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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...

引用

IET International Radar Conference 2023, IRC 2023

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

ISBN: (纸本)9781839539954

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. © The Institution of Engineering & Technology 2023.

关键词： Contrastive Learning

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Research on data processing method for detection of small and weak targets in space

Research on data processing method for detection of small an...

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Conference on AOPC - Optical Sensing, Imaging, and Display Technology

作者： Sen, Tian Yan, Wen Chinese Acad Sci Xian Inst Opt & Precis Mech Xian Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781510661905;9781510661912

Space debris identification and localization has been studied for a long time at home and abroad, but there are still shortcomings in the detection of very low SNR small targets, especially the detection and recognition of very low SNR faint small targets submerged in the dense galactic background Stars in order to effectively target detection, background removal is essential, in order to distinguish between the target and the stars, through the accumulation of judgement block number of star closely adjacent to judge, if the judgment of the block, star closely adjacent number more than set threshold, so that the point for stars, on the other hand, argue that point as the goal to be detected, then, to the star point of the block The results of the algorithm show that, according to different star map images, the size of matrix block and the threshold of star point can be modified to achieve better star removal effect.

关键词： the Milky Way's background low signal to noise ratio target detection multiframe superposition

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Sea clutter parametric model modification based on measured data

Sea clutter parametric model modification based on measured ...

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IET International Radar Conference 2023, IRC 2023

作者： Qiang, Li Yong, Chen Wang, Dang Xun Cheng, Yin Hong Wen, Xu Shu Hao, Zhou College of Information and Communication Engineering Communication University of China Beijing China National Key Laboratory Scattering and Radiation Beijing100854 China Xidian University Xi'an710126 China

ISBN: (纸本)9781839539954

To enhance the detection rate of small targets on the sea surface, it is crucial to characterize and model sea clutter. A new method is proposed in this paper to modify the parameterized sea clutter model which based on measured data. At first, the measured sea clutter data from an airborne radar is processed to obtain the data near the beam center. And then the measured data is fitted using various distribution models, such as the K distribution, generalized Pareto distribution, and Compound-Gaussian model with Inverse Gaussian texture distribution. The optimal distribution model is determined, and the parameters of this model are estimated to obtain the optimal parameters. At last, these optimal parameters are used to correct the parameterized model of sea clutter, thereby improving its performance. © The Institution of Engineering & Technology 2023.

关键词： Radar clutter

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A multi-layer simulation technique for modeling large and small-scale scattering in radar sounder data 28

A multi-layer simulation technique for modeling large and sm...

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Conference on Image and signal processing for Remote Sensing XXVIII

作者： Cortellazzi, Marco Thakur, Sanchari Sbalchiero, Elisa Bruzzone, Lorenzo Univ Trento Dept Informat Engn & Comp Sci Sommarive 5 I-38123 Trento Italy

ISBN: (纸本)9781510655386;9781510655379

Radar sounders (RS) provide information on subsurface targets for planetary investigations. Several simulation techniques have been developed to support the RS design and the data interpretation. Each technique has different properties and modeling capabilities, achieving different trade-offs between accuracy and computational requirements. The state-of-the-art RS simulation techniques include: i) numerical methods, such as the Finite-Difference Time-Domain (FDTD) technique, which allows the modelling of small-scale scattering phenomena at the cost of high computational requirements;ii) facet modeling and ray-tracing based methods, such as the multi-layered coherent RS (MCS) technique, which requires less computational resources than FDTD, allowing the modeling of large-scale scattering phenomena. Recently an integrated simulation methodology has been presented, for simulating small-scale scattering phenomena in large scenarios. However, this methodology was designed for modeling only surface scattering. In this paper, we propose a method that extends the capabilities of the integrated methodology to model both large and small-scale roughness in a multi-layer scenario. The proposed method uses the FDTD technique to evaluate the effects associated with small-scale roughness in terms of i) scattering phenomena associated with the layers and ii) power losses associated with the signal transmitted through a rough layer. To recursively apply scattering and transmission to multiple layers of the subsurface, a coherent ray-tracing method is used. We experimentally assessed the effectiveness of the proposed methodology on three-layer models by integrating the effect of roughness imposed on the layers and in transmission through them.

