Готові списки джерел за темами / Radar Recognition

Добірка наукової літератури з теми "Radar Recognition"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Radar Recognition"

1

ŚWIĘTOCHOWSKI, Norbert, and Jacek PIONTEK. "ARTILLERY RADAR RECOGNITION." Scientific Journal of the Military University of Land Forces 164, no. 2 (March 1, 2012): 5–18. http://dx.doi.org/10.5604/01.3001.0002.2771.

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Анотація:
The authors present artillery radar recognition combat effectiveness and the way of its implementation in operations. Artillery firefinder radar stations are designed to provide deep reconnaissance for artillery fires. They provide information on enemy missiles, artil-lery and mortar fire positions. The first part of the article lists and describes the technical determinants which influence the operations of artillery radar stations. The factors affecting radar capabilities are the probability of the object location, coverage area and accuracy. In the other part of the article, the optional way of artillery radar operations is shown, in particular radar positioning and concealment.
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2

Ahmed, Shahzad, Karam Dad Kallu, Sarfaraz Ahmed, and Sung Ho Cho. "Hand Gestures Recognition Using Radar Sensors for Human-Computer-Interaction: A Review." Remote Sensing 13, no. 3 (February 2, 2021): 527. http://dx.doi.org/10.3390/rs13030527.

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Human–Computer Interfaces (HCI) deals with the study of interface between humans and computers. The use of radar and other RF sensors to develop HCI based on Hand Gesture Recognition (HGR) has gained increasing attention over the past decade. Today, devices have built-in radars for recognizing and categorizing hand movements. In this article, we present the first ever review related to HGR using radar sensors. We review the available techniques for multi-domain hand gestures data representation for different signal processing and deep-learning-based HGR algorithms. We classify the radars used for HGR as pulsed and continuous-wave radars, and both the hardware and the algorithmic details of each category is presented in detail. Quantitative and qualitative analysis of ongoing trends related to radar-based HCI, and available radar hardware and algorithms is also presented. At the end, developed devices and applications based on gesture-recognition through radar are discussed. Limitations, future aspects and research directions related to this field are also discussed.
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3

Lundn, Jarmo, and Visa Koivunen. "Automatic Radar Waveform Recognition." IEEE Journal of Selected Topics in Signal Processing 1, no. 1 (June 2007): 124–36. http://dx.doi.org/10.1109/jstsp.2007.897055.

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4

Wang, Bin, Shunan Wang, Dan Zeng, and Min Wang. "Convolutional Neural Network-Based Radar Antenna Scanning Period Recognition." Electronics 11, no. 9 (April 26, 2022): 1383. http://dx.doi.org/10.3390/electronics11091383.

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Анотація:
The antenna scanning period (ASP) of radar is a crucial parameter in electronic warfare (EW) which is used in many applications, such as radar work pattern recognition and emitter recognition. For antennas of radars and EW systems, which perform scanning circularly, the method based on threshold measurement is invalid. To overcome this shortcoming, this study proposes a method using the convolutional neural network (CNN) to recognize the ASP of radar under the condition that antennas of the radar and EW system both scan circularly. A system model is constructed, and factors affecting the received signal power are analyzed. A CNN model for rapid and accurate ASP radar classification is developed. A large number of received signal time–power images of three separate ASPs are used for the training and testing of the developed model under different experimental conditions. Numerical experiment results and performance comparison demonstrate high classification accuracy and effectiveness of the proposed method in the condition that antennas of radar and EW system are circular scan, where the average recognition accuracy for radar ASP is at least 90% when the signal to-noise ratio (SNR) is not less than 30 dB, which is significantly higher than the recognition accuracy of NAC and AFT methods based on adaptive threshold detection.
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5

Wang, Bin, Shunan Wang, Dan Zeng, and Min Wang. "Convolutional Neural Network-Based Radar Antenna Scanning Period Recognition." Electronics 11, no. 9 (April 26, 2022): 1383. http://dx.doi.org/10.3390/electronics11091383.

