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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ping Han Bin Lu Bo Zhou Binbin Han Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

This paper presents a novel aircraft target detection method based on the combination of region segmentation and multi-feature decision. First, the maximum entropy threshold is used to segment region of interest (ROI) like runway, apron and related regions from PolSAR image. Then suspected aircraft targets are extracted in ROI with the feature of dissimilation scattering power. Polarimetric scattering properties of targets and their environmental information are used to construct a detection feature for true aircraft decision from the suspected ones. Experimental results with measured data show that the algorithm can effectively detect aircraft targets with less false and missing alarm rates.

关键词： Aircraft Feature extraction Scattering Entropy Aircraft manufacture Radar polarimetry Aerospace electronics

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Automatic Detection of Airport Runway Area Based on Super-Pixel PolSAR Image Classification 5th

Automatic Detection of Airport Runway Area Based on Super-Pi...

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5th ENRI International Workshop on ATM/CNS, EIWAC 2017

作者： Han, Ping Lin, Zeshan Lu, Xiaoguang Shi, Qingyan Zhang, Zhe Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (纸本)9789811370854

This paper proposes an unsupervised algorithm for airport runway area detection based on super-pixel PolSAR image classification. First, the simple linear iterative clustering (SLIC) algorithm are used to obtain super-pixel image by segmenting the PauliRGB image in order to reduce computational complexity and save computing time. Then, VAT-DBE algorithm is used to estimate and obtain the number of clusters of the image automatically. Combing the polarization information, the super-pixel image is classified by the method of spectral clustering. After that, the suspected airport runway area is extracted according to the scattering characteristics of the runway and classification result. Finally, the airport runway area is detected by using structural and topological characteristics of the runways. The experimental results show that the proposed algorithm can detect the airport runway area effectively with a clear outline, complete structure, and low false alarm rate. It also needs less time and a priori information compared with other methods. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Iterative methods

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Principles and Applications of RELAX: A Robust and Universal Estimator 1

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2019年

作者： Renbiao Wu Qiongqiong Jia Lei Yang Qing Feng

ISBN: (数字)9789811369322

ISBN: (纸本)9789811369315

The multiple signal demixing and parameter estimation problems that result from the impacts of background noise and interference are issues that are frequently encountered in the fields of radar, sonar, communications, and navigation. Research in the signal processing and control fields has always focused on improving the estimation performance of parameter estimation methods at low SNR and maintaining the robustness of estimations in the presence of model errors. This book presents a universal and robust relaxation estimation method (RELAX), and introduces its basic principles and applications in the fields of classical line spectrum estimation, time of delay estimation, DOA estimation, and radar target imaging. This information is explained comprehensively and in great detail, and uses metaphors pertaining to romantic relationships to visualize the basic problems of parameter estimation, the basic principles of the five types of classical parameter estimation methods, and the relationships between these principles. The book serves as a reference for scientists and technologists in the fields of signal processing and control, while also providing relevant information for graduate students in the related fields.

关键词： signal, Image and Speech processing Control and Systems Theory Microwaves, RF and Optical Engineering Mathematical Modeling and Industrial Mathematics

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Simulation System of Aircraft Surveillance in Airport Terminal Area

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Journal of Physics: Conference Series 2022年第1期2228卷

作者： Xiaoyun Shen Haosong Zhai Shuo Zhang Peng Cui Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin 300300 China

Because ordinary users cannot quickly obtain the three-dimensional space position of aircraft in the airport terminal area through a two-dimensional display navigation map, the aircraft surveillance simulation system in the airport terminal area based on virtual reality technology is designed. Through analyzing the Automatic Dependent Surveillance-Broadcast (ADS-B) data, the aircraft motion model is established. The three-dimensional aircraft surveillance simulation system for the airport terminal area is realized by Unreal Engine 4 (UE4). Especially, the changes in flight track and flight attitude about aircraft in the three-dimensional scene are designed. The results show that the aircraft movements can be visualized smoothly and correctly based on ADS-B data. It is helpful for users to observe the aircraft of the airport terminal area and analyze the operation status of the plane.

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Intelligent checking model of Chinese radiotelephony read-backs in civil aviation air traffic control

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Chinese Journal of Aeronautics 2018年第12期31卷 2280-2289页

作者： Guimin JIA Fangyuan CHENG Jinfeng YANG Dan LI Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China

Federal Aviation Administration(FAA) and NASA technical reports indicate that the misunderstanding in radiotelephony communications is a primary causal factor associated with operation errors, and a sizable proportion of operation errors lead to read-back errors. We introduce deep learning method to solve this problem and propose a new semantic checking model based on Long Short-Time Memory network(LSTM) for intelligent read-back error checking. A meanpooling layer is added to the traditional LSTM, so as to utilize the information obtained by all the hidden activation vectors, and also to improve the robustness of the semantic vector extracted by LSTM. A MultiLayer Perceptron(MLP) layer, which can maintain the information of different regions in the concatenated vectors obtained by the mean-pooling layer, is applied instead of traditional similarity function in the new model to express the semantic similarity of the read-back pairs quantitatively. The K-Nearest Neighbor(KNN) classifier is used to verify whether the read-back pairs are consistent in semantics according to the output of MLP layer. Extensive experiments are conducted and the results show that the proposed model is more effective and more robust than the traditional checking model to verify the semantic consistency of read-backs automatically.

关键词： Air traffic control Chinese radiotelephony read-backs LSTM Mean pooling MLP Semantic checking

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Flexible Priori Proximal Bayesian Learning for High-Resolution SAR Imagery

Flexible Priori Proximal Bayesian Learning for High-Resoluti...

