Journal of Aerospace Information Systems最新文献_第10页

Loss-of-Control Prediction of a Quadcopter Using Recurrent Neural Networks 基于递归神经网络的四轴飞行器失控预测

IF 1.5 4区工程技术

Journal of Aerospace Information Systems Pub Date : 2023-08-02 DOI: 10.2514/1.i011231

A. V. Altena, J.J.B.C. Van Beers, C. de Visser

{"title":"Loss-of-Control Prediction of a Quadcopter Using Recurrent Neural Networks","authors":"A. V. Altena, J.J.B.C. Van Beers, C. de Visser","doi":"10.2514/1.i011231","DOIUrl":"https://doi.org/10.2514/1.i011231","url":null,"abstract":"Loss of control (LOC) is a prevalent cause of drone crashes. Onboard prevention systems should be designed requiring low computing power, for which data-driven techniques provide a promising solution. This study proposes the use of recurrent neural networks (RNNs) for LOC prediction. Four architectures were trained in order to identify which RNN configuration is most suitable and if this model can predict LOC for changing aerodynamic characteristics, wind conditions, quadcopter types, and LOC events. One-hundred and seventy-two real-world LOC events were conducted using a 53 g Tiny Whoop, a 73 g URUAV UZ85, and a 265 g GEPRC CineGO quadcopter. For these flights, LOC was initiated by demanding an excessive yaw rate (2000 deg/s), which provokes an unrecoverable upset and subsequent crash. All RNNs were trained using only onboard sensor measurements. It was found that the commanded rotor values provided the clearest early warning signals for LOC because these values showed saturation before LOC. Moreover, all four architectures could correctly and reliably predict the impending LOC event 2 s before it actually occurred. Furthermore, to investigate generality of the methodology, the predictors were successfully applied to flight data in which the quadcopter mass, blade diameter, and blade count were varied.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"70 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84459264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Super-Resolution of Remote Sensing Images from Flagship Lunar-Orbiting Missions 旗舰月球轨道任务的超分辨率遥感图像

IF 1.5 4区工程技术

Journal of Aerospace Information Systems Pub Date : 2023-08-02 DOI: 10.2514/1.i011165

Aneesh M. Heintz, Ian Mackey, M. Peck

引用次数: 0

Multiscale Super-Resolution Remote Imaging via Deep Conditional Normalizing Flows 基于深度条件归一化流的多尺度超分辨率远程成像

4区工程技术

Journal of Aerospace Information Systems Pub Date : 2023-08-01 DOI: 10.2514/1.i011089

Aneesh M. Heintz, Mason Peck, Ian Mackey

引用次数: 0

Aerodynamic Parameter Estimation for a Morphing Unmanned Aerial Vehicle from Flight Tests 基于飞行试验的变形无人机气动参数估计

IF 1.5 4区工程技术

Journal of Aerospace Information Systems Pub Date : 2023-07-30 DOI: 10.2514/1.i011183

Zhe Hui, Gang Chen

引用次数: 0

Selecting Minimal Motion Primitive Libraries with Genetic Algorithms 用遗传算法选择最小运动原语库

IF 1.5 4区工程技术

Journal of Aerospace Information Systems Pub Date : 2023-07-30 DOI: 10.2514/1.i011188

Zachary C. Goddard, K. Wardlaw, Kyle Williams, A. Mazumdar

{"title":"Selecting Minimal Motion Primitive Libraries with Genetic Algorithms","authors":"Zachary C. Goddard, K. Wardlaw, Kyle Williams, A. Mazumdar","doi":"10.2514/1.i011188","DOIUrl":"https://doi.org/10.2514/1.i011188","url":null,"abstract":"Motion primitives allow for application of discrete search algorithms to rapidly produce trajectories in complex continuous space. The maneuver automaton (MA) provides an elegant formulation for creating a primitive library based on trims and maneuvers. However, performance is fundamentally limited by the contents of the primitive library. If the library is too sparse, performance can be poor in terms of path cost, whereas a library that is too large can increase run time. This work outlines new methods for using genetic algorithms to prune a primitive library. The proposed methods balance the path cost and planning time while maintaining the reachability of the MA. The genetic algorithm in this paper evaluates and mutates populations of motion primitive libraries to optimize both objectives. We illustrate the performance of these methods with a simulated study using a nonlinear medium-fidelity F-16 model. We optimize a library with the presented algorithm for obstacle-free navigation and a nap-of-the-Earth navigation task. In the obstacle-free navigation task, we show a tradeoff of a 10.16% higher planning cost for a 96.63% improvement in run time. In the nap-of-the-Earth task, we show a tradeoff of a 9.712% higher planning cost for a 92.06% improvement in run time.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"14 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91133286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical Method for Mining a Prevailing Flight Pattern in Airport Terminal Airspace 挖掘机场航站楼空域流行飞行模式的分层方法

