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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 2010"

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FBI-Net: Frequency Band Integration Network for Infrared small Target Segmentation

FBI-Net: Frequency Band Integration Network for Infrared Sma...

引用

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

作者： Biqiao Xin Qiang Li Qianchen Mao Jinbao Wang Bingshu Wang School of Software Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

small targets in infrared imagery exhibit challenging characteristics due to their minimal semantic information and the extremely imbalanced distribution between the targets and the background. In this paper, we propose a frequency band integration network to extract salient features of infrared small targets in both the spatial and frequency domains. To excavate the high-frequency features of the small targets, we propose a frequency decoupling-fusion module. To decrease the semantic loss that occurs in deep networks, we propose a semantic injection mechanism to assist in retaining critical information from shallow layers. Experimental results show that our proposed method reaches higher prediction accuracy and robustness in the infrared small target segmentation task compared with other state-of-the-art approaches.

关键词： Wavelet transforms Frequency-domain analysis Semantics signal processing Feature extraction Excavation Robustness Real-time systems Complexity theory Speech processing

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Imaging Detection and Classification for Near-space Dim targets 7

Imaging Detection and Classification for Near-space Dim Targ...

引用

7th International conference on Vision, Image and signal processing, ICVISP 2023

作者： Yuan, Yupu Xi, Runping Chen, Qian School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Province Key Laboratory of Speech & Image Processing Xi'an China National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China

ISBN: (纸本)9781839539848

The motion mode of near-space targets is complex due to their high threat level. The target imaging faces low SNR and susceptibility to background noise. The existing detection and classification algorithms struggle to meet the demands of performance and speed on such targets. To address these issues, a YOLOv5-based target detection algorithm utilizes CARAFE upsampling and the small detection head to extract internal features and edge information of dim target. The algorithm adeptly captures dim target features, enhancing real-time performance and detection accuracy while overcoming subtle feature granularity and extraction difficulties. Aiming at the problem that there is no existing trajectory classification algorithm in the near-space field, we propose an infrared dim target trajectory classification algorithm. The improved attention module is used to enhance the extraction of motion features, and the target category is accurately identified by the prediction module, improving the capacity to extract dependencies between trajectory fragments. We execute comparative experiments on common infrared dim target dataset, near-space infrared dim target dataset and real dataset to confirm the viability of our methods. The outcomes indicate that our proposed method can be applied to the above three types of dim targets and significantly outperforms state-of-the-art methods. © The Institution of Engineering & Technology 2023.

关键词： Extraction

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Holographic Radar Tracking Algorithm for "Low-Slow-small" Maneuvering targets

Holographic Radar Tracking Algorithm for "Low-Slow-Small" Ma...

引用

IET International Radar conference 2023, IRC 2023

作者： Niu, Guo Zhu, Nannan Fang, Xueqing Lei, Xueyue Wang, Yifan Zhong, Fuli Foshan University Foshan China Sun Yat-Sen Univiersity Guangzhou China Jinan University Guangzhou China Guangzhou Communications Technician Institute Guangzhou China

ISBN: (纸本)9781839539954

The proliferation of unauthorized flights and incidents involving "low-slow-small" maneuvering targets presents an increasing threat to national security and the normal progression of the national economy. Effectively addressing the threat has emerged as a vital task in upholding the security of the nation, society, and economy. This paper focuses on the attributes of "low-slow-small" targets and leverages the characteristics of holographic radar echoes to propose a Doppler-based Interaction Multiple Model Multiple Hypothesis Tracking (DIMM-TOMHT) algorithm. This algorithm yields the following advantages: 1) The tracking gate constructed by a single prediction step of the DIMM-TOMHT algorithm encompasses dimensions of Doppler velocity components. Owing to distinct disparities in position and velocity between targets and clutter, the multi-dimensional tracking gate containing Doppler components eliminates numerous irrelevant measurements, thereby reducing computational burden of the tracking algorithm while enhancing the accuracy of correlation and precision of target tracking. 2) To attain optimal global hypothesis trajectories, the DIMM-TOMHT algorithm computes scores for each hypothetical trajectory iteratively, which is employed for confirming transient trajectories and eliminating old trajectories. The algorithm inherently integrates aspects of trajectory management, thereby mitigating the complexity of multi-object tracking systems. Empirical data validate the efficacy of this algorithm in tracking "low-slow-small" targets. © The Institution of Engineering & Technology 2023.

关键词： Object tracking

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AN AUTONOMOUS FEATURE DETECTION METHOD FOR SLOW small targets ON SEA SURFACE BASED ON CONTEXTUAL BANDIT APPROACH

AN AUTONOMOUS FEATURE DETECTION METHOD FOR SLOW SMALL TARGET...

