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摘要: 飞机尾流是飞机飞行时在其后方产生的一对反向旋转的强烈湍流，对后续飞机飞行以及机场安全起降影响极大，其演化趋势的预测已成为空中交通安全管制的瓶颈，亟需发展基于实时探测数据的飞机尾流行为预测技术。在雷达探测反演得到的尾流涡心位置和速度环量等特征参数基础上，开展飞机尾流行为预测分析，能够预知飞机尾流危害区域，为机场安全起降动态间隔标准制定提供技术支撑。该文结合风场线性切变和最小二乘拟合方法构建了参数化尾流行为预测模型，解决了经典尾流预测模型气象环境参数未随时间演化实时调整的问题。该文根据复杂风场非线性演化特点，设计了基于无迹卡尔曼滤波的数据同化模型，利用雷达探测数据对尾流行为预测进行实时修正。数值仿真验证和实测数据验证结果表明，基于数据同化的飞机尾流行为预测方法能够根据实时探测数据对尾流行为预测轨迹进行修正，得到更加贴近实测的飞机尾流行为预测轨迹。Abstract: Aircraft wake are a couple of counter-rotating vortices generated by a flying aircraft, which can pose a serious hazard to follower aircraft. The behavior prediction of it is a key issue for air traffic safety management. To this end, we propose a prediction method based on data assimilation, which can be used to predict the evolution and hazard area of aircraft wake vortex from the vortex-core’s positions and circulation. To construct our wake vortex prediction model, we use linear shear and least square estimation. In addition, we use a data assimilation model based on the unscented Kalman filter to instantly correct the predicted trajectories. Our experimental results show that the proposed method performs well and runs steadily, thus, providing an effective tool for aircraft wake vortex prediction and support for the establishment of dynamic wake separation in air traffic management.
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Key words:
- Data assimilation /
- Aircraft wake vortex /
- Behavior prediction /
- Lidar detection
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1. Introduction
Today, the research and application of Artificial Intelligence (AI) has become a major area of scientific and technological development. Developing AI is a major strategy for enhancing national core competitiveness and maintaining national security.
The Massachusetts Institute of Technology (MIT) has not established a new college for decades. However, in October 2018, MIT announced a new facility, the Schwarzman College of Computing[1], and the construction of the Stata Science Center (see Fig. 1) for computer science, AI, data science, and related intersections. Its purpose is to harness the powerful role of AI and big data computing in science and technology of the future. From Fig. 2, the SCR-615B radar built by MIT during World War II is on display in the Stata Science Center lobby. The MIT president also published an article in this year’s MIT newsletter[2] emphasizing the competition and challenges brought by AI.
Figure 1. MIT Stata Science Center
Figure 2. SCR-615B radar displayed in the hallIn 2016, the United States (U.S.) White House released three important reports titled Preparing for the Future of Artificial Intelligence, National Artificial Intelligence Research and Development Strategic Plan, and Artificial Intelligence, and Automation and Economic Report, which promoted the establishment of a Machine Learning and Artificial Intelligence (MLAI) subcommittee that would actively plan for the future development of AI[3]. In January 2018, the United States Department of Defense released a new version of the National Defense Strategy report, stating that the development of advanced computing, big data analysis, and robotics are important factors affecting national security. In June 2018, the U.S. Defense Advanced Research Projects Agency (DARPA) discussed for the first time the preliminary details of the U.S. Electronic Revival Plan. The implementation of this Electronic Revival Plan will accelerate the development of AI hardware. In September of the same year, DARPA announced its commitment to building a system based on common sense, contextual awareness, and higher energy efficiency[4]. In February 2019, U.S. President Trump signed an executive order titled To Maintain U.S. Artificial Intelligence Leadership, which aims to maintain U.S. global leadership in AI. On February 12, 2019, the U.S. Department of Defense website published a Summary of the 2018 Department of Defense Artificial Intelligence Strategy—Harnessing AI to Advance Our Security and Prosperity, which clarified the U.S. military’s strategic initiatives and key areas for deploying AI[5]. The U.S. Department of Defense plans to use DARPA’s Next Generation Artificial Intelligence (AI Next) and Artificial Intelligence Exploration (AIE) projects as benchmarks for exploring and applying AI technologies to enhance military strength. The AI Next project, which was announced in September 2018, is based on the two generations of AI technology that were led by DARPA over the past 60 years. It emphasizes the environmentally adaptive capability of AI. The main areas of this project are to explore new technologies that promote the Department of Defense’s automation of key business processes, improve the robustness and reliability of AI systems, enhance the security and flexibility of machine learning and AI technologies, reduce power consumption and avoid inefficient data collection and performance, and create the next generation of AI algorithms and applications[6]. The AIE program will focus on Third Wave applications and theories of AI and aim to adapt machines to changing conditions. It will streamline proposals, contracts, and funding processes. The goal is to accelerate the research and development of AI platforms to help the U.S. maintain its technical advantages in the field of AI.
