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signal, Information and data processing (ICSIDP), IEEE International Conference on

作者： Xinda Cao Jiangang Liu Yulin Wu Fengzhi Shao Yubo Wang Guolong Cui Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of China Quzhou China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In recent years, with the introduction and development of vehicle-to-everything (V2X) and child presence detection (CPD), there’s an increasing demand for in-vehicle perception systems. Millimeter-wave (mmWave) radar has become one of the mainstream sensors in this field for high accuracy, small size, and low power consumption. In this paper, we develop a real-time in-vehicle occupancy detection method based on 77GHz mmWave radar. Firstly, a pre-processing framework based on dual-path detection is used to obtain more point clouds from weak targets. Then density-based spatial clustering of applications with noise (DBSCAN) is applied to classify the occupants’ point clouds. Finally, we propose a novel probabilistic population-assisted occupancy detection algorithm based on the long and short-term feedback results, which can suppress missed detection and false alarms well. Our algorithm has been successfully deployed onto the radar board and achieves an average accuracy rate of 99.10% under various complex scene experiments.

关键词： Point cloud compression Accuracy Radar detection signal processing algorithms Probabilistic logic Real-time systems Radar signal processing Sensors Millimeter wave communication Vehicle-to-everything

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Multi-fold high-order cumulants based CFAR detector for radar weak target detection

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DIGITAL signal processing 2023年第1期139卷

作者： Liu, Xiangli Bai, Xinlong Wang, Danyang Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

As an important detection means, radar is widely used in target monitoring field. However, due to the small radar cross section (RCS) of stealthy or small target and the complex detection environment, the target echo signal is usually submerged in the environmental clutter. Then the target detection based on Pulse-Doppler radar shows the characteristics of weak signal detection. In this paper, the multi-fold high-order cumulant (HOC) on the radar range-Doppler (R-D) map is proposed to elevate the detection performance of the weak target signal, which can effectively eliminate the noise. Then the processed signal is sent to the CFAR detector to estimate the targets' range and velocity. Although HOC has been used in radar signal detection, the detection algorithm was mainly done on the processed signal after one-fold HOC. We propose a construction method of multi-fold HOC using a set of delay vectors to further eliminate residual noise after one-fold HOC processing. Our proposed multi-fold HOC can be seen as a data preprocessing method, which can also be applied to other detection algorithm to further improve its detection performance. Simulation results show that the proposed multi-fold HOC based CFAR algorithm can greatly reduce SNR required for signal detection, and has an advantage in radar weak signal detection.(c) 2023 Elsevier Inc. All rights reserved.

关键词： Constant false alarm rate (CFAR) Multi-fold high-order cumulant (HOC) Weak signal detection Pulse-Doppler radar

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地杂波背景下雷达低慢小无人机探测数据集(LSS-Ku-1.0)

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信号处理 2025年第5期41卷 807-820页

作者：程永强邹润明陈嘉宜吴昊华小强国防科技大学电子科学学院湖南长沙410073

复杂环境下雷达对无人机等低慢小目标探测与识别面临诸多挑战,相关研究成为雷达探测领域的热点和难点问题。数据集是雷达目标探测与识别研究的基础,其数据质量和多样性对算法的性能验证起到了重要作用。目前公开发布的数据集雷达多布设... 详细信息

复杂环境下雷达对无人机等低慢小目标探测与识别面临诸多挑战,相关研究成为雷达探测领域的热点和难点问题。数据集是雷达目标探测与识别研究的基础,其数据质量和多样性对算法的性能验证起到了重要作用。目前公开发布的数据集雷达多布设于地面,对空探测背景相对较为理想,在杂波环境下雷达对无人机目标探测数据集较少,探测场景、观测视角、目标飞行高度、信号带宽等参数较为单一,数据多样性有待提高。针对上述问题,本文构建了一套地杂波背景下雷达低慢小无人机探测数据集(LSS-Ku-1.0)。采用置于高塔上的Ku波段相控阵雷达,在野外丛林和草地环境下,录取了强杂波背景无人机目标雷达回波数据。该数据集包含不同信号波形、带宽、擦地角以及三种不同飞行高度的旋翼无人机目标回波。基于该数据集,分别对杂波的统计分布特性和时间相关性进行了分析,采用5种统计模型对杂波统计分布进行了拟合,给出了拟合优度检验结果。同时,对无人机旋翼的微多普勒特性进行了分析,并研究了典型数据的一维距离像、频谱图、时频图、距离-多普勒谱,为雷达低慢小目标特性分析和检测与识别研究提供了数据支撑。

