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IEEE Radar Conference (RadarConf)

作者： Rong, Yu Mishra, Kumar Vijay Bliss, Daniel W. Arizona State Univ Sch Elect Comp & Energy Engn Ctr Wireless Informat Syst & Computat Architectur Tempe AZ 85281 USA US Army Res Lab Adelphi MD 20783 USA

ISBN: (纸本)9781728176093

In this paper, we present a novel non-contact and privacy preserving approach on recovering physiological measurements like heart rate when multiple subjects are slowly walking in the scene using a ultra-wideband (UWB) multi-frequency radar system. We employ joint probabilistic data association (JPDA) algorithm to separate detection from multiple moving objects. Additionally, we exploit a recently developed multichannel variational mode decomposition (MVMD) algorithm to extract heartbeat information from the range aligned samples. Our contribution lies in demonstration the possibility of a novel signal processing approach on heartbeat detection while multiple human targets are moving on the order of a few meters with a relatively simple setup while prior works requires a more complex setting and only considers small body movement on centimeter levels.

关键词： Bio-radars data association remote sensing tracking vital sign detection

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基于WGAN-GP-CNN的海面小目标检测

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信号处理 2024年第6期40卷 1082-1097页

作者：时艳玲陶平许述文南京邮电大学通信与信息工程学院江苏南京210003 西安电子科技大学雷达信号处理全国重点实验室陕西西安710071

针对传统基于统计理论的海面小目标检测方法在复杂海面环境中性能不高的问题,该文提出了一种改进的检测方法。首先通过分析海杂波和目标回波的特征,将检测问题转化为特征空间的分类任务。鉴于海面小目标样本数量有限,存在样本不平衡的问... 详细信息

针对传统基于统计理论的海面小目标检测方法在复杂海面环境中性能不高的问题,该文提出了一种改进的检测方法。首先通过分析海杂波和目标回波的特征,将检测问题转化为特征空间的分类任务。鉴于海面小目标样本数量有限,存在样本不平衡的问题,该文引入了一种基于梯度惩罚的沃瑟斯坦生成对抗网络(Wasserstein Generative Adversarial Network with Gradient Penalty,WGAN-GP)来增强目标数据,从而在数量上平衡目标样本与海杂波样本。同时,对原始WGAN-GP网络的损失函数进行了改进,引入相位损失以确保生成数据能够反映真实数据的相位信息。基于这些数据,进一步提取了生成目标和海杂波的高维特征,并将其送入卷积神经网络(Convolutional Neural Network,CNN)进行训练。为了应对高维特征空间中虚警概率难以控制的问题,对CNN算法进行了改进,通过设置Softmax分类器的阈值,实现了虚警概率可控。最后,借助公开的IPIX雷达数据集进行实验验证,所提的WGAN-GP-CNN检测器在积累时间为1.024 s,虚警概率为0.001时,平均检测概率达到0.8683,具有良好的检测效果。

关键词：海杂波小目标检测虚警可控生成对抗网络卷积神经网络

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A Multi-Target SNR-Progressive Learning Approach to Regression Based Speech Enhancement

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2020年 28卷 1608-1619页

作者： Tu, Yan-Hui Du, Jun Gao, Tian Lee, Chin-Hui Univ Sci & Technol China Hefei 230027 Peoples R China Georgia Inst Technol Atlanta GA 30332 USA