In March 2017, France released its Artificial Intelligence Strategy, built a new AI center, and developed data storage and processing platforms, automatic learning technology platforms, and network security platforms[7]. The German Brain Science strategy focuses on robotics and digitization. In 2012, the Max Planck Institute for Scientific Research in Germany cooperated with the U.S. in computational neuroscience[8]. Japan also attaches great importance to the development of AI technology. In 2017, the Japanese government issued the Next Generation Artificial Intelligence Promotion Strategy to clarify its focus on AI development and to promote the extension of AI technology to strong AI and super AI levels[9].
China released the New Generation Artificial Intelligence Development Plan in July 2017 and formulated a three-step goal for the national AI strategy. By 2030, China’s AI theory, technology, and applications will generally reach world-leading levels and become the world’s major AI innovation center[10]. Currently, China is showing very strong scientific research mobilization in the research and application of AI. For example, in August 2017, the National Natural Science Foundation of China (NSFC) released Guidelines for Emergency Management of Basic Research in Artificial Intelligence, which outlines plans to fund research in 25 research directions in three foundational aspects of the AI frontier, including intelligent autonomous movement bodies, intelligent decision-making theory, and key technologies of complex manufacturing processes[11]. We believe that, driven by innovation, China will achieve significant development in the research, application, and industrial fields of AI and AI technology, occupying an important territory in the world of AI.
In this paper, we propose the development of AI technology in the field of space remote sensing and target recognition. In 2017, we hosted the Institute of Electrical and Electronics Engineers’ (IEEE) Remote Sensing Intelligent Processing Conference[12] and published some papers in the IEEE Transactions on Geoscience and Remote Sensing/Geoscience and Remote Sensing Letters[13-16]. We have also published several discussions in the Science & Technology Review [17,18], highlighting concepts regarding physical intelligence and microwave vision. Here we focus on Synthetic Aperture Radar (SAR) target monitoring and information perception and discuss the research on AI information technology against the physical background of the interaction between electromagnetic waves and targets, i.e., the use of this physical intelligence to develop microwave visions that can perceive target information on the electromagnetic spectrum that cannot be recognized by the human eye.
2. Multisource Multimode SAR Information Perception
In the 1950s, SAR images were only single-mode RCS grayscale images used for monitoring military targets. Later, in the 1970s, the development and application of this technology began to make great strides in civilian fields of study, such as ocean wind fields, terrestrial hydrology, vegetation, snow, precipitation, drought, the monitoring and evaluation of natural disasters, and the identification of surface changes, to name a few. Various applications have various needs, and the theoretical and technical issues associated with different scientific connotations have strongly promoted the comprehensive development of SAR technology. Since the beginning of the 21st century, SAR satellite technologies have developed rapidly, with the realization of full polarization, interference, and high- resolution to produce a multisource multimode full-polarization high-resolution SAR (hereinafter referred to as multimode SAR) information technology (see Fig. 3).
