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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 2000"

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A Nonlinear Frequency Modulation Agile Waveform Optimization Algorithm for Pulse Doppler Radar 2

A Nonlinear Frequency Modulation Agile Waveform Optimization...

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2nd IEEE International conference on signal, Information and data processing, ICSIDP 2024

作者： Deng, Qing-Song Gao, Yu-Hang Ren, Li-Xiang Liu, Quan-Hua Fan, Hua-Yu Beijing Institute of Technology School of Information and Electrinics Beijing China Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Yangtze Delta Region Academy Beijing Institute of Technology Jiaxing China

ISBN: (纸本)9798331515669

Traditional pulse Doppler radar faces limitations such as range ambiguity and clutter folding when detecting long-range slow-moving small targets in strong clutter backgrounds. The agile waveform technique aims to suppress these issues by varying the parameters of the transmitted waveform between pulses within the coherent processing interval (CPI). However, due to the differing range sidelobes produced by the matched filtering output of the agile waveform, range sidelobe modulation (RSM) effect occurs after Doppler processing, resulting in higher sidelobes in the Doppler domain. To address these challenges, a nonlinear frequency modulation (NLFM) agile waveform optimization algorithm for Pulse Doppler radar is proposed in this paper. First, the spectrum of NLFM waveforms is initially mathematically formulated by using a cosine series representation. Then, considering the RSM effect and waveform orthogonality, with the optimization objectives being the similarity and orthogonality of the agile waveforms. Finally, the Genetic Algorithm-Particle Swarm Optimization (GA-PSO) algorithm is employed to solve the optimization objectives, resulting in a set of NLFM waveforms with improved performance. Simulation results demonstrate that the proposed algorithm can strike a good balance between the RSM suppression and waveform orthogonality. The designed waveforms, when filtered by mismatched filters, effectively reduces the signal-to-noise ratio loss(SNRL) relative to the initial waveforms. © 2024 IEEE.

关键词： signal to noise ratio

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Imaging Ultrasound Scattering targets using Density-Enhanced Chaotic Compressive Sampling

Imaging Ultrasound Scattering Targets using Density-Enhanced...

引用

Asia-Pacific-signal-and-Information-processing-Association Annual Summit and conference (APSIPA ASC)

作者： Luong Thi Theu Tran Quang Huy Tran Thi Thuy Quynh Duc-Tan Tran Hoa Binh Univ Hanoi Vietnam Hanoi Pedag Univ 2 Fac Phys Hanoi Vietnam VNU Univ Engn & Technol Fac Elect & Telecommun Hanoi Vietnam Phenikaa Univ Fac Elect & Elect Engn Hanoi 12116 Vietnam

ISBN: (纸本)9798350300673

The ultrasound imaging method is a highly promising technique used in various fields. The utilization of ultrasound technique's backscattering could also allow humans to observe objects as small as the size of the incident wavelength. However, the employing backscattering approach requires high computational calculations and poses a significant obstacle to the production of equipment using the ultrasonic method. In this work, we utilized the pseudo-random compression sampling method in the extended DBIM model, which takes tissue density factor. This is necessary because tissue density provides quantitative information about soft tissue and serves as an image contrast source. This method combines pseudo-random measurement and compressed sensing data recovery technique. The simulation results have proved the effectiveness of the proposed CCS-DBIM method. In terms of imaging quality, at compression ratios as low as 0.5, the large difference in imaging quality is evident. After 5 iterations, the normalization error of the DBIM and CCS-DBIM methods was 0.8828 and 0.5281 respectively, i.e. a 40% reduction in normalization errors when the number of measurements was only half of the variables. The disadvantage of the proposed CCS-DBIM method is that the imaging time is significantly greater than that of the DBIM method. However, with today's parallel computing technology, large amounts of data and computational time will no longer be a big problem.

关键词： Ultrasonic imaging distorted Born iterative method pseudo-random scattered data Born approximation

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small Target Detection on Sea Surface based on Feature-Weighted Support Vector Machine 8

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

引用

8th International conference on signal and Image processing, ICSIP 2023

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

ISBN: (纸本)9798350397932

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. © 2023 IEEE.

关键词： Radar cross section

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Design of Human-computer Interaction System for Infrared small Target Detection 24

Design of Human-computer Interaction System for Infrared Sma...