We propose a multi-target, signal-to-noise-ratio (SNR)-progressive learning (SNR-PL) framework for regression based speech enhancement (SE). At low SNR levels, it is often not easy to directly learn the complicated regression required in SE. We therefore decompose the original SE problem of mapping noisy to clean speech features, with a large SNR gap, into a series of sub-problems, each with a small SNR increment and presumably easier to learn. In our configurations, each hidden layer of the proposed regression neural network is guided to explicitly learn an intermediate target with a specified but small SNR gain. Tested on both deep neural network (DNN) and long short-term memory (LSTM) architectures, SNR-PL consistently outperforms the conventional & x201C;black box& x201D;DNN framework in terms of both objective measure superiority and network model compactness. Furthermore, with the best configured LSTM-based SNR-PL model, we often observe that the performance is easily saturated or even degraded when increasing the number of intermediate targets, due to the fact that useful information is lost in dimension reduction when involving more target layers. Accordingly, to address this information loss issue, we explore densely connected networks on top of the LSTM structure where the input and the preceding intermediate targets are concatenated together to learn the next target. Finally, to fully utilize the rich and complementary information of intermediate targets, a simple post-processing strategy is adopted to further improve the performance. Evaluated on the simulation speech data, experimental results in unseen noises cases demonstrate that the proposed approach consistently performs better than the conventional LSTM approach in terms of objective speech enhancement measures for speech intelligibility and quality. Furthermore, when evaluated on real data provided by the CHiME-4 Challenge for automatic speech recognition (ASR) of noisy microphone array speech, w

关键词： Speech enhancement signal to noise ratio Noise measurement Neural networks Task analysis Training SNR-progressive learning speech enhancement neural network dense structure post-processing

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STAP-informed Neural Network for Radar Moving Target Indicator

STAP-informed Neural Network for Radar Moving Target Indicat...

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IEEE National Conference on Radar

作者： Dalton Vega Michael Newey David Barrett Alan Axelrod Anu Myne Allan Wollaber MIT Lincoln Laboratory Lexington MA USA

ISBN: (数字)9798350329209

ISBN: (纸本)9798350329216

As machine learning (ML) approaches continue to advance the state-of-the-art for radar clutter removal, their network architectures leave valuable domain understanding unex-ploited. Further, there remains a dearth of sufficiently large radar datasets for practitioners to train models, compare algorithms, and report progress. We present a neural network architecture that leverages knowledge of radar image formation via locally stenciled IQ input data for rapid clutter removal in Ground Moving Target Indicator problems. The neural network replaces Space Time Adaptive processing (STAP) and produces a clutter removal weight matrix with identical function. We also present a training and validation radar dataset of sufficient complexity to stress-test STAP-based techniques with novel ML approaches. The dataset consists of multichannel, complex IQ data under three clutter conditions, each having over 2000 range-Doppler chips for training and evaluation. It includes variable numbers of injected moving point targets with a range of scattering cross sections obfuscated by a heterogeneous clutter ridge. Variants of the dataset include a representation of windblown trees and a low signal-to-noise ratio (ground-clutter-free) dataset to directly measure and contrast noise and clutter removal performance. In general, our physics-integrated, convolutional neural network architecture outperforms STAP on all simulated datasets by a significant margin.

关键词： Training Radar clutter Neural networks Training data Radar Object detection Radar imaging

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Few-shot SAR target classification with CLPN: a prototypical network combining unsupervised contrastive learning

Few-shot SAR target classification with CLPN: a prototypical...

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IET International Radar Conference (IRC 2023)

作者： Bowen Chen Minjia Yang Xueru Bai National Key Laboratory of Radar Signal Processing Xidian University Xi’an People's Republic of China

ISBN: (数字)9781837240982

In the field of Synthetic Aperture Radar (SAR), owing to the limitations of observation and imaging conditions, only a limited number of annotated samples are available for model training. Existing deep learning-based methods struggle to effectively extract discernible features, leading to degraded performance or even complete failure. To address these issues, this paper combines prototypical networks from meta-learning with unsupervised contrastive learning. By utilizing meta-learning, the proposed method ensures the model's classification performance under small sample conditions. Additionally, effective SAR data augmentation techniques are designed to incorporate unsupervised contrastive learning, enabling the model to better capture the similarities and differences between classes and enhance its generalization ability. Our proposed approach leverages both the supervised information from class labels and the unsupervised information inherent in the images, allowing the model to deeply explore the high-order information within the data and achieve accurate classification of few shot SAR targets. The recognition results on the MSTAR dataset, consisting of three target classes, demonstrate that the proposed method (CLPN) exhibits higher average recognition accuracy under the small sample condition and robustness to different depression angles.