Figure 3. Overview of SAR development in various countriesWith the improvement in spatial resolution to meters and decimeters, the perception of multimode SAR remote sensing information has produced a field of science and technology that has great significance for civilian and national defense technology. SAR in the 21st century promotes the research and application of Automatic Target Recognition (ATR). Based on the presence or absence of a one-dimensional to a two-dimensional object map, three-dimensional object feature recognition is achieved, along with identification of multi-dimensional object morphology.
However, SAR information perception and target feature inversion and reconstruction are not accomplished by human vision. The interaction between electromagnetic waves and complex targets and their image-scattering mechanisms provide the physical basis for SAR imaging. We have studied the theoretical parameter modeling, numerical simulation, and physical and numerical characteristics in the frequency, spatial, time, and polarization domains, and have developed polarized SAR parametric simulation software, techniques for scattering and imaging calculations, and target classification, recognition, and feature reconstruction[19].
Multimode SAR remote sensing produces a many series of images with multiple temporal and physical characteristics and rich and multiple types of complex data. Driven by remote sensing big data, remote sensing application technology has progressed in a broad range of areas. However, most of these are limited to traditional data statistical analysis and image processing technologies, which cannot meet the needs of multimode SAR technology and applications. In particular, it is difficult to realize the automatic recognition of various types of targets in the sky, land, and sea, as well as the perception and inversion reconstruction of fine-scale multi-dimensional information.
3. Big Data-driven AI Technology
In recent years, AI technology has attracted considerable attention from science and industry. Based on the recognition of local structure-features-whole target in the eye-retina-brain V1–V4 area, a simple perception rule was established to obtain visual perception ability. Using the method of computational neuroscience and driven by the fitting of big data, multi-layer convolution networks are constructed from the local structure and feature-vector space for large overall network calculations to realize the ability to perceive internal information, which is the basic idea of AI and deep learning.
Similarly, we must determine how to develop a new smart brain-like function suitable for the perception of SAR information from electromagnetic wave image scattering, which differs from computer vision processing that is usually based on optical vision. To do so, it is necessary to construct an intelligent information technology that can perceive SAR information from the microwave spectrum. We call this the electromagnetic AI–new scientific technology, i.e., from optical vision by the human brain to humanoid brain electromagnetic waves–microwave vision, which is driven by remote sensing big data under the guidance of the physics mechanism of multi-source multimode full-polarimetric high-resolution SAR.
Fig. 4 and Fig. 5 illustrate the physical basis of multimode SAR as a forward problem of electromagnetic-wave-scattering modeling simulation and an inverse problem of multi-dimensional information inversion and reconstruction. AI deep learning based on a brain-like computing neural algorithm is driven by various types of big data constrained by the physical background of multimode SAR remote sensing for processing perceptions of AI information for application in various fields.
Figure 4. Research and application of multisource and multimode SAR remote sensing information perception for space-ground-sea targets
Figure 5. Physical intelligence to application of remotely sensed big data4. Microwave Vision to Realize ATR Based on Physical Intelligence
Based on the SAR image-scattering mechanism, we developed a brain-like intelligent function for processing this type of big data to perceive SAR information. This is like seeing microwaves, i.e., microwave vision. Eventually, this technology will be able to perform automatic interpretations online and produce easy-to-accept visual representations and visual semantics. Known as microwave consciousness, this technology plays an important role in the technical methods of visual semantics, reasoning, decision-making, interactive detection, identification, interference, confrontation, and the attack of SAR scattered radiation fields.
In Fig. 6, we propose a combined forward and inverse theory for the creation of electromagnetic-wave-scattering and brain-like AI research to generate a new intelligent algorithm. This cross-discipline electromagnetic AI (EM AI) has important applications in Earth remote sensing, ATR, electronic countermeasures, and satellite navigation communications. Therefore, this proposal represents remote sensing-communication-navigation technology in electromagnetic space.