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IEEE International Conference on Radar

作者： Xianhua Liao Lei Yang Yuchen Dou Xuan Li Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China Macalester College Saint Paul United States China Greatwall Technology Group Co. Ltd Shenzhen China

ISBN: (纸本)9781665468893

The conventional high-resolution synthetic aperture radar(SAR) imagery based on Bayesian learning encounter the problems of static and inflexible of prior. In this paper, a novel Bayesian learning algorithm based on flexible prior (FP-Bayes) is proposed for high-resolution SAR imagery, in which a new flexible prior is introduced to represent the rich features of target scene. However, the model becomes complicated due to the intended prior, which is hard to be solved by the classic Markov Chain Monte Carlo (MCMC) sampling algorithms. Therefore, the Hamiltonian Monte Carlo (HMC) is applied to this problem. Considering the potential function of the posterior distribution is likely to be non-differentiable, the proximal operator is introduced, and the proximal-HMC (P-HMC) is developed, which is capable of solving the sampling problem of this posterior. To this end, the high-resolution SAR images can be obtained. In the experimental part, the raw SAR data is utilized to verify the effectiveness and superiority of the proposed algorithm. The phase transition diagram (PTD) is also adopted to analyze the imaging performance quantitatively.

关键词： Monte Carlo methods Imaging Scattering Radar imaging Markov processes Apertures Radar polarimetry

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Image restoration of finger-vein networks based on encoder-decoder model

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Optoelectronics Letters 2019年第6期15卷 463-467页

作者： GUO Xiao-jing LI Dan ZHANG Hai-gang YANG Jin-feng College of Electronic Information and Automation Civil Aviation University of ChinaTianjin 300300China Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of ChinaTianjin 300300China

Finger-vein recognition is widely applied on access control system due to the high user acceptance and convince. Improving the integrity of finger-vein is helpful for increasing the finger-vein recognition accuracy. During the process of finger-vein imaging, foreign objects may be attached on fingers, which directly affects the integrity of finger-vein images. In order to effectively extract finger-vein networks, the integrity of venous networks is still not ideal after preprocessing of finger vein images. In this paper, we propose a novel deep learning based image restoration method to improve the integrity of finger-vein networks. First, a region detecting method based on adaptive threshold is presented to locate the incomplete region. Next, an encoder-decoder model is used to restore the venous networks of the finger-vein images. Then we analyze the restoration results using several different methods. Experimental results show that the proposed method is effective to restore the venous networks of the finger-vein images.

关键词： decoder networks incomplete

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Bounded perturbation resilience of generalized viscosity iterative algorithms for split variational inclusion problems

Applied Set-Valued Analysis and Optimization

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Applied Set-Valued Analysis and Optimization 2020年第1期2卷 49-61页

作者： Duan, Peichao Zheng, Xubang College of Science Civil Aviation University of China Tianjin300300 China Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin300300 China

In this paper, we propose a generalized viscosity approximation method combing a sequence of contractive mappings to solve a split variational inclusion problem. The bounded perturbation resilience of the method is investigated in Hilbert spaces. Under mild conditions, we prove that our algorithms strongly converge to a solution of the split variational inclusion problem, which is also the unique solution of some variational inequality problem. Furthermore, we show the convergence and effectiveness of the algorithms via two numerical examples. Our results extend and improve the related results in the literature. ©2020 Applied Set-Valued Analysis and Optimization

关键词： Viscosity

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An Acoustic Model of Civil Aviation's Radiotelephony Communication 19

An Acoustic Model of Civil Aviation's Radiotelephony Communi...

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Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition

作者： Yuanqing Liu Xiaojing Guo Haigang Zhang Jinfeng Yang Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China Civil Aviation University of China Tianjin China Shenzhen Polytechnic Shenzhen China

ISBN: (纸本)9781450376570

Civil Aviation's Radiotelephony Communication (CARC) in China involves Chinese and English. Due to the particular grammatical structure and pronunciation of CARC, the universal cross-lingual acoustic model isn't applicable. For the purpose of achieving the Chinese-English speech recognition of CARC, this paper proposes a cross-lingual acoustic model using a shared-hidden-layer Convolution Depth Neural Network (CDNN) with hidden Markov model. It introduces the Convolution Neural Network (CNN) to overcome the diversity of speech signals. In order to shorten the training and decoding time, the Low Frame Rate (LFR) is added in the feature extraction stage. The experimental results show that the acoustic model based on CDNN is better applied to the domain of CARC. The introduction of CNN can further improve recognition performance. Adding the LFR effectively reduces the training time and the word error rate.

关键词： Convolution depth neural network Low frame rate Cross-lingual speech recognition Civil aviation's radiotelephony communication

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Polsar Image Classification based on Optimal Feature and Convolution Neural Network

Polsar Image Classification based on Optimal Feature and Con...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ping Han Zetao Chen Yishuang Wan Zheng Cheng Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China Basic Experiment Center Civil Aviation University of China Tianjin China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

This paper proposes a new polarimetric synthetic aperture radar (PolSAR) image classification method which uses optimal feature selection and convolutional neural network (CNN). Firstly, original image features are extracted by performing target decomposition on PolSAR data. Then, in order to reduce feature dimension and improve image classification accuracy, a group of optimal and high correlation property features are selected with the method of filtering and wrappering from the original one. Finally, the optimized CNN model is designed to implement PolSAR image classification. Experiments with measured data show that the algorithm can choose a more effective polarization feature set and achieve better classification results.

关键词： Feature extraction Classification algorithms Particle swarm optimization Image classification Training Radar imaging Convolutional neural networks

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