关键词： Radar sounder planetary exploration simulations FDTD ray-tracing coherent simulator RIME SRS

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SAR IMAGE SIMULATION METHOD FOR COMPLEX targets BASED ON SHADOW GEOMETRY MODEL

SAR IMAGE SIMULATION METHOD FOR COMPLEX TARGETS BASED ON SHA...

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IET International Radar Conference 2023, IRC 2023

作者： Hu, Xinchang Li, Gang Han, Jianghong qiyuanlab Beijing China Tsinghua University Beijing China

ISBN: (纸本)9781839539954

Since the synthetic aperture radar (SAR) has the advantages of strong permeability and long-range detection, target recognition based on SAR images is playing an increasingly critical role in reconnaissance and marine remote sensing applications. However, due to the high cost of acquiring real SAR images, it is hard to meet the requirements of target recognition in terms of quantity and conditional coverage. This paper presents a fast SAR image simulation method for complex targets to enlarge the dataset for SAR target recognition. In the process of simulating the SAR echo, the shadow effect between the target and the background environment along the direction of radar line-of-sight (RLOS) is considered, and the radar cross-section (RCS) data is obtained through Physical Optics (PO) method. Firstly, the surface of the established 3D model is divided into small surface elements to compute the backscattering coefficient of targets by PO method. Then, the shadow region can be determined by building the incidence angle geometry model. The speed of SAR echo simulation can be improved by fast convolution and the Deramp-Chirp Scaling (CS) algorithm is adopted for imaging processing. Experimental results demonstrate the effectiveness of the proposed method, which can generate the SAR simulated images of the specified target under different system parameters. © The Institution of Engineering & Technology 2023.

关键词： Radar cross section

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Research on Spatial Angle Super-Resolution Algorithm Based on Adaptive Layered Sparse

Research on Spatial Angle Super-Resolution Algorithm Based o...

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2023 International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2023

作者： Xue, Min Gao, Yuexin Wu, Zhixin Xidian University National Key Laboratory of Radar Signal Processing Xi'an China School of Aerospace Science and Technology Xidian University Xi'an China

ISBN: (纸本)9798350318012

Compressed Sensing (CS) theory is based on the sparsity of signals and involves compressively sampling high-dimensional data to obtain a small number of linear observations that contain the complete information of the signals. By solving an optimization problem, the original signals can be recovered from these compressed linear observations. This paper combines CS theory with the sparse characteristics of targets in the spatial domain and proposes an adaptive layered sparse spatial angle super-resolution algorithm. This algorithm converts the target spatial angle super-resolution into the problem of using orthogonal basis to reconstruct sparse signals. Then, the target spatial angle super-resolution is carried out by using single snapshot data through optimization solution, and the target spatial angle domain is continuously reduced through layered orthogonal matching pursuit (LOMP) solution to complete the target spatial angle reconstruction. The effectiveness and robustness of the algorithm are verified by using the proposed algorithm to process the measured data. © 2023 IEEE.

关键词： Compressed sensing

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Dim and small targets based on the fusion of infrared radiation characteristics and spectral characteristics research on detection methods 8

Dim and small targets based on the fusion of infrared radiat...

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8th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Dong, Liu Yang, Yining Liu, Tongyu Yong, Zhang Hu, Yutong Shuai, Jia Ma, Baoqiang Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

The geometric and motion characteristics of false targets are more similar to those of real targets. The detection accuracy of traditional infrared radiation characteristics methods for infrared targets is obviously low. For small infrared weak targets with small size and low signal-to-noise ratio, it is difficult to effectively extract shape features by using a single band detection method. Therefore, this paper makes full use of the advantages of infrared single band image and multispectral image target detection methods, and combines single band detection technology and multispectral detection technology, which not only avoids the defect of single band detection missing alarm, but also reduces the impact of large amount of multispectral detection data, and has real-time performance and good detection effect. The hybrid detection algorithm proposed in this paper has stronger adaptability, higher recognition rate and faster processing ability. © 2022 SPIE

关键词： Infrared radiation

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data-dependent Channel Selection Method for STAP based on MVDR Criterion

Data-dependent Channel Selection Method for STAP based on MV...

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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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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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