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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...

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IET International Radar conference (IRC 2023)

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

ISBN: (数字)9781837240982

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.

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Long-Time Coherent Integration Algorithm for Sea Surface Maneuvering small Target Based on Dynamic Programming 4

Long-Time Coherent Integration Algorithm for Sea Surface Man...

引用

4th IEEE International conference on Electronics Technology, ICET 2021

作者： Zhang, Yao Liu, Dunge Xia, Zhenghuan Zhang, Tao Zhao, Zhilong Guo, Yuhua Liu, Xin Yang, Yuqian Zhang, Kejia Shi, Huifeng Beijing Institute of Satellite Information Engineering State Key Laboratory of Space-Ground Integrated Information Technology Beijing China

ISBN: (纸本)9781728176734

Detection of sea surface maneuvering small target has long been faced with the problems of strong sea clutter background and low signal-clutter-noise ratio (SCNR), the long-time coherent integration algorithm can effectively improve the SCNR. A long-time coherent integration algorithm is proposed in this paper to process the echo signals of sea surface strongly maneuvering targets with both range and Doppler frequency migration. By using the idea of stage optimization, this algorithm can compensate the range and Doppler frequency migration at the same time, efficiently accumulate the target energy along the trajectory of the target, and effectively improve the SCNR of the target. In addition, this algorithm can also achieve the precise tracking of the target speed and position during the accumulation process, and is suitable for any maneuvering mode. The actual data processing and analysis show that this algorithm can realize the effective accumulation of maneuvering targets with low SCNR, and the accumulation gain is improved by more than 6dB compared with existing methods. © 2021 IEEE.

关键词： Dynamic programming

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Biosensors Based Controller for small Unmanned Aerial Vehicle Navigation

Biosensors Based Controller for Small Unmanned Aerial Vehicl...

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SoutheastCon conference

作者： McCoggle, Mylena Wilson, Shyra Rivera, Andrea Alba-Flores, Rocio Soloiu, Valentin Univ Michigan Dept Mech Engn Dearborn MI 48128 USA Kennesaw State Univ Dept Elect Engn Technol Kennesaw GA 30144 USA Interamer Univ Dept Mech Engn Bayamon PR USA Georgia Southern Univ Dept Elect Engn Statesboro GA USA Georgia Southern Univ Dept Mech Engn Statesboro GA USA

ISBN: (纸本)9781665406529

This work describes a system that uses electromyography (EMG) signals obtained from muscle sensors and an Artificial Neural Network (ANN) for signal classification and pattern recognition that is used to control a small unmanned aerial vehicle using specific arm movements. The main objective of this endeavor is the development an intelligent interface that allows the user to control the flight of a drone beyond direct manual control. The biosensors used in this work were the MyoWare Muscle sensors which contain two EMG electrodes and were used to collect signals from the posterior (extensor) and anterior (flexor) forearm, and the bicep. The collection of the raw signals from each sensor were performed using an Arduino Uno. data processing algorithms were developed with the purpose of classifying the signals generated by the arm's muscles when performing specific movements, namely: flexing, resting, arm-up, and arm motion from right to left. With these arm motions, roll control of the drone was achieved. MATLAB software was utilized to condition the signals and prepare them for the classification stage. To generate the input vector for the ANN and perform the classification, the root mean squared, and the standard deviation were processed for the signals from each electrode. The neuromuscular information was trained using an ANN with a single 10 neurons hidden layer to categorize the four targets. The result of the classification shows that an accuracy of 97.5% was obtained for a single user. Afterwards, classification results were used to generate the appropriate control signals from the computer to the drone through a Wi-Fi network connection. These procedures were successfully tested, where the drone responded successfully in real time to the commanded inputs.

关键词： Biosensors Electromyography Artificial Neural Network Arduino drone flight control Machine Learning

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small-scale pedestrian detection method based on attention mechanism and adaptive feature fusion

Small-scale pedestrian detection method based on attention m...

