin Maxim Grachev LLC “Sphera Lab” Ryazan Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The paper is devoted to modeling algorithms for estimating channel transmission coefficients in a communication system with digital antenna arrays taking into account mutual coupling, the complex of flicker, phase and thermal noises, and integrated signal processing. The primary signal processing in the path of each element of the antenna array is considered and the effect of noise from both individual elements of antenna systems and digital processing noise is evaluated. The influence of a complex of noises on the communication system's channel capacity is revealed by channel capacity estimation for parallel method of signal receiving. Methods for increasing the efficiency of digital signal processing, filtering and noise compensation in the communication system are proposed.

关键词： Phased arrays Adaptation models Channel capacity Communication systems Array signal processing Low frequency noise Noise signal processing algorithms Digital signal processing Antenna arrays

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A Fast Saturation Based Dehazing Framework with Accelerated Convolution and Attention Block

A Fast Saturation Based Dehazing Framework with Accelerated ...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shuocheng Wang Jiaming Liu Yilian Zhong Ruoxi Zhu Jiazheng Lian Hao Zhang Yibo Fan State Key Laboratory of Integrated Chips and Systems Fudan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Real-time image dehazmg is crucial for applications such as autonomous driving, surveillance, and remote sensing, where haze can significantly reduce visibility. However, many deep learning algorithms are hindered by large model sizes, making real-time processing difficult to achieve. Several fast and lightweight dehazing networks rely on estimating K(x), but they often fail to deliver satisfying performance. In this paper, we present a novel fast dehazing framework built upon the saturation-based algorithm. We design a new convolution module called Feature Extraction Partial Convolution (FEPC), which is faster and achieves better performance than the vanilla 3×3 convolution. Additionally, we fully leverage the information redundancy between feature map channels by dividing it into two parts along the channel dimension and designing a Self-Cross Attention Block (SCAB). The reduction in channel count significantly reduces computational load and improves the framework’s speed. Through extensive experiments, our method demonstrates not only a fast inference speed but also superior dehazing performance, providing a promising solution for real-time practical deployment. Our code will be available at https://***/superwscZFSB-Dehazing-Framework.

关键词： Deep learning Image quality Convolution Surveillance Redundancy signal processing algorithms Feature extraction Real-time systems Speech processing Remote sensing

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signal processing Algorithm and System Realization of Broadband Parametric Array Sub-Bottom Profiler

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IEEE SENSORS JOURNAL 2022年第17期22卷 17090-17102页

作者： Zhou, Hanyun Li, Wei Huang, S. H. Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Huawei Technol Co Ltd Intelligent Vehicle Solut Business Unit Hangzhou 310051 Peoples R China Zhejiang Univ Ocean Coll Zhoushan 316000 Peoples R China

Parametric acoustic array can generate a low frequency narrow sound beam with sufficient penetrating ability with a small size transducer. Besides, the generated beam has almost no side lobe, and it is easy to form a wide band signal at low frequencies, thus effectively improving the signal processing gain. Therefore, broadband parametric array shows advantages in shallow water sub-bottom profiling. In this paper, the signal processing algorithm of transmitting end and receiving end of parametric array sonar was studied, and a parametric array sub-bottom profiler was designed and realized for experimental verification. Based on Berktay's far-field solution, the performance of different modulation methods was analyzed and compared, and suppressed carrier double-sideband modulation was chosen as the optimal modulation method. Furthermore, corresponding echo signal processing algorithm was designed to improve the detection resolution. According to lake trials of the broadband parametric array sub-bottom profile measurement system as designed, a clear sub-bottom profile was obtained, which verified the system's effectiveness.

