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Relevant bibliographies by topics / Automatic Aircraft Recognition System

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Journal articles

Academic literature on the topic 'Automatic Aircraft Recognition System'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "Automatic Aircraft Recognition System"

1

Jia, Jiaqi, and Haibin Duan. "Automatic target recognition system for unmanned aerial vehicle via backpropagation artificial neural network." Aircraft Engineering and Aerospace Technology 89, no. 1 (2017): 145–54. http://dx.doi.org/10.1108/aeat-07-2015-0171.

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Purpose The purpose of this paper is to propose a novel target automatic recognition method for unmanned aerial vehicle (UAV), which is based on backpropagation – artificial neural network (BP-ANN) algorithm, with the objective of optimizing the structure of backpropagation network, to increase the efficiency and decrease the recognition time. A hardware-in-the-loop system for UAV target automatic recognition is also developed. Design/methodology/approach The hybrid model of BP-ANN structure is established for aircraft automatic target recognition. This proposed method identifies controller pa

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2

Bobin, A. V., V. A. Azarov, S. A. Bulgakov, and D. A. Savin. "Technique for recognition of aircrafts and radar traps in the control circuit of airspace control system based on neural network technology." Izvestiya MGTU MAMI 7, no. 1-4 (2013): 124–30. http://dx.doi.org/10.17816/2074-0530-67843.

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The paper proposes a method for building of automatic recognizers of aircrafts on a set of radar measurements based on the cascade of multilayer feedforward neural networks. The practical application of this technique in recognizing of three types of aircraft is presented as well.

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3

Silva Filho, P., E. H. Shiguemori, and O. Saotome. "UAV VISUAL AUTOLOCALIZATON BASED ON AUTOMATIC LANDMARK RECOGNITION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W3 (August 18, 2017): 89–94. http://dx.doi.org/10.5194/isprs-annals-iv-2-w3-89-2017.

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Deploying an autonomous unmanned aerial vehicle in GPS-denied areas is a highly discussed problem in the scientific community. There are several approaches being developed, but the main strategies yet considered are computer vision based navigation systems. This work presents a new real-time computer-vision position estimator for UAV navigation. The estimator uses images captured during flight to recognize specific, well-known, landmarks in order to estimate the latitude and longitude of the aircraft. The method was tested in a simulated environment, using a dataset of real aerial images obtai

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Roopa, K., T. V. Rama Murthy, and P. Cyril Prasanna Raj. "Neural Network Classifier for Fighter Aircraft Model Recognition." Journal of Intelligent Systems 27, no. 3 (2018): 447–63. http://dx.doi.org/10.1515/jisys-2016-0087.

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Abstract Fighter aircraft recognition is important in military applications to make strategic decisions. The complexity lies in correctly identifying the unknown aircraft irrespective of its orientations. The work reported here is a research initiative in this regard. The database used here was obtained by using rapid prototyped physical models of four classes of fighter aircraft: P51 Mustang, G1-Fokker, MiG25-F, and Mirage 2000. The image database was divided into the training set and test set. Two feature sets, Feature Set1 (FS1) and FS2, were extracted for the images. FS1 consisted of 15 ge

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Сафтли, Ф. Х. А., and С. Т. Баланян. "Methodology for assessing the control system of aircraft weapons in the process of aiming controlled aircraft weapons equipped with optelectronic homing heads." Vestnik of Russian New University. Series «Complex systems: models, analysis, management», no. 1 (March 23, 2022): 64–75. http://dx.doi.org/10.18137/rnu.v9187.22.01.p.064.