引用

7th International conference on Image and Graphics processing, ICIGP 2024

作者： Kou, Renke Wang, Chunping Fu, Qiang Li, Wei Yu, Ying Feng, Changfeng Army Engineering University Shijiazhuang Campus Hebei Shijiazhuang050003 China University of Sanya School of Information & Intelligence Engineering Hainan Sanya572022 China School of Information and Electronics Beijing University of Technology Beijing10081 China

ISBN: (纸本)9798400716720

Infrared (IR) passive detection technology is widely utilized for identifying small targets over long distances, owing to its robust anti-interference capabilities and all-weather functionality. As of now, numerous IR small target detection algorithms have been publicly published. To promote research in this field, this paper first briefly introduces single IR small target detection algorithms, which are primarily categorized as model-driven and data-driven. Subsequently, the paper presents human-computer interaction (HCI) systems tailored to these two algorithm categories, incorporating a total of 20 existing model-driven algorithms and 15 data-driven algorithms, respectively. Ultimately, the paper delves into potential avenues for optimizing HCI system design. The proposed IR small target detection HCI systems not only improve user-friendliness, but also provide a benchmark platform for experimental comparison. The introduction of relevant HCI systems can be found on kourenke/GUI-system-for-IR-small-target-detection (***). © 2024 ACM.

关键词： signal detection

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MTKD-Det: An Efficient SAR Target Detector Based on Multi-Teacher Knowledge Distillation 2

MTKD-Det: An Efficient SAR Target Detector Based on Multi-Te...

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2nd IEEE International conference on signal, Information and data processing, ICSIDP 2024

作者： Zhang, Guang Wang, Zhaocheng Wang, Ruonan Song, Hualiang Kang, Hailong Hebei University of Technology School of Electronics and Information Engineering Tianjin China Xidian University Hangzhou Institute of Technology Hangzhou China Xidian University National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9798331515669

As a critical branch of synthetic aperture radar (SAR) interpretation, SAR target detection has many applications in the military and civil fields. Although traditional deep learning methods achieve some satisfactory performance in target detection tasks, they inherently require significant computational resources and interpretation time. Hence, traditional deep learning methods frequently struggle to maintain a balance between high efficiency and precision. It is undoubtedly a huge challenge for target detection tasks. To address this problem, an efficient SAR target detector based on multi-teacher knowledge distillation (MTKD-Det) is proposed in this paper, which can effectively enhance target detection accuracy while reducing inference time. Specifically, a multiple-teacher knowledge distillation strategy is used in the proposed method, which integrates the experiences and methodologies of multiple teachers to collaboratively guide the training and learning of the student network, ultimately enhancing the robustness of the student network. In this work, a novel teacher network is developed to enhance the detection performance of the proposed method specifically for small targets. Furthermore, an attention mechanism is incorporated into the student network to enable it to focus on more critical information, thereby improving overall detection efficacy. Experimental results obtained from a measured SAR target detection dataset convincingly validate the benefits of the proposed approach. © 2024 IEEE.

关键词： Synthetic aperture radar

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On Joint Active-Passive Sonar Detection Performance of Underwater Long-Range Low-Speed small Target 6

On Joint Active-Passive Sonar Detection Performance of Under...

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

作者： Lai, Kai Liu, Xionghou Yang, Yixin Sun, Chao School of Marine Science and Technology Northwestern Polytechnical University Xi'an710072 China Xi'an China Shaanxi Key Laboratory of Underwater Information Technology 710072 China

ISBN: (纸本)9798350339994

We discuss the feasibility of the joint active-passive sonar detection method of underwater low-speed small targets located at long ranges. Specifically, the transmission loss, the reverberation level, and the noise level related to the joint active-passive detection method are modeled and calculated. The relationship between the figure of merit and the transmission loss is used to analyze the performance of the joint active-passive detection method of underwater low-speed small targets. For obtaining a long detection range, we mainly consider the working frequency of both the active and passive sonars to be much lower than traditional small target detection sonars. The active sonar is set to be worked at a frequency band not higher than 40kHz. Meanwhile, the passive sonar is supposed to work at a frequency band not higher than 1 kHz. The factors that affect the performance of the joint active-passive detection method are analyzed in detail. The results indicate that the considered joint active-passive detection method may have a detection range of more than 2000m in a relatively good underwater acoustic environment, which provides a potential way to significantly increase the working range of the underwater detection sonar system. © 2023 IEEE.

关键词： detection performance figure of merit joint active-passive detection underwater low-speed small targets

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A training method to improve the effect of small target detection

A training method to improve the effect of small target dete...

引用

2022 International conference on Image, signal processing, and Pattern Recognition, ISPP 2022

作者： Chen, Boxu Yu, Lixin Meng, Xiao Hua, Yang Beijing Microelectronic Technology Institute Beijing100076 China

ISBN: (纸本)9781510654846

Deep learning target detection has always been a major research direction in the field of artificial intelligence. Its research results are widely used in the fields of automatic driving, security system and medical treatment. This paper proposes a method to improve the detection effect of small targets, which realizes the detection of objects of different scales in the input image, especially to improve the detection effect of small-scale targets. Before the collected data set is sent to the neural network for training, it is first divided into three different scales according to the size of the target to be detected in the image of the data set. Then one or several images in the large target data set are stitched, the images in the small target data set are enlarged, and the above two types of images are used to form a new data set. Finally, the new data set and the original data set are sent to the neural network for training. In this paper, the YOLOX target detection network is used for verification. The results show that the detection effect of the network obtained by this method on small targets has been improved, and the missed detection rate has been reduced from 31.2% to 27.5%. At the same time, the detection effect of large and medium-sized targets has not been sacrificed. © 2022 SPIE.