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Анотація:
The antenna scanning period (ASP) of radar is a crucial parameter in electronic warfare (EW) which is used in many applications, such as radar work pattern recognition and emitter recognition. For antennas of radars and EW systems, which perform scanning circularly, the method based on threshold measurement is invalid. To overcome this shortcoming, this study proposes a method using the convolutional neural network (CNN) to recognize the ASP of radar under the condition that antennas of the radar and EW system both scan circularly. A system model is constructed, and factors affecting the received signal power are analyzed. A CNN model for rapid and accurate ASP radar classification is developed. A large number of received signal time–power images of three separate ASPs are used for the training and testing of the developed model under different experimental conditions. Numerical experiment results and performance comparison demonstrate high classification accuracy and effectiveness of the proposed method in the condition that antennas of radar and EW system are circular scan, where the average recognition accuracy for radar ASP is at least 90% when the signal to-noise ratio (SNR) is not less than 30 dB, which is significantly higher than the recognition accuracy of NAC and AFT methods based on adaptive threshold detection.
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6

Wang, Bin, Shunan Wang, Dan Zeng, and Min Wang. "Convolutional Neural Network-Based Radar Antenna Scanning Period Recognition." Electronics 11, no. 9 (April 26, 2022): 1383. http://dx.doi.org/10.3390/electronics11091383.

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Анотація:
The antenna scanning period (ASP) of radar is a crucial parameter in electronic warfare (EW) which is used in many applications, such as radar work pattern recognition and emitter recognition. For antennas of radars and EW systems, which perform scanning circularly, the method based on threshold measurement is invalid. To overcome this shortcoming, this study proposes a method using the convolutional neural network (CNN) to recognize the ASP of radar under the condition that antennas of the radar and EW system both scan circularly. A system model is constructed, and factors affecting the received signal power are analyzed. A CNN model for rapid and accurate ASP radar classification is developed. A large number of received signal time–power images of three separate ASPs are used for the training and testing of the developed model under different experimental conditions. Numerical experiment results and performance comparison demonstrate high classification accuracy and effectiveness of the proposed method in the condition that antennas of radar and EW system are circular scan, where the average recognition accuracy for radar ASP is at least 90% when the signal to-noise ratio (SNR) is not less than 30 dB, which is significantly higher than the recognition accuracy of NAC and AFT methods based on adaptive threshold detection.
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7

Du, Congju, and Bin Tang. "Novel Unconventional-Active-Jamming Recognition Method for Wideband Radars Based on Visibility Graphs." Sensors 19, no. 10 (May 21, 2019): 2344. http://dx.doi.org/10.3390/s19102344.

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Анотація:
Radar unconventional active jamming, including unconventional deceptive jamming and barrage jamming, poses a serious threat to wideband radars. This paper proposes an unconventional-active-jamming recognition method for wideband radar. In this method, the visibility algorithm of converting the radar time series into graphs, called visibility graphs, is first given. Then, the visibility graph of the linear-frequency-modulation (LFM) signal is proved to be a regular graph, and the rationality of extracting features on visibility graphs is theoretically explained. Therefore, four features on visibility graphs, average degree, average clustering coefficient, Newman assortativity coefficient, and normalized network-structure entropy, are extracted from visibility graphs. Finally, a random-forests (RF) classifier is chosen for unconventional-active-jamming recognition. Experiment results show that recognition probability was over 90% when the jamming-to-noise ratio (JNR) was above 0 dB.
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8

Mi, Shengnan, Xinzhuo Liu, and Zhiyu Qu. "Recognition of Radar Signal Modulation Based on Fractional Fourier Transform." International Journal of Signal Processing Systems 5, no. 2 (June 2017): 65–69. http://dx.doi.org/10.18178/ijsps.5.2.65-69.