关键词：雷达地杂波低慢小目标无人机数据集

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A Multi-Domain Joint Novel Method for ISAR Imaging of Multi-Ship targets

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REMOTE SENSING 2023年第19期15卷 4878-4878页

作者： Zhang, Yangyang Xu, Ning Li, Ning Guo, Zhengwei Henan Univ Sch Comp & Informat Engn Kaifeng 475004 Peoples R China Henan Prov Engn Res Ctr Spatial Informat Proc Kaifeng 475004 Peoples R China Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China

As a key object on the ocean, regulating civilian and military ship targets more effectively is a very important part of maintaining maritime security. One of the ways to obtain high-resolution images of ship targets is the inverse synthetic aperture radar (ISAR) imaging technique. However, in the actual ISAR imaging process, ship targets in a formation often lead to complicated motion conditions. Due to the close distance between the ship targets, the rough imaging results of the targets cannot be completely separated in the image domain, and the small differences in motion parameters lead to overlapping phenomena in the Doppler history. Therefore, for situations in which ship formation targets with little difference in motion parameters are included in the same radar beam, this paper proposes a multi-domain joint ISAR separation imaging method for multi-ship targets. First, the method performs echo separation using the Hough transform (HT) with the minimum entropy autofocus method in the image domain. Secondly, the time-frequency curve is extracted in the time-frequency domain using the short-time Fourier transform (STFT) for time-frequency analysis, which solves the problem of the ship formation targets being aliased on both echo and Doppler history after range compression and achieves the purpose of separating the echo signals of the sub-ship targets with high accuracy. Eventually, better-focused images of each target are obtained via further motion compensation and precise imaging. Finally, the effectiveness of the proposed method is verified using a simulation and measured data.

关键词： inverse synthetic aperture radar (ISAR) multi-targets signal separation Hough transform (HT) short-time Fourier transform (STFT)

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small Target Detection on Sea Surface based on Feature-Weighted Support Vector Machine

Small Target Detection on Sea Surface based on Feature-Weigh...

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IEEE International Conference on signal and Image processing (ICSIP)

作者： Yaqing Chen Shijian Shen Zhiyong Lei Nanjing Research Institute of Electronics Technology Nanjing China

Because the sea clutter is greatly affected by the complex and changeable environment, it is difficult to describe its characteristics through a single amplitude statistical model. In addition, the great variety and the small radar cross section of the target on the sea surface increase the detection difficulty. This paper proposes a feature-weighted SVM algorithm, which regards the detection of small targets on the sea surface as a binary classification problem. The algorithm extracts six features from different domains of the sea clutter, calculates the weight of the features according to the contribution of the features and constructs the feature space for detection with weighted feature vectors. Experiments on IPIX data sets show that the detection method improves the detection accuracy compared with the traditional algorithm and it is helpful for sea surface monitoring.

关键词：

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Robust and Secure Resource Allocation for ISAC Systems: A Novel Optimization Framework for Variable-Length Snapshots

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IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第12期70卷 8196-8214页

作者： Xu, Dongfang Yu, Xianghao Ng, Derrick Wing Kwan Schmeink, Anke Schober, Robert Friedrich Alexander Univ Erlangen Nurnberg FAU Inst Digital Commun D-91058 Erlangen Germany City Univ Hong Kong CityU Dept Elect Engn Hong Kong Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Rhein Westfal TH Aachen Chair Informat Theory & Data Analyt D-52074 Aachen Germany