关键词： Image enhancement

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Sea Surface small Target Detection using Tri-median Normalized Doppler Spectrum Feature 16

Sea Surface Small Target Detection using Tri-median Normaliz...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Guo, Zi-Xun Wu, Kang-Hui Bai, Xiao-Hui Su, Jia Fan, Yi-Fei Gong, Yan-Yun Wang, Ling Shui, Peng-Lang School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China School of Information Xi’an University of Finance and Economics Shaanxi Xi’an710100 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China

ISBN: (纸本)9781510689251

This letter aims to develop a detector for detecting small targets on the sea surface based on feature utilization median normalization. At the long observation time, sea clutter and returns with target show differences of Doppler amplitude spectra (DAS). The median normalized DAS (MNDAS) is used to enhance the Doppler characteristics of returns with target, which is computed from the DAS of the cell under test and its neighboring reference cells. From the MNDAS, three salient features can be extracted for the detection, including the non-zero interval length, vector entropy and global accumulation in the MNDAS. The three features are combined into 3D feature vectors. Considering that the returns with target and their feature vectors cannot be used in the training, one semi-supervised classifier based on the convexhull learning algorithm is built by the feature vectors of sea clutter only. Finally, a new detector based on feature utilization median normalization which can control the false alarm rate precisely is constructed. Through the experiments on the IPIX and CSIR radar databases and one offshore experimental data using an unmanned aerial vehicle with a corner reflector, the proposed detector attains competitive performance and lower computation cost with the TF-tri-feature-based detector. © 2025 SPIE.

关键词： Vectors

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Remote sensing aircraft small object detection algorithm based on YOLOv5

Remote sensing aircraft small object detection algorithm bas...

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2024 International conference on Image, signal processing, and Pattern Recognition, ISPP 2024

作者： Qiu, Yijuan Xue, Jiefeng Zhang, Jie Jiang, Ping Zhang, Gang Lei, Tao National Key Laboratory of Optical Field Manipulation Science and Technology Chinese Academy of Sciences Sichuan Chengdu610209 China Institute of Optics and Electronics Chinese Academy of Sciences Sichuan Chengdu610209 China University of Chinese Academy of Sciences Beijing100049 China Southwest University of Science and Technology 621010 China

ISBN: (纸本)9781510680425

With the rapid development of remote sensing technology, remote sensing images play an important role in the agricultural field, geological field, and natural disaster detection. The size of aircraft in complex scenes in remote sensing images is extremely small, and aircraft of different models have similar shapes. Therefore, improving the accuracy of aircraft target recognition is a challenging task. We propose an improved aircraft small target recognition method based on yolov5, which can improve the recognition accuracy of aircraft targets while ensuring the speed of the model. The specific content is as follows: To address the problem of lack of remote sensing aircraft training sets, we use existing public remote sensing images to combine with aircraft model images. Most of the aircraft models only occupy a dozen to twenty pixels in the 1k*1k image, and perform Scale the generated data set;in order to better combine features of different scales and obtain higher-level feature fusion, introduce the BiFPN module with more residual connections and more complete feature fusion;use the SE attention mechanism to learn the weights of different features, extract information that is more important for detection and improve model performance;in view of the small size of the aircraft, a detection method based on Wasserstein distance such as NWD (Normalized Wasserstein Distance) is selected as the loss function. © 2024 SPIE.

关键词： Training aircraft

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Research on Enterprise Innovation Interaction Model Based on Complex Network and Big data Analysis 4

Research on Enterprise Innovation Interaction Model Based on...

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4th International signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Zhang, Lili Guangxi Vocational Normal University Nanning Guangxi China

ISBN: (纸本)9798331528676

This paper proposes an enterprise innovation interaction model based on complex network and big data analysis. The model uses PageRank algorithm to analyze the importance of each node in the enterprise innovation network, identify the core enterprises in the innovation activities and their key roles in the cooperation network. By constructing a complex network, the cooperation relationship in the enterprise innovation system is visualized, and valuable interaction patterns are extracted by combining big data analysis. The system design includes modules such as data preprocessing, complex network construction and PageRank algorithm implementation, which can effectively improve the accuracy of enterprise innovation interaction. During the simulation process, the paper used a real data set containing 500 enterprises and 2000 cooperation projects. The results show that the system can significantly improve the efficiency of identifying key nodes in the cooperation network between enterprises. The node importance identification accuracy of the PageRank algorithm reaches 93.2%, which is about 10% higher than the traditional method. In addition, the simulation results show that the prediction accuracy of core node identification and innovation interaction patterns in complex networks has also been significantly improved. ©2024 IEEE.

关键词： data accuracy

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