关键词： Frequency modulation Array signal processing Acoustic beams Broadband communication Acoustic arrays signal processing algorithms Transducers Parametric acoustic array sub-bottom profiler signal processing system realization

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Optimal signal processing in Ground-Based Forward Scatter Micro Radars

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2012年第4期48卷 3006-3026页

作者： Hu, Cheng Sizov, V. Antoniou, M. Gashinova, M. Cherniakov, M. Beijing Inst Technol Dept Elect Engn Beijing 100081 Peoples R China Univ Birmingham Birmingham B15 2TT W Midlands England Moscow Inst Elect Technol Moscow Russia

The received signal in forward scatter radar (FSR) depends on the target's electrical size and trajectory, which are unknown a priori. As a result, in practical situations, it is impossible to obtain the accurate reference function at the reception side, and adaptation of optimal filtering is therefore proposed for this case. This paper presents a signal processing algorithm for ground target detection using FSR, which includes the construction of the adaptive reference functions and the identification of target velocity and its observation time. Furthermore, the algorithm performance is analytically determined under practical motion trajectories such as different motion directions and baseline crossing points, which indicate the effectiveness of the proposed algorithm in a practical case for FSR. The effectiveness of the algorithm is shown using both simulated and experimental data. Finally, the resolution in convoy targets in ground-based FSR is analytically obtained for the first time;the resolution is totally different from the resolution in conventional radar theory because of the target signature characteristics in ground-based FSR. The majority of the analytical results are verified experimentally.

关键词： optimal filters Resolving power trajectory radar temporal resolution signal processing algorithms algorithms Object detection signal processing received signal Radar theory Flexed Sidebent Rotated

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On Noise-Sensitivity of Unlimited Sampling in Line Spectral Estimation

On Noise-Sensitivity of Unlimited Sampling in Line Spectral ...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Wenlong Wang Zai Yang School of Mathematics & Statistics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The unlimited sampling framework, utilizing modulo analog-to-digital converters (ADCs), has recently been introduced to mitigate the information loss caused by the dynamic range limitations of traditional ADCs. In this paper, we evaluate the noise sensitivity of unlimited sampling in line spectral estimation by deriving the Cramér–Rao bound (CRB) and show that estimation based solely the modulo measurements can be sensitive to noise. To address this issue, we propose integrating the modulo ADC-based unlimited sampling with the sign information of the original signal. We compare the CRBs of different unlimited sampling frameworks and show the benefit of enhanced robustness to noise by the sign-aided approach.

关键词： Sensitivity Parameter estimation Noise Estimation signal processing algorithms Dynamic range Robustness Noise measurement Speech processing Analog-digital conversion

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So Far Yet So Near: Time Series Data Augmentation with Exploring non-Semantic Boundaries based on Reinforcement Learning

So Far Yet So Near: Time Series Data Augmentation with Explo...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Haoran Li Zhibo Zhang Jiarong Kang Xun Jiang Xiaoli Gong Jin Zhang Zhe Sun Andrzej Cichocki College of Computer Science Nankai University Tianjin China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Medicine and Faculty of Health Data Science Juntendo University Japan Polish Academy of Science Systems Research Institute Warsaw Poland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Data augmentation effectively expands feature distribution in time series classification, enhancing downstream task performance. However, existing techniques often fail to maintain semantic consistency between augmented and original time series data, causing label noise and thereby degrading downstream task performance. We argue that data augmentation should preserve time series semantic consistency and expand the non-semantic information space. In this paper, we reformulate data augmentation as a semantic path planning problem between original data and augmented data, modeled as a Markov Decision Process (MDP). We propose a reinforcement learning-based algorithm (RL) named FreqSYN, where the action space is defined by a set of learnable Gaussian kernels that perturbs the frequency domain of the original data to generate augmented samples. The confidence coefficients of augmented data in semantically relevant classification tasks are used as a reward to iteratively refine the FreqSYN. Our method is validated across four datasets, achieving state-of-the-art performance, with a 2% improvement in F1 score over the SimPSI method. The code and models are available at https://***/NKU-EmbeddedSystem/FreqSYN.

关键词： Semantics Time series analysis signal processing algorithms Reinforcement learning Data augmentation Prediction algorithms Path planning Data models Kernel Speech processing

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