Figure 6. Artificial intelligence of space electromagneticsWe have recently edited a book series titled Spaceborne Microwave Remote Sensing[20], whereby 14 monographs will be published by Science Press in the next two years, eight monographs of which deal with the acquisition of SAR information (Fig. 7). These include the monograph Intelligent Interpretation of Radar Image Information, written by our laboratory team[21]. Based on the background and research status of SAR image interpretation, this monograph summarizes our laboratory’s latest research progress using deep learning intelligent technology in SAR ATR and polarized SAR feature classification, and provides sample data and program code for relevant chapters.
Figure 7. Spaceborne microwave remote sensing research and application seriesSome of the research conducted at our laboratory on intelligent information perception can be summarized as follows:
• We proposed an intelligent recognition algorithm for SAR targets[15]. The full convolutional network we proposed reduces the number of independent parameters by removing fully connected layers. It achieved a classification accuracy of 99% for a 10-class task when applied to the SAR target classification dataset MSTAR[22]. In addition, an end-to-end target detection–discrimination–recognition method for SAR images was implemented. Furthermore, we proposed a fast-detection algorithm for surface ship targets, established an SAR image ship target data set, and performed a ship target classification experiment based on transfer learning.
• We proposed a deep-learning training network algorithm in a complex domain[16], whereby we can train a Convolutional Neural Network (CNN) of a polarized SAR surface classification with complex multi-dimensional images in a polarized coherence matrix. This algorithm achieved state-of-the-art accuracy of 95% for a 15-class task on the Flevoland benchmark dataset[22].
• We proposed a CNN using few samples for target ATR, which has good network generalization ability. We also studied the target recognition and classification ability of CNN feature-vector distribution under the condition of no samples[14]. Zero-sample learning is important for SAR ATR because training samples are not always suitable for all targets and scenarios. In this paper, we proposed a new generation-based deep neural network framework, the key aspect of which is a generative deconvolutional neural network, called a generator that automatically constructs a continuous SAR target feature space composed of direction-invariant features and direction angles while learning the target hierarchical representation. This framework is then used as a reference for designing and initializing the interpreter CNN, which is antisymmetric to the generator network. The interpreter network is then trained to map any input SAR image to the target feature space.
• We proposed a deep neural network structure for CNN processing to despeckle SAR-image noise[23]. This process uses a CNN to extract image features and reconstruct a discrete RCS Probability Density Function (PDF). The network is trained by a mixed loss function that measures the distance between the actual and estimated SAR image intensity PDFs, which is obtained by the convolution between the reconstructed RCS PDF and the prior speckled PDF. The network can be trained using either simulated or real SAR images. Experimental results on both simulated SAR images and real NASA/JPL AIRSAR images confirm the effectiveness of the proposed noise-despeckling deep neural network.
• Lastly, we proposed a colorized CNN processing method from single-polarized SAR images to polarized SAR images for scene analysis and processing[24]. This paper proposed a deep neural network that converts a single-polarized SAR image into a fully polarized SAR image. This network has two parts, a feature extraction network and a feature translation network that is used to match spatial and polarized features. Using this method, the polarization covariance matrix of each pixel can be reconstructed. The resulting fully polarized SAR image is very close to the real fully polarized SAR image not only visually but also in real PolSAR applications.
In addition, part of the work of our laboratory is to do the SAR-AI-ATR identification of—base on domestic and foreign SAR data including China’s GF-3 SAR data. do the SAR-AI-ATR identification of ground vehicles, airport aircraft, and sea surface ships. In addition, we proposed a CNN method for the inversion of forest tree heights by interferometric SAR, i.e., INSAR, and a method for constructing the reciprocal generation of optical images and microwave radar images by the contrast training of optical and microwave images. The above work can be found in related monographs[21].
