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Sparse signal Phase Retrieval for Phaseless Short-Time Fourier Transform Measurement Based on Local Search

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Li, Xiaodong Zheng, Pinjun Fu, Ning Qiao, Liyan Al-Naffouri, Tareq Y. Harbin Inst Technol HIT Sch Elect & Informat Engn Harbin 150080 Peoples R China King Abdullah Univ Sci & Technol KAUST Elect & Comp Engn Program Comp Elect & Math Sci & Engn CEMSE Thuwal 239556900 Saudi Arabia

The sparse signal phase retrieval (PR) for phaseless short-time Fourier transform (STFT) measurement is a crucial problem manifesting across various applications. The existing solutions involve amplitude and support estimation. Amplitude estimation, a nonlinear least squares problem, faces issues due to the not full rank of the derivative matrix associated with the objective function. Existing support estimation relies on random initialization, reducing accuracy and noise robustness. To address these, a novel phaseless measurement structure and the corresponding solution framework are proposed. Initially, a measurements preprocessing algorithm is employed, utilizing the properties of the measurement matrix to efficiently reduce the dimensions of the solution. Subsequently, a support estimation algorithm based on local search is developed, where the support preestimation takes advantage of the sparse support characteristics. In addition, an amplitude estimation algorithm, utilizing the trust region method, is proposed. The proposed algorithm's effectiveness and its superiority in accuracy and noise robustness over existing methods are demonstrated through numerical simulations and hardware experiments.

关键词： Phase measurement Antenna measurements Acoustic measurements Noise measurement signal processing algorithms Optical variables measurement Estimation Local search short-time Fourier transform (STFT) sparse phase retrieval (PR) trust region method

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Measuring The Fatigue of Persons Based on EOG signal processing Mmethods

Measuring The Fatigue of Persons Based on EOG Signal Process...

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International Conference on Engineering Technology and their Applications (IICETA)

作者： Hassanein R. Mahmood Mohammed S. Irshayyid Manaf K. Hussien Basim k. J. Al-Shammari Department of Electrical Engineering College of Engineering University of Wasit Wasit Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

Driver fatigue is one of the major causes of a road accidents. Various driver fatigue detection systems have been designed to detect and warn the driver of impending fatigue. Most available prototypes and ongoing research have focused on the video-based eye-tracking system, which demands high computing power due to real-time video processing. In our research, the use of electrooculography (EOG) as an alternative to video-based systems in detecting eye activities caused by fatigue is evaluated. The cornea retinal potential (CRP) is the source of the EOG signals, which are being generated by the movement of the eyeballs within the conductive environment of the skull. Electrooculogram signals are generated from neurological conditions actions and EOG is generally used to capture these signals. EOG is an easy and low-cost method of recording eye movements. This paper proposed a fatigue detection mechanism which is based on EOG recorder with (Ag- AgC1) electrodes. Five subjects (2M, 3F) were participated in this study. Six features were presented to evaluate the suggested algorithm. The presented approach has managed to provide 89.2% to 96.5% effective blink detection.

关键词： Electrodes Electrooculography Road accidents signal processing algorithms Streaming media Fatigue Retina

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Lean Neural Networks for Real-time Embedded Spectral Notching Waveform Design 31

Lean Neural Networks for Real-time Embedded Spectral Notchin...

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IEEE 31st International Symposium on Industrial Electronics (ISIE)

作者： Baietto, Anthony Boubin, Jayson Farr, Patrick Bihl, Trevor J. Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Appl Res Solut Beavercreek OH USA US Air Force Res Lab Wright Patterson AFB OH USA