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Рассматривается повышение эффективности боевого применения управляемой авиационной ракеты класса «воздух – поверхность» по полученным целеуказаниям с беспилотного летательного аппарата, позволяющее на больших расстояниях автоматически решать задачи поиска, обнаружения и распознавания целей в условиях реального масштаба времени при сложной фоноцелевой обстановке в зоне боевых действий, тем самым уменьшая риск попадания самолета-носителя в зону действия ПВО противника. Разработана оптимизированная архитектура сверточной нейронной сети для сегментации изображений и распознавания наземных целей в

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6

Zhang, Li Ping, Chao Wang, Hong Zhang, and Bo Zhang. "Aircraft Type Recognition in High-Resolution SAR Images Using Multi-Scale Autoconvolution." Key Engineering Materials 439-440 (June 2010): 1475–80. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.1475.

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Automatic target recognition is the key stage of SAR image interpretation system and has been taking a great interest to the researchers in recent years. Aiming at the issue of aircraft type recognition in high-resolution SAR images, a novel method based on multi-scale autoconvolution (MSA) affine invariant moment is proposed. First, the texture analysis and clustering method are used to segment the SAR images and then the denoising algorithm and morphological processing are applied to segmented results. Second, 29 MSA features are extracted and form a feature vector to represent the target, t

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7

Bohouta, Gamal. "Automatic speech recognition for unmanned aerial vehicles." Journal of the Acoustical Society of America 152, no. 4 (2022): A98. http://dx.doi.org/10.1121/10.0015671.

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Unmanned aerial vehicles (UAVs), also known as unmanned aerial systems (UASs), are quickly becoming a ubiquitous technology, poised to enter some key large-scale markets in the very near future. Fleets of such vehicles will be required in these large-scale deployments for commercial, industrial, and emergency response, along with the ability to efficiently control these fleets. Voice control and communication between human operators and these fleets will become imperative. This paper explores the framework for building an automatic speech recognition (ASR) use to the control of unmanned aerial

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Shabelnik, Tetyana, Serhii Krivenko, and Olena Koneva. "AUTOMATIC PILOT SYSTEM FOR UNMANNED OF AIRCRAFT IN THE ABSENCE OF RADIO COMMUNICATION." Cybersecurity: Education, Science, Technique 1, no. 9 (2020): 93–103. http://dx.doi.org/10.28925/2663-4023.2020.9.93103.

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One of the most pressing problems of piloting unmanned aerial vehicles (UAV) in the absence of radio communication is considered in the article. Therefore, the aim of the article is to develop an algorithm and method of automatic piloting of UAV in terms of loss of radio control signal using the methods of technical vision. The most effective methods of tracking, identification and detection of landmarks are based on the comparison of reference information (database of known navigation objects) with the observation scene in real time.Working system of automatic piloting of UAVs in the conditio

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Kniaz, V. V. "A Fast Recognition Algorithm for Detection of Foreign 3D Objects on a Runway." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3 (August 11, 2014): 151–56. http://dx.doi.org/10.5194/isprsarchives-xl-3-151-2014.

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!The systems for detection of foreign objects on a runway during the landing of an aircraft are highly demanded. Such systems could be installed in the airport or could be mounted on the board of an aircraft. This work is focused on a fast foreign object recognition algorithm for an onboard foreign object detection system. <br><br> The algorithm is based on 3D object minimal boundary extraction. The boundary is estimated through an iterative process of minimization of a difference between a pair of orthophotos. During the landing an onboard camera produces a sequence of images from

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10

Sun, Yuchuang, Wen Jiang, Jiyao Yang, and Wangzhe Li. "SAR Target Recognition Using cGAN-Based SAR-to-Optical Image Translation." Remote Sensing 14, no. 8 (2022): 1793. http://dx.doi.org/10.3390/rs14081793.

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Target recognition in synthetic aperture radar (SAR) imagery suffers from speckle noise and geometric distortion brought by the range-based coherent imaging mechanism. A new SAR target recognition system is proposed, using a SAR-to-optical translation network as pre-processing to enhance both automatic and manual target recognition. In the system, SAR images of targets are translated into optical by a modified conditional generative adversarial network (cGAN) whose generator with a symmetric architecture and inhomogeneous convolution kernels is designed to reduce the background clutter and edg

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