5. Conclusion
Data is not synonymous with information. Big data is just material and a driver, and different data have different scientific connotations. Therefore, the use of simple and direct statistics in the analysis of big data cannot realize the perception of connotative information, especially in the imaging of multi-dimensional vectorized complex data of multimode microwave SAR, which is difficult to intuitively perceive by the human eye. In this paper, we proposed the use of AI driven by big data under the guidance of physics to retrieve information and develop new AI models and algorithms to meet the needs of SAR remote sensing physics and applications. Interdisciplinary AI research is very important. The realization of new EM AI technology will drive the development of multiple industries and applications.
At present, research on multimode remote sensing intelligent information and target recognition is still in the exploratory stage, and further research is needed to continue to develop new theories, methods, and applications of microwave vision.
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图 2 激光雷达探测飞机尾流场景设置
Figure 2. Geometry configuration for Lidar detection of wake vortex
图 3 飞机尾流多普勒速度RHI回波分布图
Figure 3. Doppler velocity distribution of wake vortex in an RHI
图 5 估计背景风场的非尾流区域
Figure 5. Regions free of wake vortex that was used to estimate the background wind
图 6 切向速度与多普勒速度的几何关系
Figure 6. Relationship between the tangential velocity and Doppler velocity
图 8 飞机尾流行为预测方法对比(水平方向轨迹)
Figure 8. Comparison between different wake vortex behavior prediction (Horizontal trajectories)
图 9 飞机尾流行为预测方法对比(垂直方向轨迹)
Figure 9. Comparison between different wake vortex behavior prediction (Vertical trajectories)
图 10 飞机尾流行为预测方法对比(速度环量)
Figure 10. Comparison between different wake vortex behavior prediction (Circulation)
图 11 香港机场北跑道激光雷达探测场景设置2014
Figure 11. Geometry setup of the observation in north runway of Hong Kong international airport, 2014
图 12 飞机尾流行为预测方法对比
Figure 12. Comparison between different wake vortex behavior prediction
图 13 香港机场南跑道激光雷达探测场景设置2018
Figure 13. Geometry setup of the observation in south runway of Hong Kong international airport, 2018
图 14 飞机尾流行为预测方法对比
Figure 14. Comparison between different wake vortex behavior prediction
表 1 激光雷达探测参数设置
Table 1. Detection parameters of the Lidar
主要参数 量值 雷达波长 1.55 μm 脉冲宽度 170 ns 时间窗长度 120 ns 距离门宽度 21 m 采样率 50 MHz 脉冲积累数 1500 信号噪声比 –5 dB 扫描速度 2°/s 扫描范围 0～15° 
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表 2 飞机尾流预测位置相对误差
Table 2. Relative error of predict trajectories
主要参数 DS method (%) DA method (%) 横向风
()切变率 纵向风
()–7 0.05 –0.3 17.74 19.63 1.12 1.35 –10 0.05 –0.3 26.83 28.52 2.64 2.41 –5 0.01 –0.3 3.42 3.14 1.98 1.76 –5 0.10 –0.3 4.16 4.71 2.86 2.17 –5 0.05 -0.5 3.59 3.21 1.23 1.01 –5 0.05 –0.8 6.87 6.52 1.36 1.71
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表 3 飞机尾流速度环量相对误差
Table 3. Relative error of wake vortex circulation
主要参数
EDR ()DS method (%) DA method (%) 0.01 3.24 3.58 1.12 1.31 0.03 3.26 3.75 1.25 1.02 0.08 3.04 2.95 1.17 1.16 0.10 3.41 3.25 1.08 1.29
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表 4 香港机场激光雷达探测参数设置
Table 4. Detection parameters of the Lidar in Hong Kong field campaigns
主要参数 量值 雷达波长(μm) 1.54 距离门宽度(m) 25 脉冲宽度(ns) 200 脉冲重复频率(kHz) 20 探测距离(m) 50～6000