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9

Bartsch, A., F. Fitzek, and R. H. Rasshofer. "Pedestrian recognition using automotive radar sensors." Advances in Radio Science 10 (September 18, 2012): 45–55. http://dx.doi.org/10.5194/ars-10-45-2012.

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Abstract. The application of modern series production automotive radar sensors to pedestrian recognition is an important topic in research on future driver assistance systems. The aim of this paper is to understand the potential and limits of such sensors in pedestrian recognition. This knowledge could be used to develop next generation radar sensors with improved pedestrian recognition capabilities. A new raw radar data signal processing algorithm is proposed that allows deep insights into the object classification process. The impact of raw radar data properties can be directly observed in every layer of the classification system by avoiding machine learning and tracking. This gives information on the limiting factors of raw radar data in terms of classification decision making. To accomplish the very challenging distinction between pedestrians and static objects, five significant and stable object features from the spatial distribution and Doppler information are found. Experimental results with data from a 77 GHz automotive radar sensor show that over 95% of pedestrians can be classified correctly under optimal conditions, which is compareable to modern machine learning systems. The impact of the pedestrian's direction of movement, occlusion, antenna beam elevation angle, linear vehicle movement, and other factors are investigated and discussed. The results show that under real life conditions, radar only based pedestrian recognition is limited due to insufficient Doppler frequency and spatial resolution as well as antenna side lobe effects.
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10

Huang, Jing, Bin Wu, Peng Li, Xiao Li, and Jie Wang. "Few-Shot Learning for Radar Emitter Signal Recognition Based on Improved Prototypical Network." Remote Sensing 14, no. 7 (March 31, 2022): 1681. http://dx.doi.org/10.3390/rs14071681.

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Анотація:
In recent years, deep learning has been widely used in radar emitter signal identification and has significantly increased recognition rates. However, with the emergence of new institutional radars and an increasingly complex electromagnetic environment, the collection of high-quality signals becomes difficult, leading to a result that the amount of some signal types we own are too few to converge a deep neural network. Moreover, in radar emitter signal identification, most existing networks ignore the signal recognition of unknown classes, which is of vital importance for radar emitter signal identification. To solve these two problems, an improved prototypical network (IPN) belonging to metric-based meta-learning is proposed. Firstly, a reparameterization VGG (RepVGG) net is used to replace the original structure that severely limits the model performance. Secondly, we added a feature adjustment operation to prevent some extreme or unimportant samples from affecting the prototypes. Thirdly, open-set recognition is realized by setting a threshold in the metric module.
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Дисертації з теми "Radar Recognition"

1

Cole, Zachary K. "Radar target recognition using bispectrum correlation." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Jun%5FCole.pdf.

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Анотація:
Thesis (M.S. in Physics)--Naval Postgraduate School, June 2007.
Thesis Advisor(s): Brett Borden. "June 2007." Description based on title screen as viewed on July 31, 2007. Includes bibliographical references (p. 79-80). Also available in print.
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2

Kothe, Martin. "Object Recognition with Surveillance Radar Systems." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:kon4-opus-1161.

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3

Yeo, Jiunn Wah. "Bi-spectral method for radar target recognition." Thesis, Monterey, Calif. : Naval Postgraduate School, 2006. http://bosun.nps.edu/uhtbin/hyperion.exe/06Dec%5FYeo_Jiunn.pdf.

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Анотація:
Thesis (M.S. in Combat Systems Science and Technology))--Naval Postgraduate School, December 2006.
Thesis Advisor(s): Brett Borden, Donald L. Walters. "December 2006." Includes bibliographical references (p. 71-72). Also available in print.
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4

Green, Thomas Joseph. "Three-dimensional object recognition using laser radar." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/13073.

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Анотація:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1992.
Includes bibliographical references (leaves 217-220).
by Thomas Joseph Green, Jr.
Ph.D.
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5

French, A. "Target recognition techniques for multifunction phased array radar." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/19675/.