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XAI-CNVMarker: Explainable AI-based copy number variant biomarker discovery for breast cancer subtypes

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BIOMEDICAL signal processing AND CONTROL 2023年第1期84卷

作者： Rajpal, Sheetal Rajpal, Ankit Agarwal, Manoj Kumar, Virendra Abraham, Ajith Khanna, Divya Kumar, Naveen Univ Delhi Dept Comp Sci Delhi India Univ Delhi Hans Raj Coll Dept Comp Sci Delhi India All India Inst Med Sci Dept Nucl Magnet Resonance Imaging New Delhi India FLAME Univ Fac Comp & Data Sci Pune Maharashtra India Delhi IGIB Inst Genom & Integrat Biol Funct Genom Lab New Delhi India

Breast cancer is a leading cause of cancer-related deaths among women. The multi-omic data has revolutionized the methodology to unravel molecular heterogeneity in breast cancer. As genetic variations captured from Copy Number Variation (CNV) data are considered the most stable amongst the multi-omic data, it leads to robust biomarkers. Thus, this paper targets the discovery of a set of CNV biomarkers for dissecting this heterogeneity. The existing algorithms yield biomarkers, too huge to be interpreted clinically. So, in this paper, we have proposed XAI-CNVMarker-an explainable AI-based post-hoc biomarker discovery framework to discover a small set of interpretable biomarkers. We exploit the power of deep learning to build DLmodel-a deep learning model for breast cancer classification. Subsequently, the trained model is analyzed using different explainable AI methods to arrive at a set of 44 CNV biomarkers. Using 5-fold cross-validation, we obtained a classification accuracy of 0.712 (+/- 0.048) at a 95% confidence interval. Gene set analysis revealed 37 subtype-specific enriched Reactome and Kegg pathways, 21 druggable genes, and 13 biomarkers linked with the prognostic outcome. Finally, we validated the efficacy of the identified biomarkers on METABRIC. Thus, the proposed framework demonstrates the role of explainable AI in discovering clinically reliable biomarkers.

关键词： Explainable AI CNV Biomarker Deep learning Breast cancer subtypes Gradient

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Dop-NET: a micro-Doppler radar data challenge

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ELECTRONICS LETTERS 2020年第11期56卷 568-569页

作者： Ritchie, M. Capraru, R. Fioranelli, F. UCL Dept Elect & Elect Engn Gower St London WC1E 6BT England Delft Univ Technol Delft Univ Technol Dept Microelect Mekelweg 5 NL-2628 CD Delft Netherlands

Radar sensors have a new growing application area of dynamic hand gesture recognition. Traditionally radar systems are considered to be very large, complex and focused on detecting targets at long ranges. With modern electronics and signal processing it is now possible to create small compact RF sensors that can sense subtle movements over short ranges. For such applications, access to comprehensive databases of signatures is critical to enable the effective training of classification algorithms and to provide a common baseline for benchmarking purposes. This Letter introduces the Dop-NET radar micro-Doppler database and data challenge to the radar and machine learning communities. Dop-NET is a database of radar micro-Doppler signatures that are shareable and distributed with the purpose of improving micro-Doppler classification techniques. A continuous wave 24 GHz radar module is used to capture the first contributions to the Dop-NET database and classification results based on discriminating these hand gestures as shown.

关键词： learning (artificial intelligence) radar signal processing CW radar Doppler radar signal classification signal processing compact RF sensors Dop-NET radar microDoppler database machine learning communities radar microDoppler signatures microDoppler classification techniques Dop-NET database microDoppler radar data challenge radar sensors dynamic hand gesture recognition continuous wave radar module frequency 24 0 GHz

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Long-Time Adaptive Coherent Detection of small targets in Sea Clutter by Fast Inversion Algorithm of Block Tridiagonal Speckle Covariance Matrices

SSRN

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SSRN 2023年

作者： Zhang, Xiao-Jun Shui, Peng-Lang Xue, Yu-Fan National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China