ISBN: (纸本)9781665482400

Spectral notching, a waveform design method used in signal processing and radar, mitigates interference caused by an ever-growing number of technologies which saturate the radio frequency (RF) spectrum. Online waveform design is possible with current technology, but extant techniques can not meet real-time latency requirements on the low size weight and power (SWaP) embedded hardware ideal for contexts where online waveform design is most useful. Current techniques rely on convex optimization, leading to non-trivial execution times and excessive compute and power requirements. Artificial Neural Networks (ANNs) could feasibly be used for spectral notching due to their ability to approximate complex non-linear functions;however, ANNs generally require considerable processing power and result in long inference times. Prior work has shown that ANNs can accurately perform real-time radar waveform design, albeit in very specific contexts. In this paper, we extend that work, demonstrating that lean neural networks, which model successful convex optimization algorithms in a lower dimensional representation, can meet real-time latency constraints for general waveform design without sacrificing accuracy on low SWaP embedded hardware. Our results are exemplified in simulation on real embedded and general purpose hardware. Overall, our lean neural network solution decreases inference time on traditional embedded hardware by 8.5x when compared to the fastest optimization approach, and uses 10.6x less energy. Our system executes quickly on low-power embedded devices, using only 16.6% of the energy a conventional GPU-provisioned system would require.

关键词： signal processing algorithms Radar Interference Artificial neural networks Hardware Real-time systems Convex functions

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A Density Clustering-Based CFAR Algorithm for Ship Detection in SAR Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Li, Yang Wang, Zeyu Chen, Hongmeng Li, Yachao Beijing Univ Posts & Telecommun Sch Informat & Commun Engn State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Inst Radio Measurement Beijing 100854 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

The clutter selection strategy based on sliding window in the conventional constant false alarm rate (CFAR) algorithm leads to different clutter qualities between pixels of the same target in a complex environment. To solve the problem, this letter proposes an improved CFAR algorithm based on density clustering. First, a two-parameter CFAR is used to detect ship targets. Then, density clustering is performed on each detected target pixel based on spatial distance and detection threshold to improve the target detection accuracy. Finally, false alarms caused by speckle noise are eliminated by using the number of times a pixel is clustered. The experimental results show that compared with the conventional CFAR algorithm and the superpixel-level CFAR detectors for ship detection in synthetic aperture radar (SAR) imagery (SP-CFAR), the proposed algorithm achieves a detection accuracy improvement of over 14.8% in heterogeneous clutter scenarios and dense target scenarios, while maintaining a low false alarm rate no higher than 0.13% in strong noise environments.

关键词： Clustering algorithms Clutter Marine vehicles Radar polarimetry signal processing algorithms Detectors Noise Constant false alarm rate (CFAR) density clustering ship detection synthetic aperture radar (SAR)

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A Robust Super-Resolution Gridless Imaging Framework for UAV-Borne SAR Tomography

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Gao, Silin Wang, Wenlong Wang, Muhan Zhang, Zhe Yang, Zai Qiu, Xiaolan Zhang, Bingchen Wu, Yirong CAS Key Lab Technol Geospatial Informat Proc & Ap Beijing 100190 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 101408 Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Natl Key Lab Microwave Imaging Beijing 100190 Peoples R China Suzhou Key Lab Microwave Imaging Proc & Applicat Suzhou 215123 Jiangsu Peoples R China Suzhou Aerosp Informat Res Inst Suzhou 215123 Jiangsu Peoples R China

Synthetic aperture radar tomography (TomoSAR) retrieves 3-D information from multiple synthetic aperture radar (SAR) images, effectively addresses the layover problem, and has become pivotal in urban mapping. Unmanned aerial vehicle (UAV) has gained popularity as a TomoSAR platform, offering distinct advantages such as the ability to achieve 3-D imaging in a single flight, cost-effectiveness, rapid deployment, and flexible trajectory planning. The evolution of compressed sensing (CS) has led to the widespread adoption of sparse reconstruction techniques in TomoSAR signal processing, with a focus on l(1) norm regularization and other grid-based CS (GBCS) methods. However, the discretization of illuminated scene along elevation introduces modeling errors, resulting in reduced reconstruction accuracy, known as the "off-grid" effect. Recent advancements have introduced gridless CS algorithms to mitigate this issue. This article presents an innovative gridless 3-D imaging framework tailored for UAV-borne TomoSAR. Capitalizing on the pulse repetition frequency (PRF) redundancy inherent in slow UAV platforms, a multiple measurement vector (MMV) model is constructed to enhance noise immunity without compromising azimuth-range resolution. Given the sparsely placed array elements due to mounting platform constraints, an atomic norm soft thresholding (AST) algorithm is proposed for partially observed MMV, offering gridless reconstruction capability and super-resolution. An efficient alternative optimization algorithm is also employed to enhance computational efficiency. The validation of the proposed framework is achieved through computer simulations and flight experiments, affirming its efficacy in UAV-borne TomoSAR applications.