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This thesis, submitted for the degree of Doctor of Philosophy at University College London, is a discussion and analysis of combined stepped-frequency and pulse-Doppler target recognition methods which enable a multifunction phased array radar designed for automatic surveillance and multi-target tracking to offer a Non Cooperative Target Recognition (NCTR) capability. The primary challenge is to investigate the feasibility of NCTR via the use of high range resolution profiles. Given stepped frequency waveforms effectively trade time for enhanced bandwidth, and thus resolution, attention is paid to the design of a compromise between resolution and dwell time. A secondary challenge is to investigate the additional benefits to overall target classification when the number of coherent pulses within an NCTR wavefrom is expanded to enable the extraction of spectral features which can help to differentiate particular classes of target. As with increased range resolution, the price for this extra information is a further increase in dwell time. The response to the primary and secondary challenges described above has involved the development of a number of novel techniques, which are summarized below: • Design and execution of a series of experiments to further the understanding of multifunction phased array Radar NCTR techniques • Development of a ‘Hybrid’ stepped frequency technique which enables a significant extension of range profiles without the proportional trade in resolution as experienced with ‘Classical’ techniques • Development of an ‘end to end’ NCTR processing and visualization pipeline • Use of ‘Doppler fraction’ spectral features to enable aircraft target classification via propulsion mechanism. Combination of Doppler fraction and physical length features to enable broad aircraft type classification. • Optimization of NCTR method classification performance as a function of feature and waveform parameters. • Generic waveform design tools to enable delivery of time costly NCTR waveforms within operational constraints. The thesis is largely based upon an analysis of experimental results obtained using the multifunction phased array radar MESAR2, based at BAE Systems on the Isle of Wight. The NCTR mode of MESAR2 consists of the transmission and reception of successive multi-pulse coherent bursts upon each target being tracked. Each burst is stepped in frequency resulting in an overall bandwidth sufficient to provide sub-metre range resolution. A sequence of experiments, (static trials, moving point target trials and full aircraft trials) are described and an analysis of the robustness of target length and Doppler spectra feature measurements from NCTR mode data recordings is presented. A recorded data archive of 1498 NCTR looks upon 17 different trials aircraft using five different varieties of stepped frequency waveform is used to determine classification performance as a function of various signal processing parameters and extent (numbers of pulses) of the data used. From analysis of the trials data, recommendations are made with regards to the design of an NCTR mode for an operational system that uses stepped frequency techniques by design choice.
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6

Pisane, Jonathan. "Automatic target recognition using passive bistatic radar signals." Phd thesis, Supélec, 2013. http://tel.archives-ouvertes.fr/tel-00963601.

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We present the design, development, and test of three novel, distinct automatic target recognition (ATR) systems for the recognition of airplanes and, more specifically, non-cooperative airplanes, i.e. airplanes that do not provide information when interrogated, in the framework of passive bistatic radar systems. Passive bistatic radar systems use one or more illuminators of opportunity (already present in the field), with frequencies up to 1 GHz for the transmitter part of the systems considered here, and one or more receivers, deployed by the persons managing the system, and not co-located with the transmitters. The sole source of information are the signal scattered on the airplane and the direct-path signal that are collected by the receiver, some basic knowledge about the transmitter, and the geometrical bistatic radar configuration. The three distinct ATR systems that we built respectively use the radar images, the bistatic complex radar cross-section (BS-RCS), and the bistatic radar cross-section (BS-RCS) of the targets. We use data acquired either on scale models of airplanes placed in an anechoic, electromagnetic chamber or on real-size airplanes using a bistatic testbed consisting of a VOR transmitter and a software-defined radio (SDR) receiver, located near Orly airport, France. We describe the radar phenomenology pertinent for the problem at hand, as well as the mathematical underpinnings of the derivation of the bistatic RCS values and of the construction of the radar images.For the classification of the observed targets into pre-defined classes, we use either extremely randomized trees or subspace methods. A key feature of our approach is that we break the recognition problem into a set of sub-problems by decomposing the parameter space, which consists of the frequency, the polarization, the aspect angle, and the bistatic angle, into regions. We build one recognizer for each region. We first validate the extra-trees method on the radar images of the MSTAR dataset, featuring ground vehicles. We then test the method on the images of the airplanes constructed from data acquired in the anechoic chamber, achieving a probability of correct recognition up to 0.99.We test the subspace methods on the BS-CRCS and on the BS-RCS of the airplanes extracted from the data acquired in the anechoic chamber, achieving a probability of correct recognition up to 0.98, with variations according to the frequency band, the polarization, the sector of aspect angle, the sector of bistatic angle, and the number of (Tx,Rx) pairs used. The ATR system deployed in the field gives a probability of correct recognition of $0.82$, with variations according to the sector of aspect angle and the sector of bistatic angle.
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7