引用

IET International Radar conference 2023, IRC 2023

作者： Wu, Xijie Liu, Tianpeng Liu, Yongxiang Liu, Li Xu, Jianfeng Zhu, Jing National University of Defense Technology Changsha410005 China Rocket Force University of Engineering Xi'an710025 China Unit 92192 of the PLA China

ISBN: (纸本)9781839539954

This paper combines the contextual bandit approach with existing feature detection process, and then proposes an autonomous feature detection method of slow small targets on sea surface based on contextual bandit approach. The method is divided into the training stage and detection stage. In training stage, the method utilizes a large amount of sea clutter data collected in advance to generate the decision regions corresponding to existing feature detectors. In detection stage, every time the radar receives a frame of data, the method selects a feature detector in real time through the modified kernel-based upper confidence bound policy proposed in this paper. Finally, the decision region of real-time detector is used to detect the target in current frame. The superior performance of proposed method is verified by the measured sea clutter datasets. © The Institution of Engineering & Technology 2023.

关键词： Radar clutter

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Cost-Benefit Analysis of Metavariable Variation in Convolutional Autoencoders Applied to Acoustic Backscattering data from small Underwater targets

Cost-Benefit Analysis of Metavariable Variation in Convoluti...

引用

OCEANS Hampton Roads conference

作者： Linhardt, Timothy Sen Gupta, Ananya Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA

ISBN: (数字)9781665468091

ISBN: (纸本)9781665468091

With an end goal of physically interpretable results, we build on progress towards finding generalized low dimension encodings of acoustic backscattering data from sonar measurements of small targets submerged in water. The data set in use is TREX-13. The initial effort succeeded in learning non-invertible mappings (encodings) of the data to a low-dimensional vector space with convolutional autoencoders and sparse convolutional autoencoders. The prior work largely used default values for various "metavariables" according to the Keras and Tensorflow library specifications. Mitigating the failure rate of network training as well as ancillary increases in signal reconstruction accuracy and network training speed are the goals of our current efforts. The metavariables in question are the number of convolutional kernels in each network layer, the layer count, the size of the convolutional kernels, the technique for randomly initializing the values of the kernels, and the choice of nonlinear activation functions between network layers. We perform some cost benefit analysis for the modifications in terms of training time, reconstruction accuracy, and GPU memory utilization, and decide whether to continue using the variations in further research.

关键词： machine learning autoencoder signal processing metavariable acoustics

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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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Extracting Sinusoidal Frequency Modulation signal Signature Using Fourier-Bessel Transform and Short-Time Fraction Fourier Transform 6

Extracting Sinusoidal Frequency Modulation Signal Signature ...

引用

6th International conference on Information Communication and signal processing, ICICSP 2023

作者： Ma, Xiaomeng Gao, Meiguo School of Information and Communication Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350339994

A method using Fourier-Bessel transform (FBT) and short-time fraction Fourier transform (ST-FRFT), for accurate and robust parameter estimate and separation of sinusoidally frequency modulated (SFM) nonstationary signal, is presented. The rotation micro-motion coupling with the rigid body's motion provides an approximately sinusoidal phase modulation, which is known as the micro-Doppler (m-D) signal. According to priori research, the SFM signal shows the nearly linear frequency modulation characteristic in the local part and sparsity features in fraction domain. Firstly, in order to extract the slope of nearly FM and the rotation target's angular frequency, here using FBT method can decompose the rotation target's echo into stationary and nonstationary component. Then, using ST-FRFT method separate the target's signal into the fraction domain where through peak search to catch the SFM signal's slope and rotation angular frequency. This function shows enormous potential in solving the problem that nonstationary component always can't be extracted because of weak echo amplitude. The rotation angle frequency is an important characteristic of the rotating target, which shows great significance to the classification of low-altitude and low-speed small targets. This proposed method has been demonstrated by the air target's simulation data which shows effective results of estimating these parameters. © 2023 IEEE.

关键词： Fourier-Bessel transform (FBT) fraction Fourier transform (FRFT) sinusoidal frequency modulation (SFM) unmanned aerial vehicle (UAV)

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Sea clutter parametric model modification based on measured data

Sea clutter parametric model modification based on measured ...

引用

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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Localized Target Detection Algorithms in Low Illumination Wide-Field of View High-Resolution Environments 24

Localized Target Detection Algorithms in Low Illumination Wi...

引用

4th International conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2024

作者： Xie, Chengyou Huang, Xiaohui Jia, Zhenhong Song, Sensen Wang, Jiajia Zhou, Gang Shi, Fei Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi Xinjiang China

ISBN: (纸本)9798400710247

To solve the problem of difficult detection of small targets in wide-field videos captured by eagle-eye equipment in low-illumination environments. we propose a local moving target detection algorithm based on sample consistency. First, the initial background model is established using the first ten frames of the video. The contrast and texture features between the current and background frames are calculated, and the S component in the HSV space is extracted and fused to obtain the final features. Finally, The target area is located through the global segmentation map and the local tracking detection module is activated. Experimental results demonstrate that Compared to other algorithms, the proposed algorithm reduces the running time by implementing local detection. It effectively minimizes the interference caused by light sources and dark spot noise in low-light environments, thereby enhancing the accuracy of the algorithm. © 2024 Copyright held by the owner/author(s).

关键词： Object detection

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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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