IF 1.5 4区工程技术

Journal of Aerospace Information Systems Pub Date : 2023-07-04 DOI: 10.2514/1.i011263

Xiao Chu, W. Zeng, Xianghua Tan, Yadong Zhou, Dan Zhu

{"title":"Hierarchical Method for Mining a Prevailing Flight Pattern in Airport Terminal Airspace","authors":"Xiao Chu, W. Zeng, Xianghua Tan, Yadong Zhou, Dan Zhu","doi":"10.2514/1.i011263","DOIUrl":"https://doi.org/10.2514/1.i011263","url":null,"abstract":"Due to the variety of flight patterns in airport terminal airspace, as well as the high global similarity of different flight patterns entering and leaving from the same runway or corridor, it is difficult for current mainstream methods to achieve good clustering. To this end, this paper first constructs a truncated dynamic time warping (TDTW) trajectory similarity measurement to characterize different trajectory patterns with high global similarity and large local differences. Furthermore, a hierarchical flight pattern mining method is proposed, which is divided into four layers according to different characteristics. The first three layers of the method classify trajectories according to takeoff and landing types, runways, and corridors; whereas the fourth layer uses a [Formula: see text]-medoid clustering method based on TDTW, thereby making the mining process more controllable and in line with actual operation. Compared to dynamic time warping, the experimental results show that the intraclass compactness and interclass separation of the cluster obtained by the proposed method have decreased and increased by 44.6 and 20.1%, respectively, and the overall performance has improved by 54.1%. More refined and reasonable flight patterns have been obtained.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"08 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85830737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of Exergy Efficiency Optimization of Space Launch Vehicles 空间运载火箭的能效优化评价

IF 1.5 4区工程技术

Journal of Aerospace Information Systems Pub Date : 2023-07-01 DOI: 10.2514/1.i011241

C. White, Jeffrey E. Dyas, Bryan L. Mesmer

引用次数: 0

Introduction to the Systems Engineering’s Top Space Challenges Virtual Collection 介绍系统工程的顶级空间挑战虚拟集合

IF 1.5 4区工程技术

Journal of Aerospace Information Systems Pub Date : 2023-07-01 DOI: 10.2514/1.i011266

Jeffrey M. Newcamp, Michael Z. Miller, A. Golkar, W. Gu, A. Salado

引用次数: 0

Spatiotemporal Image-Based Flight Trajectory Clustering Model with Deep Convolutional Autoencoder Network 基于深度卷积自编码器网络的时空图像飞行轨迹聚类模型

IF 1.5 4区工程技术

Journal of Aerospace Information Systems Pub Date : 2023-06-13 DOI: 10.2514/1.i011194

Ye Liu, K. K. Ng, Nan Chu, Kai Kwong Hon, Xiaoge Zhang

{"title":"Spatiotemporal Image-Based Flight Trajectory Clustering Model with Deep Convolutional Autoencoder Network","authors":"Ye Liu, K. K. Ng, Nan Chu, Kai Kwong Hon, Xiaoge Zhang","doi":"10.2514/1.i011194","DOIUrl":"https://doi.org/10.2514/1.i011194","url":null,"abstract":"Recent studies in four-dimensional flight trajectories attempted to identify the impacts of various flight trajectories and maneuver parameters on air traffic management efficiency and aviation safety. The previous studies attempted to cluster trajectories based on spatial scales. However, these might require converting the flight trajectories to equal lengths for sequence-based clustering. This paper proposes a novel trajectory three-channel image representation and Gaussian mixture model clustering based on several image-processing methodologies. The aircraft’s latitude, longitude, flight level, and ground speed are represented as corresponding pixel information of the image followed by image-based flight trajectory representation and clustering methods (including deep convolutional autoencoder (DCAE), principal component analysis (PCA) image dimensionality reduction, and image feature points extraction) using a half-year of automatic dependent surveillance-broadcast flight trajectory data in the Hong Kong flight information region. The computational results indicate that the image-based trajectory representation produces more insights for trajectory processing, such as the application of convolutional neural networks and image-processing algorithms. In addition, the DCAE model has better performance and robustness for trajectory feature extraction and similarity analysis than PCA, which will provide ideas for multiparameter trajectory similarity analysis and prediction.","PeriodicalId":50260,"journal":{"name":"Journal of Aerospace Information Systems","volume":"64 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85671278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Optimal Trajectories for Aircraft Avoidance of Multiple Weapon Engagement Zones 飞机避开多武器交战区的最佳轨迹

IF 1.5 4区工程技术

Journal of Aerospace Information Systems Pub Date : 2023-06-12 DOI: 10.2514/1.i011224

Patrick M. Dillon, Michael D. Zollars, Isaac E. Weintraub, Alexander Von Moll

引用次数: 0