Ehrman, Lisa M. "Automatic target recognition using passive radar and a coordinated flight model." Thesis, Available online, Georgia Institute of Technology, 2004:, 2004. http://etd.gatech.edu/theses/available/etd-06072004-131128/unrestricted/ehrman%5Flisa%5Fm%5F200405%5Fms.pdf.

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8

Olsson, Andreas. "Target recognition by vibrometry with a coherent laser radar." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1730.

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Laser vibration sensing can be used to classify military targets by its unique vibration signature. A coherent laser radar receives the target´s rapidly oscillating surface vibrations and by using proper demodulation and Doppler technique, stationary, radially moving and even accelerating targets can be taken care of.

A frequency demodulation method developed at the former FOA, is for the first time validated against real data with turbulence, scattering, rain etc. The issue is to find a robust and reliable system for target recognition and its performance is therefore compared with some frequency distribution methods. The time frequency distributions have got a crucial drawback, they are affected by interference between the frequency and amplitude modulated multicomponent signals. The system requirements are believed to be fulfilled by combining the FOA method with the new statistical method proposed here, the combination being suggested as aimpoint for future investigations.

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9

Lane, R. O. "Bayesian super-resolution with application to radar target recognition." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/10593/.

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This thesis is concerned with methods to facilitate automatic target recognition using images generated from a group of associated radar systems. Target recognition algorithms require access to a database of previously recorded or synthesized radar images for the targets of interest, or a database of features based on those images. However, the resolution of a new image acquired under non-ideal conditions may not be as good as that of the images used to generate the database. Therefore it is proposed to use super-resolution techniques to match the resolution of new images with the resolution of database images. A comprehensive review of the literature is given for super-resolution when used either on its own, or in conjunction with target recognition. A new superresolution algorithm is developed that is based on numerical Markov chain Monte Carlo Bayesian statistics. This algorithm allows uncertainty in the superresolved image to be taken into account in the target recognition process. It is shown that the Bayesian approach improves the probability of correct target classification over standard super-resolution techniques. The new super-resolution algorithm is demonstrated using a simple synthetically generated data set and is compared to other similar algorithms. A variety of effects that degrade super-resolution performance, such as defocus, are analyzed and techniques to compensate for these are presented. Performance of the super-resolution algorithm is then tested as part of a Bayesian target recognition framework using measured radar data.
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10

Yen, Brent J. 1977. "Target recognition performance for FLIR and laser radar systems." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86854.

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Анотація:
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (leaves 69-70).
by Brent J. Yen.
M.Eng.
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Книги з теми "Radar Recognition"

1

Engineers, Institution of Electrical, ed. Introduction to radar target recognition. London: Institution of Electrical Engineers, 2005.

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2

Nebabin, V. G. Methods and techniques of radar recognition. Boston: Artech House, 1995.

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3

Schachter, Bruce J. Automatic target recognition. Bellingham, Washington: SPIE, 2016.