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2021 International conference on Advanced Algorithms and signal Image processing, AASIP 2021

作者： You, Yuhao Chen, Houjin Li, Yanfeng Wang, Minjun Zhu, Jinlei School of Electronic Information Engineering Beijing Jiaotong University Beijing China Synthesis Electronic Technology Co. Ltd. Shandong Jinan China

In pedestrian detection, high detection accuracy has been achieved for large-scale pedestrians, but for small-scale pedestrians, the detection effect needs to be further improved. In order to improve the detection accuracy of small-scale pedestrians, this paper proposes a YOLOv4 small-scale pedestrian detection algorithm based on the fusion of attention and weighted features. In order to enhance effective features and suppress ineffective features, an attention mechanism that adapts features is introduced in the backbone network CSPdarknet53 to reduce the interference to small-scale pedestrian detection. In order to better integrate features with inconsistent semantics and scales, adaptive channel-weighted feature fusion is used in the feature pyramid, so that the deep and shallow features focus on pedestrian targets of corresponding scales. The logarithmic mean missed detection rate (LAMR) on the small-scale pedestrian test sets "Far" and "Medium" of the Caltech public data set has decreased: 9.67 % And 7.38%. Compared with other pedestrian detection algorithms, it has obvious advantages for small-scale pedestrian detection. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Semantics

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Exploring the Spatial Correlation of Shadowing in RF-based Device-Free Localization by Block-Sparse Bayesian Learning

Exploring the Spatial Correlation of Shadowing in RF-based D...

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IEEE International conference on Robotics and Biomimetics (IEEE ROBIO)

作者： Tan, Jiaju Zhao, Xin Guo, Xuemei Wang, Guoli Wuyi Univ Sch Math & Computat Sci Jiangmen Peoples R China Nankai Univ Inst Robot & Automat Informat Syst IRAIS Tianjin Key Lab Intelligent Robot TjKLIR Tianjin Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Key Lab Machine Intelligence & Adv Comp Minist Educ Guangzhou Peoples R China

ISBN: (纸本)9781665405355

Device-Free Localization (DFL) realized by the Radio Frequency (RF) signal sensing, is an emerging technology to obtain the targets position without needing the target carrying any electronic devices or tags. Due to its through-wall detectability and privacy protection, the RF-based DFL is becoming a promising research topic in the Location-based Services (LBS) applications based on Internet of Things (IoT). Among the RF-based DFL approaches, Radio Tomographic Imaging (RTI) is a low-cost and novel computational imaging method to reconstruct the target-induced shadowing effect in the RF monitored area and then estimate the targets position. However, the multipath interference in the RF sensing network often induces the reconstruction degradation in the RTI system and leads to the position misestimation in DFL applications. Addressing this problem, considering that the targets shadowing may occupy a small spatial range and then express some spatial structure, this aricle focuses on the spatial correlation of the targets shadowing. Then an RTI reconstruction methods based on the Block Sparse Bayesian Learning is proposed to explore the spatial correlation in the sparse targets shadowing. Moreover, the actual localization experiments are conducted to validate the spatial correlation in shadowing is an important property to improve the RTI reconstruction performance and DFL accuracy.

关键词： Computational Imaging

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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 National conference on Radar

作者： Javier Trujillo Rodriguez Fabiola Colone Pierfrancesco Lombardo Dept. of Information Engineering Electronics and Telecommunications (DIET) Sapienza University of Rome Italy

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.

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Dense Convolution Siamese Network for Hyperspectral Image Target Detection

Dense Convolution Siamese Network for Hyperspectral Image Ta...

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IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Kun Shen Weixin Xie Haojin Tang Yanshan Li ATR National Key Laboratory of Defense Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

Compared with grayscale and RGB images, hyperspectral image (HSI) can provide both spatial and spectral information of ground targets, which makes it possible to improve the efficiency and accuracy of target detection. Therefore, the research of HSI target detection algorithms has attracted widespread concern in recent years. With the development of hardware devices and the arrival of big data era, deep learning algorithms have been successfully applied to image processing, text recognition and other fields. However, due to the complex gathering environment of HSI, it is so difficult to obtain a large number of labeled samples, which limits the application of deep learning algorithms in HSI target detection. Therefore, a dense convolution Siamese network (DCSN) is proposed for HSI target detection, which improves the accuracy in the scenery of small-scale training samples. The main contributions of this paper include the following three points. First, we design a target sample generation method based on improved autoencoder to enhance target training data. Then, a background selection method based on density estimation is presented, which can acquire typical background samples effectively. Finally, a spectral feature extraction method based on dense convolution is proposed to extract the more discriminative spectral features. The experimental results of HSI target detection on Muufl Gulfport and San Diego datasets indicate that our proposed DCSN is able to achieve superior performance than the existing target detectors.