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[1] 李健兵, 高航, 王涛, 等. 飞机尾流的散射特性与探测技术综述[J]. 雷达学报, 2017, 6(6): 660–672. doi: 10.12000/JR17068LI Jianbing, GAO Hang, WANG Tao, et al. A survey of the scattering characteristics and detection of aircraft wake vortices[J]. Journal of Radars, 2017, 6(6): 660–672. doi: 10.12000/JR17068 [2] GERZ T, HOLZÄPFEL F, BRYANT W, et al. Research towards a wake-vortex advisory system for optimal aircraft spacing[J]. Comptes Rendus Physique, 2005, 6(4/5): 501–523. doi: 10.1016/j.crhy.2005.06.002 [3] THOBOIS Ludovic and CARIOU Jean-Pierre. Next generation scanning LIDAR systems for optimizing wake turbulence separation minima[J]. Journal of Radars, 2017, 6(6): 689–698. doi: 10.12000/JR17056. [4] HON Kaikwong and CHAN Pakwai. Aircraft wake fortex observations in Hong Kong[J]. Journal of Radars, 2017, 6(6): 709–718. doi: 10.12000/JR17072. [5] Vortex State-of-the-Art & Research Needs. Project report under EC contract 2134622015[R]. 2015. doi: 10.17874/BFAEB7154B0. [6] CHENG J, TITTSWORTH J, GALLO W, et al. The development of wake turbulence recategorization in the United States[C]. 8th AIAA Atmospheric and Space Environments Conference, Washington, USA, 2016: 1–12. [7] HOLZÄPFEL F. Probabilistic two-phase wake vortex decay and transport model[J]. Journal of Aircraft, 2003, 40(2): 323–331. doi: 10.2514/2.3096 [8] HOLZÄPFEL F. Sensitivity analysis of the effects of aircraft and environmental parameters on aircraft wake vortex trajectories and lifetimes[C]. 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, Dallas, USA, 2013: 7–10. [9] DE VISSCHER I, WINCKELMANS G, LONFILS T, et al. The WAKE4D simulation platform for predicting aircraft wake vortex transport and decay: Description and examples of application[C]. AIAA Atmospheric and Space Environments Conference, Toronto, Canada, 2010: 7994. [10] SARPKAYA T, ROBINS R E, and DELISI D P. Wake-vortex eddy-dissipation model predictions compared with observations[J]. Journal of Aircraft, 2001, 38(4): 687–692. doi: 10.2514/2.2820 [11] DELISI D P. Development of the VIPER fast-time wake vortex model (development, assumptions, examples, and plans)[C]. WakeNet3-Europe Specific Workshop on "Operational Wake Vortex Models", Belgium, 2011. [12] PROCTOR F, HAMILTON D, and SWITZER G. TASS driven algorithms for wake prediction[C]. 44th AIAA Aerospace Sciences Meeting and Exhibit, Reno, USA, 2006. [13] HOLZÄPFEL F. On the maturity of wake vortex observation, prediction, and validation[C]. WakeNet3-Eurooe 1st Workshop on "Wake Turbulence Safety in Future Aircraft Operations", Paris, France, 2009. [14] DE VISSCHER I, WINCKELMANS G, and BRICTEUX L. Some reflections on the achievable quality of operational WV prediction using operational meteorological and aircraft inputs[C]. WakeNet3-Europe 1st Workshop on "Wake Turbulence Safety in Future Aircraft Operations", Paris, France, 2009. [15] PRUIS M J. Development of a new probabilistic wake vortex prediction model[C]. WakeNet3-Europe Specific Workshop on "Operational Wake Vortex Models", Belgium, 2011. [16] SCHÖNHALS S, STEEN M, and HECKER P. Wake vortex prediction and detection utilising advanced fusion filter technologies[J]. The Aeronautical Journal, 2011, 115(1166): 221–228. doi: 