In this paper, we investigate the robust resource allocation design for secure communication in an integrated sensing and communication (ISAC) system. A multi-antenna dual-functional radar-communication (DFRC) base station (BS) serves multiple single-antenna legitimate users and senses for targets simultaneously, where already identified targets are treated as potential single-antenna eavesdroppers. The DFRC BS scans a sector with a sequence of dedicated beams, and the ISAC system takes a snapshot of the environment during the transmission of each beam. Based on the sensing information, the DFRC BS can acquire the channel state information (CSI) of the potential eavesdroppers. Different from existing works that focused on the resource allocation design for a single snapshot, in this paper, we propose a novel optimization framework that jointly optimizes the communication and sensing resources over a sequence of snapshots with adjustable durations. Besides, artificial noise (AN) is exploited by the BS for joint sensing and physical layer security provisioning. To this end, we jointly optimize the duration of each snapshot, the beamforming vector, and the covariance matrix of the AN for maximization of the system sum secrecy rate over a sequence of snapshots while guaranteeing a minimum required average achievable rate and a maximum information leakage constraint for each legitimate user. The resource allocation algorithm design is formulated as a non-convex optimization problem, where we account for the imperfect CSI of both the legitimate users and the potential eavesdroppers. To make the problem tractable, we derive a bound for the uncertainty region of the potential eavesdroppers' small-scale fading based on a safe approximation, which facilitates the development of a block coordinate descent-based iterative algorithm for obtaining an efficient suboptimal solution. Simulation results illustrate that the proposed scheme can significantly enhance the physical layer

关键词： Sensors Radar Array signal processing Wireless communication Uncertainty Optimization Resource management Integrated sensing and communication imperfect channel state information secure communication beamforming design

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small Target Detection Method in Sea Clutter Based on Interframe Multi-feature Iteration 6

Small Target Detection Method in Sea Clutter Based on Interf...

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6th International Conference on signal and Image processing, ICSIP 2021

作者： Xu, Shuwen Ma, Yitu Bai, Xiaohui Xidian University National Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9780738133737

For marine radar, since sea clutter is complex and dynamic, and small targets are difficult to model, the detection of floating small targets in sea clutter is always the focus and difficulty of radar target detection. The radar cross section of the floating small targets is small and the velocity is slow, so traditional detection methods have some bottlenecks, which belong to the problem of "over-clutter detection". The multi-feature-based detection method is an effective way to solve the detection of small targets on the sea surface, but traditional feature-based detection generally requires long accumulated time. Traditional scanning radar needs to take into account the surveillance space, which makes the number of accumulated pulses at each beam position restricted. It limits the application of feature detection methods to scanning radars. In this paper, aiming at the problem that the radar cannot stare at an angle for a long time in scanning mode, three features of relative average amplitude, relative Doppler peak height, and relative Doppler vector entropy are firstly extract. After that, the idea of multi-feature iteration between frames is used to update the feature values in the feature space, and then the fast convex hull algorithm is used to construct the decision region for detection. The measured data verifies that the proposed method can be well applied to scanning radar and obtains better detection performance than original methods. © 2021 IEEE.

关键词： Radar cross section

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3-D Point Cloud Generation From Airborne Single-Pass and Single-Channel Circular SAR data

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第10期59卷 8398-8417页

作者： Palm, Stephan Stilla, Uwe Fraunhofer FHR Dept High Frequency Radar & Applicat HRA D-53343 Wachtberg Germany Tech Univ Muenchen Dept Photogrammetry & Remote Sensing D-80333 Munich Germany

This article presents a framework to generate 3-D point clouds by very-high-resolution single-pass and single-channel circular synthetic aperture radar (CSAR). The focus is both on the precise 3-D determination of very small, detached objects and on larger buildings in complex urban scenes. Inspired by optical flow methods, our approach evaluates the tracked aspect dependent backscatter energy flow of objects above the focusing reference plane. Besides the derivation of the exact projection geometry in CSAR, we give answers to the question of how accurate 3-D information can be extracted by this approach as a function of aspect interval and the object's signal-to-clutter ratio (SCR). A further coherent adaption uses considerably larger subapertures while focusing the data on different reference heights. The computed 3-D information from multiple aspect views is then fused to a georeferenced 3-D point cloud. This results in the first demonstration of 3-D point cloud generation from CSAR data collected with a frequency-modulated continuous-wave (FMCW) radar at the W-band. The results were validated with light detection and ranging (LiDAR) data, and the height accuracy was evaluated in relation to the aspect integration interval and the number of aspect views. For small point targets with edge lengths of 3 cm, we could demonstrate that an aspect interval of only 8.5 degrees leads to a height accuracy below 20 cm. Building roofs reach a height accuracy of several tens of centimeters. Besides the spatial information, we can reveal the angular scattering behavior of individual objects and determine over which aspect interval they are even visible.