Low-velocity small target detection in maritime surveillance radars is always a challenging task. Low signal-to-clutter ratio requires long-time coherent integration to obtain enough gain of target returns. However, the long-time coherent integration conflicts with temporal nonstationarity and spatial inhomogeneity of sea clutter. In this paper, considering the decorrelation time of speckle component of sea clutter short up to a dozen of milliseconds, the spherical invariant random vector (SIRV) model with block tridiagonal speckle covariance matrix and the inverse Gamma distributed texture is proposed to model sea clutter sequences in several tenths of a second. In this model, a long-time adaptive generalized likelihood ratio test with linear threshold detector (GLRT-LTD) is constructed. Owing to the block tridiagonal structure of speckle covariance matrices, the adaptive detection requires much less reference cells for speckle covariance matrix estimation and much lower computational cost for its inversion. The proposed detector is verified by an X-band high-resolution island-based radar data with an unmanned aerial vehicle (UAV) as test target. The experimental result shows that it obtains competitive detection performance in comparison with the state-of-the-art detectors. © 2023, The Authors. All rights reserved.

关键词： Covariance matrix

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自编码器在浅海小目标前向散射信号检测中的应用

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振动与冲击 2022年第11期41卷 171-179,188页

作者：刘帅京许枫杨娟中国科学院声学研究所海洋声学技术中心北京100190 中国科学院大学北京100049

针对近距离浅海小目标前向散射信号检测受信道自身起伏影响较大的问题,提出一种基于自编码器的目标检测方法。该方法通过匹配滤波、希尔伯特变换和相位对齐等处理过程获取接收声场的稳态到达结构,利用自编码器无监督地学习无目标时背景... 详细信息

针对近距离浅海小目标前向散射信号检测受信道自身起伏影响较大的问题,提出一种基于自编码器的目标检测方法。该方法通过匹配滤波、希尔伯特变换和相位对齐等处理过程获取接收声场的稳态到达结构,利用自编码器无监督地学习无目标时背景声场的稳态结构,然后通过观测自编码器的重构误差能量变化实现目标探测。应用海上试验的蛙人穿越数据进行分析,说明了当收发距离为50 m,目标穿越位置与发射源距离为15 m时,单通道接收声场的探测扰动量大约可达到8 dB以上,联合多通道信号进行训练可进一步抑制背景声场的扰动。试验结果表明该算法在动态海洋环境中能够实现前向散射信号的检测,并可以确定目标的穿越时间。

关键词：自编码器前向散射信号检测浅海声场异常侦测

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FPGA-Accelerated Digital signal processing for UAV Traffic Control Radar

FPGA-Accelerated Digital Signal Processing for UAV Traffic C...

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作者： Moody, Kacen Paul Brigham Young University

学位级别：M.Sc.

As an extension of previous work done by Luke Newmeyer in his master's thesis \cite{newmeyer2018efficient}, this report presents an improved signal processing chain for efficient, real-time processing of radar data for small-scale UAV traffic control systems. The HDL design described is for a 16-channel, 2-dimensional phased array feed processing chain and includes mean subtraction, windowing, FIR filtering, decimation, spectral estimation via FFT, cross-correlation, and averaging, as well as a significant amount of control and configuration logic. The design runs near the the max allowable memory bus frequency at 300MHz, and using AXI DMA engines can achieve throughput of 38.3 Gb/s (~0.25% below theoretical 38.4 Gb/s), transferring 2MB of correlation data in about 440us. This allows for a pulse repetition frequency of nearly 2kHz, in contrast to 454Hz from the previous design. The design targets the Avnet Ultra: Zed-EV MPSoC board, which boots custom PetaLinux images. API code and post-processing algorithms run in this environment to interface with the FPGA control registers and further process frames of data. Primary configuration options include variable sample rate, window coefficients, FIR filter coefficients, chirp length, pulse repetition interval, decimation factor, number of averaged frames, error monitoring, three DMA sampling points, and DMA ring buffer transfers. The result is a dynamic, high-speed, small-scale design which can process 16 parallel channels of data in real time for 3-dimensional detection of local UAV traffic at a range of 1000m.

关键词： FPGA-Accelerated Digital signal processing for UAV Traffic Control Radar

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