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IET International Radar Conference 2023, IRC 2023

作者： Li, Yu Chen, Jinming Cheng, ZiYang Wang, Weiwei Zhang, Jun Wu, Tao Duan, Chongdi Xi'an Institute of Space Radio Technology No.504 East Changan Street Changan district Xi'an China University of Electronic Science and Technology of China No. 2006 Xiyuan Street West Gaoxin district Chengdu China

ISBN: (纸本)9781839539954

Space-time adaptive processing (STAP) combines the returned signals from all elements of an antenna array and all coherent pulse trains during a CPI period to provide clutter suppression and target detection ability, which is a promising technique for acquiring small, slow, and moving targets. In this work, we proceed to the data-dependent channel selection method for Post-Doppler beam- space STAP to improve clutter suppression performance in sample starved environments, in which no additional a prior information is used in our framework. The main innovation of this manuscript lies in the characteristic that the selected channels in the fully-dimension weight vector, which is inversely transformed from the RD weight vector, is sparse relative to the whole beam-Doppler channels. Compared with several existing methods, the proposed method significantly enhances the STAP performance in sample starved environments. © The Institution of Engineering & Technology 2023.

关键词： Space time adaptive processing

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AugTarget data Augmentation for Infrared small Target Detection

AugTarget Data Augmentation for Infrared Small Target Detect...

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

作者： Shengjia Chen Jiewen Zhu Luping Ji Hongjun Pan Yuhao Xu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Sichuan University Chengdu China

Sample shortage has always been a frequently-faced problem for the machine-learning models in infrared small target detection. As one of main limitations, it is hampering the further promotion of target detection performance. In this paper, we propose a simple and effective data augmentation scheme, AugTarget, to address this shortage issue of small target samples. Our scheme mainly consists of two crucial algorithms: target augmentation and batch augmentation. The former is designed to generate sufficient targets, by random target representation. The latter is devised to diversify training samples. Moreover, the initially-generated image samples of small targets are further enriched by randomly aggregating the feature representation of different images. The experiments on public datasets demonstrate that our AugTarget could bring an obvious improvement to mean intersection over union (mIoU). Cooperated by the augmentation of our AugTarget, the state-of-the-art (SOTA) method, AGPC could even achieve a distinct performance promotion by 3.03%, 2.17% and 2.63% on MDFA, SIRST-Aug and Merged datasets, respectively. In addition, the experimental results on three baseline models also show the universality & adaptivity of AugTarget to different dataset augmentation. Our source codes are available at https://***/UESTC-nnLab/AugTarget.

关键词： Training Adaptation models Source coding signal processing algorithms Object detection Machine learning signal processing

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Dual-Level Contrast Enhancement Method for Infrared small Target Detection

Dual-Level Contrast Enhancement Method for Infrared Small Ta...

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

作者： Haotian Sun Wenzheng Wang Yuhan Xu Qiuyu Jin Zipeng Zhang Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In the field of target detection, infrared small target detection(ISTD) is a critical area of research, playing a vital role in reconnaissance, guidance, and early warning systems. Existing spatial Filtering algorithms perform well in clean backgrounds; however, as these filters require manual design based on prior knowledge, they are prone to interference from complex scenes (e.g., clouds, buildings), leading to target omission. small targets typically exhibit blurry features and weak energy, making direct detection susceptible to interference from high-intensity areas, which may result in missed detections. To tackle these challenges, this paper presents a two-tier contrast improvement method (DLCE). First, a gradient filter group is used to enhance the signal-to-noise ratio (SNR) of the target, and then, built upon the concept of local contrast measurement (LCM), a novel intensity calculation method is developed. The new contrast window design effectively enhances the target’s SNR. Compared with existing algorithms, like MPCM (Multi-Scale Patch-based Contrast Measurement) on publicly available datasets, the proposed method more effectively enhances the target’s radiative intensity while suppressing background noise. The effectiveness of the proposed algorithm is confirmed by the simulation results.

关键词： Filters Simulation Object detection Interference Reconnaissance Robustness Windows Noise measurement signal to noise ratio Visual perception

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Experimental evaluation of Supervised Reciprocal Filter Strategies for OFDM-radar signal processing

Experimental evaluation of Supervised Reciprocal Filter Stra...