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4

Rihaczek, August W. Principles of high-resolution radar. Boston: Artech House, 1996.

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5

Fang kong lei da mu biao shi bie ji shu: Target recognition techniques of surveillance radar. Beijing Shi: Guo fang gong ye chu ban she, 2008.

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6

Md.) Automatic Target Recognition (Conference) (23rd 2013 Baltimore. Automatic Target Recognition XXIII: 29-30 April 2013, Baltimore, Maryland, United States. Edited by Sadjadi Firooz A, Mahalanobis Abhijit, and SPIE (Society). Bellingham, Washington, USA: SPIE, 2013.

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7

Shirman, Yakov D. Computer simulation of aerial target radar scattering, recognition, detection, and tracking. Boston: Artech House, 2002.

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8

Shirman, Yakov D. Computer simulation of aerial target radar scattering, recognition, detection, and tracking. Boston: Artech House, 2002.

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9

Day, James V. Construction of a continuous wave frequency modulation sensitive laser radar for use in target identification. Monterey, Calif: Naval Postgraduate School, 1997.

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10

Society of Photo-optical Instrumentation Engineers, ed. Automatic target recognition XVIII: 19-20 March 2008, Orlando, Florida, USA. Bellingham, Wash: SPIE, 2008.

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Частини книг з теми "Radar Recognition"

1

Ezquerra, N. F. "Target Recognition Considerations." In Principles of Modern Radar, 646–77. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1971-9_21.

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2

Jiang, Qiuxi. "Network Radar Countermeasure Systems for Target Recognition." In Network Radar Countermeasure Systems, 131–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48471-5_3.

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3

Borden, Brett. "Phase Monopulse Tracking and Its Relationship to Noncooperative Target Recognition." In Radar and Sonar, 45–55. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4684-7832-7_5.

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4

Jordanov, Ivan, and Nedyalko Petrov. "Intelligent Radar Signal Recognition and Classification." In Recent Advances in Computational Intelligence in Defense and Security, 101–35. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26450-9_5.

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5

Gamba, Jonah. "Target Recognition and Classification Techniques." In Radar Signal Processing for Autonomous Driving, 105–21. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9193-4_8.

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6

Hänsch, Ronny, and Olaf Hellwich. "Object Recognition from Polarimetric SAR Images." In Radar Remote Sensing of Urban Areas, 109–31. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-3751-0_5.

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7

Dong, Jian, Li Zhang, Yufeng Ling, Jian Lu, and Zhiming Cai. "Action Recognition Using WiFi Radar Signal Characteristics." In Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021), 515–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76346-6_47.

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8

Güler, S., G. Garcia, L. Gülen, and M. N. Toksöz. "The Detection of Geological Fault Lines in Radar Images." In Pattern Recognition Theory and Applications, 193–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83069-3_16.

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9

Jin, Xin, Le Wu, Xinghui Zhou, Geng Zhao, Xiaokun Zhang, Xiaodong Li, and Shiming Ge. "Predicting Aesthetic Radar Map Using a Hierarchical Multi-task Network." In Pattern Recognition and Computer Vision, 41–50. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03335-4_4.

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10

Liu, Yu, Yuheng Wang, Haipeng Liu, Anfu Zhou, Jianhua Liu, and Ning Yang. "Long-Range Gesture Recognition Using Millimeter Wave Radar." In Green, Pervasive, and Cloud Computing, 30–44. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64243-3_3.

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Тези доповідей конференцій з теми "Radar Recognition"

1

Novak, Les. "Target recognition and polarimetric SAR." In 2008 IEEE Radar Conference (RADAR). IEEE, 2008. http://dx.doi.org/10.1109/radar.2008.4721157.

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2

Toumi, A., A. El Housseini, and A. Khenchaf. "Aircrafts Recognition using Convolutional Neurons Network." In International Conference on Radar Systems (Radar 2017). Institution of Engineering and Technology, 2017. http://dx.doi.org/10.1049/cp.2017.0519.