关键词： Imaging Tomography Atomic measurements Autonomous aerial vehicles Image reconstruction Vectors signal processing algorithms Atomic norm gridless compressed sensing (CS) multiple measurement vectors (MMVs) synthetic aperture radar tomography (TomoSAR) unmanned aerial vehicle (UAV)

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Deep Deterministic Policy Gradient Artificial Intelligence for Radar Applications

Deep Deterministic Policy Gradient Artificial Intelligence f...

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IEEE Radar Conference (RadarConf)

作者： Reininger, Taylor J. Smith, Graeme E. Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA

ISBN: (纸本)9781728153681

The work presented in this paper is intended to build a deeper understanding of reinforcement learning (RL), a branch of artificial intelligence (AI), as it pertains to radar applications. We demonstrate a method for using a deep deterministic policy gradient (DDPG) AI algorithm to solve an example problem of producing a spectral notch in a radar waveform to avoid interfering with other electronic systems operating in the same portion of the spectrum. The DDPG algorithm is able to produce phase-coded waveforms with 80 dB deep notches in their power spectra. As such, it can provide an alternative method to gradient-based optimization and hand-engineered solutions. The radar performance of the resulting notched waveform was not addressed in this effort. Instead, the intent of the paper is to demonstrate that AI can be used in a fundamental radar signal processing task.

关键词： Conferences signal processing algorithms Reinforcement learning Radar signal processing Radar applications Artificial intelligence Task analysis

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FBC-SBL: Frequency Band Clustering Sparse Bayesian Learning for Off-Grid Wideband DOA Estimation With Different Frequency Bands

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Tang, Tao Yang, Chengzhu Xie, Tianrang Liu, Yining Xu, Lijun Chen, Desheng Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

Direction-of-arrival (DOA) estimation methods for wideband signals based on sparse Bayesian learning (SBL) have shown impressive performance. It generally assumes that wideband signals share the same frequency band, whereas deteriorating the performance of DOA estimation when target signals are located at different bands. To deal with this problem, we propose an off-grid DOA estimation framework, namely, frequency band clustering (FBC)-SBL, based on SBL incorporating FBC. FBC can exploit the cluster structure of signal distribution in frequency domain by applying a specific clustering algorithm. Compared with other existing algorithms, FBC-SBL can infer the frequency band occupation, thereby estimating DOAs more accurately. Specifically, the whole frequency band is first divided into finer subbands, and the variance vector of each subband is estimated. Then, FBC gathers the subbands with similar signal distributions. Finally, the estimated DOAs can be obtained by analyzing each cluster through SBL framework. Numerical simulations and real data experiments show that our method has superior performance compared with other algorithms and achieves an optimal average absolute error of 1.64 degrees in real data experiments.

关键词： Direction-of-arrival estimation Wideband Estimation Frequency estimation Vectors Bayes methods signal processing algorithms Frequency band clustering (FBC) sparse Bayesian learning (SBL) wideband direction-of-arrival (DOA) estimation

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Unsupervised Blind Joint Dereverberation and Room Acoustics Estimation With Diffusion Models

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 2244-2258页

作者： Jean-Marie Lemercier Eloi Moliner Simon Welker Vesa Välimäki Timo Gerkmann Signal Processing group at Universität Hamburg Hamburg Germany Acoustics Lab Department of Information Communications Engineering Aalto University Espoo Finland