关键词： Deep learning Training Convolution Text recognition signal processing algorithms Training data Object detection

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GAUSSIAN PROCESSES OVER GRAPHS

GAUSSIAN PROCESSES OVER GRAPHS

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

作者： Venkitaraman, Arun Chatterjee, Saikat Handel, Peter KTH Royal Inst Technol Div Decis & Control Syst Stockholm Sweden KTH Royal Inst Technol Div Informat Sci & Engn Stockholm Sweden

ISBN: (纸本)9781509066315

Kernel Regression over Graphs (KRG) was recently proposed for predicting graph signals in a supervised learning setting, where the inputs are agnostic to the graph. KRG model predicts targets that are smooth graph signals as over the given graph, given the input when all the signals are deterministic. In this work, we consider the development of a stochastic or Bayesian variant of KRG. Using priors and likelihood functions, our goal is to systematically derive a predictive distribution for the smooth graph signal target given the training data and a new input. We show that this naturally results in a Gaussian process formulation which we call Gaussian Processes over Graphs (GPG). Experiments with real-world datasets show that the performance of GPG is superior to a conventional Gaussian Process (without the graph-structure) for small training data sizes and under noisy training.

关键词： Graph signal processing Gaussian processes Bayesian estimation kernel regression graph-Laplacian

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Using data Analytics to Strengthen Monitoring and Surveillance of Routine Immunization Coverage for Children under One Year in Uganda 14

Using Data Analytics to Strengthen Monitoring and Surveillan...

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14th International Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 14th Int Conf on Bio-inspired Systems and signal processing (BIOsignalS) / 14th Int Conf on Biomedical Electronics and Devices (BIODEVICES)

作者： Nantongo, Bartha Alexandra Nabukenya, Josephine Nabende, Peter Makerere Univ Coll Comp & Informat Syst Dept Informat Syst POB 7062 Kampala Uganda

ISBN: (纸本)9789897584909

Immunization coverage is a traditional key performance indicator that enables stakeholders to monitor child health, investigate gaps, and take remedial actions. It is continuously challenged by validity due to the neglect of unstructured data and process indicators that track small changes/milestones. While empirical evidence indicates digitalized immunization systems establish coverage from structured data, renowned administrative and household survey estimates are often inaccurate/untimely. Government instituted awareness, accessibility, and results-based performance approaches, but stakeholders are challenged by accurate monitoring of performance against Global Vaccination Action Plan coverage targets. This heightens inappropriate strategy implementation leading to persistent low coverage and declining trends. There is scanty literature substantiating the essence of comprehensive immunization indicators in monitoring evidence-based and timely interventions. For this reason, health workers failed to appreciate immunization process indicators and monitoring role. The study aims at developing a real-time immunization coverage monitoring framework that supports evidence-based strategy implementation using prescriptive analytics. The envisaged artifact analyzes a variety of data and monitors immunization performance against comprehensive indicators. It is a less resource-demanding strategy that prompts accurate and real-time insights to support intervention implementation decisions. This study will follow an explanatory research approach by first collecting quantitative data and later qualitative for in-depth analysis.

关键词： data Analytics Monitoring Routine Immunization Coverage Performance

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Research on recognition algorithm of LSS based on video in airport clearance area 2

Research on recognition algorithm of LSS based on video in a...

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2nd IEEE International conference on Big data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2021

作者： Qu, Jingyi Bi, Xinjie Liu, Shanliang Civil Aviation University of China Tianjin Key Laboratory of Advanced Signal Processing Tianjin300300 China

ISBN: (纸本)9780738131221

LSS (Low, small and Slow) targets have characteristics of low cost, simple operation, easy to carry, low take-off requirements, strong sudden takeoff, and difficult to find and handle. With the development of aviation industry, the cases of LSS flying illegally and used in terrorist attacks are increasing day by day. It will not only cause great negative effects on economy, but also have serious influence on the national security and the normal development of the national economy. In order to eliminate the threat of illegal flight of UAV in the airport clearance area to the safe operation of airport, a LSS recognition algorithm based on the deep learning was proposed in the paper. Compared with other detection types, the video detection was used in the algorithm, which has many advantages such as strong visualization, low cost and fast detection speed. By using the Yolov3 object detection model, one of the most popular deep learning methods, the problem of low accuracy of small objects such as LSS can be solved. The experiments show that the Yolov3 model can improve the accuracy for small objects and can effectively detect the LSS. Thus, the necessary precondition for the subsequent LSS countermeasures can be provided, and the safe operation of the airport can be ensured. © 2021 IEEE.

关键词： Object detection

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