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IEEE Radar Conference (RadarConf)

作者： Rodriguez, Javier Trujillo Colone, Fabiola Lombardo, Pierfrancesco Sapienza Univ Rome Dept Informat Engn Elect & Telecommun DIET Rome Italy

ISBN: (纸本)9781665436694

In this paper, we present an experimental evaluation of recently proposed Supervised Reciprocal Filter approaches for the compression of OFDM-radar signals. The range-Doppler map is usually evaluated using a suboptimal batches algorithm, after fragmenting the signal in batches with length equal to the OFDM symbol. Using "OFDM fragmentation" requires symbol synchronization and sets constraints on the non-ambiguous Range-Doppler area of targets that can be detected with limited signal-to-Noise Ratio (SNR) loss. Supervised Reciprocal Filters have been recently proposed to operate with batches of longer lengths than the OFDM symbol without requiring any synchronization. In this paper we extend the study to include the case of batches equal to a fraction of the OFDM symbol, which provides higher flexibility to adapt the processor to the range-Doppler scenario of interest. These filters have been shown to contain the large SNR losses obtained with a direct application of the Reciprocal Filter (RF) with the non-OFDM fragmentation. Moreover, they have been shown theoretically to preserve the benefits of the RF over the Matched Filter (MF) against the clutter-limited scenarios. To assess the performance of the Supervised Filter against a real scenario, an acquisition campaign has been carried out using the Sapienza experimental passive radar along the coast north of Rome, against a maritime traffic scenario, including non-cooperative vessels, as well as a cooperating small boat equipped with differential GPS positioning registration tools. The effectiveness of the proposed approaches is validated by applying them to experimental data from a PBR application exploiting DVB-T transmissions.

关键词： signal to noise ratio

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Priority Scheduling Using Recurrent Quadrant Search for Handling Priority and 'Pop-Up' targets in Aerial Videos

Priority Scheduling Using Recurrent Quadrant Search for Hand...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Chandrakanth, V. Singh, Shivpal Channappayya, Sumohana S Palaniappan, K. Defence Research and Development Laboratory Hyderabad India Indian Institute of Technology Hyderabad India University of Missouri Columbia United States

ISBN: (纸本)9798350359527

Target detection and tracking is a well established application in the radar (RF) domain. However, the same operating principles may not directly extend to image-based tracking systems. In the optical domain, the problem becomes challenging due to the lack of an active transmit signal (as available in radar processing), which actively maps the environment for target detection and track initiation. Passive imaging systems often require a separate assisted intervention in the optical processing channel for real-Time target detection and track initialization. This limitation has been successfully addressed with the development of automated tracking algorithms using deep learning architectures like Convolutional Neural Networks (CNNs). CNNs are extensively employed in generic object classification and localization applications for single and multi-Target scenarios. In multi-Target scenarios, the deep architecture framework detects all the objects in the frame without any priority designation (eg. YOLO, VGG19 etc.). However, in multi-Target scenarios where, handling selective targets is critical (*** an ambulance in traffic) or prioritized tracking is needed of a 'Pop-Up' target, that is an object entering the field-of-view (FOV)) (i.e. in autonomous driving systems, e-commerce deliveries etc.), the generic networks are not sufficient. In this work, we aim at addressing this specific problem of priority/ selective target tracking in aerial videos. We propose a novel CNN termed 'PriorityNet' with embedded priority information and a recurrent quadrant search algorithm for prioritized tracking and false alarm rejection in multi-Target scenarios. The CNN and the associated algorithm is successfully deployed on a portable NVIDIA Jetson-Nano board (a small form factor GPU) integrated on-board a custom built UAV. The complete system is tested on multiple open-source and custom multi-modal data sets (RGB and Thermal). The results obtained demonstrate the efficacy of the

关键词： Deep learning

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Fast small Object Detection Algorithm Based on Feature Enhancement and Reconstruction 13

Fast Small Object Detection Algorithm Based on Feature Enhan...

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13th International Conference on Wireless Communications and signal processing, WCSP 2021

作者： Huo, Zhiyong Yan, Tianwen Cao, Weiye Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing China

ISBN: (纸本)9781665407854

small object detection is one of the research hotspots and challenges in the field of machine vision. Because small targets have fewer pixels in the image, the number of features captured by the first few layers of the target detection network is very small, and the high-level semantic information such as feature classification disappears quickly, making the accuracy of target detection relatively low. The paper uses sub-pixel convolution to reconstruct feature information, uses SE (Squeeze-and-Excitation) attention mechanism to enhance features, and uses U-Net network to capture feature context information and has the advantages of faster detection speed. The algorithm realizes the fast and accurate identification of small targets. Numerical experimental results on the COCO data set and the SUN data set show that the algorithm improved performance by more than 12% in the case of the bounding box AP compared with mainstream algorithms such as YOLOv4. © 2021 IEEE.

关键词： Convolution

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Research and Implementation of Radar signal processing Algorithm for FPGA-Based DBF System

Research and Implementation of Radar Signal Processing Algor...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

作者： Shuqiong Chen Wenwen Xu Dalong Xu Hao Wang Jianyin Cao Nanjing University of Science and Technology Nanjing Jiangsu Prov China

In order to address the issue of low energy echoes from “low, slow, small” targets, this paper investigates a signal processing system with FPGA as the core architecture. The real-time signal processing for 64 channels and wideband-narrowband pulse echoes are achieved in this paper. Besides, DBF is utilized in the elevation dimension, providing advantages such as a small blind zone, the ability to detect and track targets at various distances, as well as simultaneous formation of multiple beams. This real-time radar processing system, compared to traditional FPGA+DSP architecture phased-array radars, offers advantages of high-speed data transmission, high parallelism, high stability, and high reliability.