This paper presents an unsupervised method for single-channel blind dereverberation and room impulse response (RIR) estimation, called BUDDy. The algorithm is rooted in Bayesian posterior sampling: it combines a likelihood model enforcing fidelity to the reverberant measurement, and an anechoic speech prior implemented by an unconditional diffusion model. We design a parametric filter representing the RIR, with exponential decay for each frequency subband. Room acoustics estimation and speech dereverberation are jointly carried out, as the filter parameters are iteratively estimated and the speech utterance refined along the reverse diffusion trajectory. In a blind scenario where the RIR is unknown, BUDDy successfully performs speech dereverberation in various acoustic scenarios, significantly outperforming other blind unsupervised baselines. Unlike supervised methods, which often struggle to generalize, BUDDy seamlessly adapts to different acoustic conditions. This paper extends our previous work by offering new experimental results and insights into the algorithm’s versatility. We demonstrate the robustness of our proposed method to new acoustic and speaker conditions, as well as its adaptability to high-resolution singing voice dereverberation, using both instrumental metrics and subjective listening evaluation. We study BUDDy’s performance for RIR estimation and observe it surpasses a state-of-the-art supervised DNN-based estimator on mismatched acoustic conditions. Finally, we investigate the sensitivity of informed dereverberation methods to RIR estimation errors, thereby motivating the joint acoustic estimation and dereverberation design.

关键词： Acoustics Diffusion models signal processing algorithms Reverberation Noise reduction Kernel Training Robustness Inverse problems Indexes

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FPGA Implementation of ANN Using Customized Instruction Set for 1-D signal processing

FPGA Implementation of ANN Using Customized Instruction Set ...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Anudita Bagal Varsha Harpale Electronics and Telecommunication Engineering Pimpri-Chinchwad College of Engineering Pune Pune India

ISBN: (纸本)9781665484527

This work describes the implementation of hardware architecture based on ANN and its hardware realization using hardware description language (Verilog) on FPGA. The objective of this project is “FPGA implementation of ANN using customized Instruction set for 1-D signal processing”. Our aim in this project is on designing small in size and functional processor architecture. The proposed project presents architectural design and execution of artificial neural network processor with a customized instruction set processor for 1 D signal processing. The ANN processor architecture is flexible, supporting random counts of layers and a number of 784 artificial neurons (ANs), The proposed architecture will support the feed-forward algorithm. The proposed architecture is flexible with the counts of input data; output data and signals will represent the fixed-point format and will compare the accuracies of the Tanh function and sigmoid function.

关键词： Instruction sets Computational modeling Time series analysis signal processing algorithms Artificial neural networks Computer architecture signal processing

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MM-FGRM: Fine-Grained Respiratory Monitoring Using MIMO Millimeter Wave Radar

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Wang, Shuxuan Han, Chong Guo, Jian Sun, Lijuan Nanjing Univ Posts & Telecommun Coll Comp Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu High Technol Res Key Lab Wireless Sensor Nanjing 210023 Peoples R China

Long-term respiratory monitoring plays an extremely important role in the diagnosis of respiratory system-related diseases. It can detect diseases temporally and improve the effectiveness of treatment, which is crucial for home health monitoring. With the rapid development of radar chip technology, noncontact respiratory monitoring based on commercial millimeter-wave radar sensors has received increasing attention. However, traditional signal processing methods are difficult to extract fine respiratory waveforms from radar signals mixed with human body movements. Fortunately, deep learning provides a solution, as we can use its powerful learning ability to learn a mapping from radar signals to real respiratory waveforms. With this method, we can directly observe fine-grained respiratory waveforms and further improve the accuracy of respiratory rate detection. Therefore, we propose MM-FGRM, a respiratory monitoring system based on a commercial 77 GHz multiple input multiple output (MIMO) radar. The core of this system is a deep learning-based network called IQ-Transformer with a self-attention mechanism that aims at capturing the latent respiratory-related features from I/Q components of radar signals directly in each human body region of interest (ROI) and recovering the respiratory waveforms. We collected 12 h of data from eight subjects and conducted experiments. The experimental results show that MM-FGRM can accurately recover respiratory waveforms and provide accurate respiratory rates. In addition, we perform testing on data from two other users who do not participate in the training, and the results verify that MM-FGRM has a strong generalization ability. Our results demonstrate the feasibility of further development of home respiratory monitoring products.

关键词： Radar Monitoring signal processing algorithms Millimeter wave radar Radar signal processing Ultra wideband radar Interference Deep learning mimo radar noncontact respiratory monitoring self-attention mechanism

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