10.1017/S0001924000005674 [17] LI Jianbing, CHAN P W, WANG Tao, et al. Circulation retrieval of wake vortex with a side-looking scanning Lidar[C]. CIE International Conference on Radar, Guangzhou, China, 2016: 1–4. [18] JULIER S, UHLMANN J, and DURRANT-WHYTE H F. A new method for the nonlinear transformation of means and covariances in filters and estimators[J]. IEEE Transactions on Automatic Control, 2000, 45(3): 477–482. doi: 10.1109/9.847726 [19] JULIER S J and UHLMANN J K. Unscented filtering and nonlinear estimation[J]. Proceedings of the IEEE, 2004, 92(3): 401–422. doi: 10.1109/JPROC.2003.823141 [20] GERZ T, HOLZÄPFEL F, and DARRACQ D. Commercial aircraft wake vortices[J]. Progress in Aerospace Sciences, 2002, 38(3): 181–208. doi: 10.1016/S0376-0421(02)00004-0 [21] 屈龙海. 晴空和湿性大气中飞机尾流雷达散射特性的研究[D]. [博士论文], 国防科技大学, 2015: 29–31.QU Longhai. Study on the radar scattering characteristics of aircraft wake vortex in clear air and moist air[D]. [Ph. D. dissertation], National University of Defense Technology, 2015: 29–31. [22] AHMAD N N and PROCTOR F. Review of idealized aircraft wake vortex models[C]. 52nd Aerospace Sciences Meeting, National Harbor, USA, 2014. [23] BURNHAM D. Chicago monostatic acoustic vortex sensing system, Volume I: Data collection and reduction[R]. FAA-RD-79-103, 1979. [24] SHEN Chun, LI Jianbing, ZHANG Fulin, et al. Two-step locating method for aircraft wake vortices based on Gabor filter and velocity range distribution[J]. IET Radar, Sonar & Navigation, 2020, 14(12): 1958–1967. doi: 10.1049/iet-rsn.2020.0319 [25] LI Jianbing, SHEN Chun, GAO Hang, et al. Path Integration (PI) method for the parameter-retrieval of aircraft wake vortex by Lidar[J]. Optics Express, 2020, 28(3): 4286–4306. doi: 10.1364/OE.382968 [26] 焦云涛. 低空风切变与飞行安全[J]. 民航经济与技术, 1994, (11): 13–14.JIAO Yuntao. Windshear at low altitude and flight safety[J]. Civil Aviation Economics and Technology, 1994, (11): 13–14. [27] WILSON D K, OSTASHEV V E, GOEDECKE G H, et al. Quasi-wavelet calculations of sound scattering behind barriers[J]. Applied Acoustics, 2004, 65(6): 605–627. doi: 10.1016/j.apacoust.2003.11.009 [28] 张宏昇. 大气湍流基础[M]. 北京: 北京大学出版社, 2014: 161–165.ZHANG Hongsheng. Atmospheric Turbulence Foundation[M]. Beijing: Peking University Press, 2014: 161–165. [29] 李金梁. 箔条干扰的特性与雷达抗箔条技术研究[D]. [博士论文], 国防科学技术大学, 2010: 57–58.LI Jinliang. Study on characteristics of chaff jamming and anti-chaff technology for radar[D]. [Ph.D. dissertation], National University of Defense Technology, 2010: 57–58. [30] SMALIKHO I N, BANAKH V A, HOLZÄPFEL F, et al. Method of radial velocities for the estimation of aircraft wake vortex parameters from data measured by coherent Doppler Lidar[J]. Optics Express, 2015, 23(19): A1194–A1207. doi: 10.1364/OE.23.0A1194 [31] 沈淳, 高航, 王雪松, 等. 基于激光雷达探测的飞机尾流特征参数反演系统[J]. 雷达学报, 2020, 9(6): 1032–1044. doi: 10.12000/JR20046SHEN Chun, GAO Hang, WANG Xuesong, et al. Aircraft wake vortex parameter-retrieval system based on Lidar[J]. Journal of Radars, 2020, 9(6): 1032–1044. doi: 10.12000/JR20046 [32] GAO Hang, LI Jianbing, CHAN P W, et al. Parameter-retrieval of dry-air wake vortices with a scanning Doppler Lidar[J]. Optics Express, 2018, 26(13): 16377–16392. doi: 10.1364/OE.26.016377 -
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