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3

Li, Gaopeng, Zhao Sun, and Yun Zhang. "ISAR Target Recognition Using Pix2pix Network Derived from cGAN." In 2019 International Radar Conference (RADAR). IEEE, 2019. http://dx.doi.org/10.1109/radar41533.2019.171345.

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4

Cui, Jingjing, Jon Gudnason, and Mike Brookes. "Hidden Markov models for multi-perspective radar target recognition." In 2008 IEEE Radar Conference (RADAR). IEEE, 2008. http://dx.doi.org/10.1109/radar.2008.4721004.

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5

Shi, Xiaoran, Yaxin Li, Feng Zhou, and Lei Liu. "Human Activity Recognition Based on Deep Learning Method." In 2018 International Conference on Radar (RADAR). IEEE, 2018. http://dx.doi.org/10.1109/radar.2018.8557335.

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6

Sun, Yuliang, Tai Fei, Xibo Li, Alexander Warnecke, Ernst Warsitz, and Nils Pohl. "Multi-Feature Encoder for Radar-Based Gesture Recognition." In 2020 IEEE International Radar Conference (RADAR). IEEE, 2020. http://dx.doi.org/10.1109/radar42522.2020.9114664.

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7

Wan, Jinwei, Bo Chen, Yijun Yuan, Hongwei Liu, and Lin Jin. "Radar HRRP Recognition using Attentional CNN with Multi-resolution Spectrograms." In 2019 International Radar Conference (RADAR). IEEE, 2019. http://dx.doi.org/10.1109/radar41533.2019.171237.

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8

Amin, Moeness G., Zhengxin Zeng, Tao Shan, and Ronny G. Guendel. "Automatic Arm Motion Recognition Using Radar for Smart Home Technologies." In 2019 International Radar Conference (RADAR). IEEE, 2019. http://dx.doi.org/10.1109/radar41533.2019.171318.

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9

Liang, Huaiyuan, Xiangrong Wang, Maria S. Greco, and Fulvio Gini. "Enhanced Hand Gesture Recognition using Continuous Wave Interferometric Radar." In 2020 IEEE International Radar Conference (RADAR). IEEE, 2020. http://dx.doi.org/10.1109/radar42522.2020.9114807.

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10

Miller, R. J., and D. J. Shephard. "Model-Based Aircraft Recognition." In 2006 International Radar Symposium. IEEE, 2006. http://dx.doi.org/10.1109/irs.2006.4338138.

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Звіти організацій з теми "Radar Recognition"

1

Xue, Kefu, and Sam Sink. Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) Parametric Study. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada418766.

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2

Ling, Hao. Exploitation of Microdoppler and Multiple Scattering Phenomena for Radar Target Recognition. Fort Belvoir, VA: Defense Technical Information Center, August 2006. http://dx.doi.org/10.21236/ada452992.

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3

Foster, Thomas. Application of Pattern Recognition Techniques for Early Warning Radar (EWR) Discrimination. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada298895.

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4

Ling, Hao. Exploitation of Microdoppler and Multiple Scattering Phenomena for Radar Target Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada420040.

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5

Youmans, Douglas G., and George A. Hart. Three-Dimensional Template Correlations for Direct-Detection Laser-Radar Target Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada389661.

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6

Binford, Thomas O., and Tsung-Liang Chen. Context and Quasi-Invariants in Automatic Target Recognition (ATR) with Synthetic Aperture Radar (SAR) Imagery. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada400048.

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7

Withman, Raymond, Donnie Cates, and Bob Kotz. The Affect of Image Compression on a Synthetic Aperture Radar Automatic Target Recognition Prescreener and the Relation to SAR Image Statistics. Fort Belvoir, VA: Defense Technical Information Center, August 1997. http://dx.doi.org/10.21236/ada337807.

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