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Multiband Radar signal Fusion and Extrapolation Method Based on Transformer Model

Multiband Radar Signal Fusion and Extrapolation Method Based...

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

作者： Chunxu Gong Weidong Li Ruiming Lu Rui Wang Radar Technology Research Institute Beijing Institute of Technology Beijng China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The improvement of range resolution is crucial for enhancing the capability of imaging radars to detect small targets, such as drones. However, due to technical and cost limitations, current radars cannot indefinitely increase hardware bandwidth to enhance range resolution. To address this challenge, the multiband fusion and extrapolation technique has emerged. This method uses multiple discrete sub-band radar signals and fuses them to extrapolate the missing frequency bands, effectively achieving a wideband radar signal. Traditional approaches primarily rely on constructing signal models and estimating model parameters to accomplish fusion and extrapolation. This paper is the first to apply machine learning techniques to the field of multiband fusion and extrapolation, proposing a radar multiband signal fusion and extrapolation method based on a Transformer model. By training the Transformer network, the method enables accurate estimation of unknown frequency band signals. The effectiveness of the approach is validated through electromagnetic simulation data of drone echoes, demonstrating significantly improved estimation accuracy compared to traditional signal fusion and extrapolation methods.

关键词： Extrapolation Accuracy Image resolution Estimation Radar Machine learning Radar imaging Transformers Frequency estimation Electromagnetics

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一种FPGA实现的复杂背景红外小目标检测网络

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北京航空航天大学学报 2023年第2期49卷 295-310页

作者：周海侯晴宇卞春江冯水春刘一腾中国科学院国家空间科学中心北京100190 中国科学院复杂航天系统电子信息技术重点实验室北京100190 中国科学院大学北京100049 哈尔滨工业大学航天学院哈尔滨150001

红外(IR)小目标检测算法具有检测率高、虚警率低、实时性好等优点在红外遥感领域有重要的应用价值。由于复杂背景下小目标对比度低和信噪比(SNR)低,传统红外小目标检测算法难以保证检测性能。在强鲁棒性的红外小目标检测网络(RISTDnet)... 详细信息

红外(IR)小目标检测算法具有检测率高、虚警率低、实时性好等优点在红外遥感领域有重要的应用价值。由于复杂背景下小目标对比度低和信噪比(SNR)低,传统红外小目标检测算法难以保证检测性能。在强鲁棒性的红外小目标检测网络(RISTDnet)基础上,面向更为多样的目标结构特征和更高的实时处理性能要求,提出一种增强型红外小目标检测网络(EISTDnet)与其基于现场可编程逻辑门阵列(FPGA)高性能并行处理的算法。EISTDnet构造了手工特征算法与卷积神经网络相结合的多尺度小目标特征提取框架,采用多级展开思路对卷积核尺寸进行归一化设计,并通过数据深度复用和多维循环并行展开有效提高推理阶段实时处理性能。实验结果表明:采用单片FPGA实现的EISTDnet能够快速实时检测复杂背景下不同大小、低信噪比的小目标,与现有5种算法相比在10^(-3)低虚警率下平均检测率提升49.5%,与RISTDnet相比,在实时处理速度提高1.33倍的优势下,对低信噪比条状小目标检测率提升29.4%,所提算法具有更好的有效性和鲁棒性。

关键词：卷积神经网络红外小目标目标检测现场可编程逻辑门阵列实时

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Improving the signal-to-Clutter Ratio in Sub-THz Radar Using the Radon Transform

Improving the Signal-to-Clutter Ratio in Sub-THz Radar Using...

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International Radar Symposium (IRS)

作者： Samuel Harris Dillon Kumar Anum Pirkani Andy Stove Edward Hoare Duncan A. Robertson Marina Gashinova School of Electronic Electrical and Systems Engineering University of Birmingham Birmingham UK SUPA School of Physics and Astronomy University of St. Andrews St. Andrews UK

ISBN: (数字)9788395602092

ISBN: (纸本)9798350371109

This paper details the use of the Radon transform for improving small target detection in a marine environment by radar. The data used were collected using a 150 GHz radar that stared at anchored targets in a wave tank which simulated maritime conditions. After transforming range-time profiles of the data, a method was developed in Radon space to easily distinguish between the normative form of the sea clutter and the anomalous forms of the targets, after which the normative form is reduced. The proposed processing technique shows consistent improvements of 10–12 dB in the signal-to-clutter ratio.

关键词： Sea surface Radar clutter Radon Radar detection Transforms Object detection Clutter

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