关键词： 3-D point clouds airborne circular synthetic aperture radar (CSAR) CSAR data fusion CSAR tomography millimeter-wave radar radar signal processing video synthetic aperture radar (SAR)

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small MOVING TARGET MOT TRACKING WITH GM-PHD FILTER AND ATTENTION-BASED CNN 31

SMALL MOVING TARGET MOT TRACKING WITH GM-PHD FILTER AND ATTE...

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IEEE 31st International Workshop on Machine Learning for signal processing (MLSP)

作者： Aguilar, Camilo Ortner, Mathias Zerubia, Josiane Univ Cote dAzur INRIA Sophia Antipolis France Airbus Def & Space Toulouse France

ISBN: (纸本)9781728163383

We present a multi-object tracking (MOT) approach to track small moving targets in satellite images. Our objects of interest span few pixels, do not present a defined texture, and are easily lost in cluttered environments. We propose a patch-based convolutional neural network (CNN) that focuses on specific regions to detect and discriminate nearby small objects. We use the object motion information to drive the patch selection and detect objects using a region-based CNN. In addition, we present a direct MOT data-association approach by using an improved Gaussian mixture-probability hypothesis density (GM-PHD) filter. The GM-PHD filter offers an efficient yet robust MOT formulation that takes into account clutter, misdetection, and target appearance and disappearance. We are able to detect and track blob-like moving objects and demonstrate an improvement over competing state-of-the-art tracking approaches.

关键词： Object Detection Satellite Object Tracking MOT CNN Deep Learning GM-PHD

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Lightweight helmet detection algorithm based on improved YOLOv5 2

Lightweight helmet detection algorithm based on improved YOL...

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2nd International Conference on Advanced Algorithms and signal Image processing, AASIP 2022

作者： Li, Jianning Zhang, Junzhao Zhang, Xinyu Wang, Shuai College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Software Institute Nanjing University Jiangsu Nanjing210093 China Shanxi Electric Drive and Internet of Things Engineering Research Center Shanxi Taiyuan030024 China Shanxi Province 1331 Project-Smart City Based on Big Data Lighting Data Sharing and Public Service Engineering Technology Research Center Shanxi Taiyuan030024 China Shanxi Intelligent Digital Lighting Union Laboratory Shanxi Taiyuan030024 China Shanxi Intelligent Digital Cultural Tourism Industry Research Institute Shanxi Taiyuan030024 China

ISBN: (纸本)9781510660571

In the construction process, wearing safety helmet can effectively reduce the injury caused by falling objects and hard objects. In order to detect the wearing of safety helmet during the construction process, a lightweight safety helmet wearing detection algorithm YOLOv5-CS is proposed and applied to the embedded equipment at the construction site. The algorithm is based on the improvement of YOLOv5 target detection algorithm. The improved CAShuffle module is used to form the backbone feature extraction network, which reduces the size of the original algorithm and ensures the detection accuracy of the algorithm;DIoU as the loss function, which makes the algorithm more conducive to detecting small targets such as hard hats;modify dataset, add the dataset on the basis of the original dataset and mark the safety helmet and the anchor frame of the person separately, and judge whether the worker wears the safety helmet by analyzing positional relationship of the identified anchor frame, use the algorithm combining K-means and genetic algorithm to predict the anchor frame of the dataset, and use Mosaic algorithm to enhance the data. The results show that the parameter quantity of YOLOv5-CS algorithm is 2.4M, less than one third of YOLOv5;The average precision decreased by only 4.2%;640x640 pictures can be recognized at 31 frames per second on Jetson Nano, a embedded platform. © 2022 SPIE.

关键词： signal detection

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