Relevant bibliographies by topics / Radar targets. Pattern recognition systems

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Radar targets. Pattern recognition systems'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Radar targets. Pattern recognition systems"

1

Grazzini, M., and A. Farina. "Radar fusion to detect targets. Part III." Signal Processing 84, no. 8 (2004): 1347–58. http://dx.doi.org/10.1016/j.sigpro.2004.05.009.

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Yu, Xuelian, Xuegang Wang, and Benyong Liu. "Supervised kernel neighborhood preserving projections for radar target recognition." Signal Processing 88, no. 9 (2008): 2335–39. http://dx.doi.org/10.1016/j.sigpro.2007.11.015.

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Haywood, Brett, and Robin J. Evans. "Discrete 2-D system identification for imaging rotating radar targets." Signal Processing 29, no. 2 (1992): 191–211. http://dx.doi.org/10.1016/0165-1684(92)90021-n.

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Xue, Jian, ShuWen Xu, and Penglang Shui. "Knowledge-based adaptive detection of radar targets in generalized Pareto clutter." Signal Processing 143 (February 2018): 106–11. http://dx.doi.org/10.1016/j.sigpro.2017.08.023.

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Coluccia, Angelo, Alessio Fascista, and Giuseppe Ricci. "A novel approach to robust radar detection of range-spread targets." Signal Processing 166 (January 2020): 107223. http://dx.doi.org/10.1016/j.sigpro.2019.07.016.

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Park, Hyung-Rae, Jian Li, and Hong Wang. "Polarization-space-time domain generalized likelihood ratio detection of radar targets." Signal Processing 41, no. 2 (1995): 153–64. http://dx.doi.org/10.1016/0165-1684(94)00097-j.

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Du, Chuan, Bo Chen, Bin Xu, Dandan Guo, and Hongwei Liu. "Factorized discriminative conditional variational auto-encoder for radar HRRP target recognition." Signal Processing 158 (May 2019): 176–89. http://dx.doi.org/10.1016/j.sigpro.2019.01.006.

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Liao, Leiyao, Lan Du, and Jian Chen. "Class factorized complex variational auto-encoder for HRR radar target recognition." Signal Processing 182 (May 2021): 107932. http://dx.doi.org/10.1016/j.sigpro.2020.107932.

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Yang, Shuyuan, Min Wang, and Licheng Jiao. "Radar target recognition using contourlet packet transform and neural network approach." Signal Processing 89, no. 4 (2009): 394–409. http://dx.doi.org/10.1016/j.sigpro.2008.09.015.

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Li, Song, Yongmei Cheng, Ratnasingham Tharmarasa, et al. "3-D tracking of air targets using a single 2-D radar." Signal Processing 166 (January 2020): 107241. http://dx.doi.org/10.1016/j.sigpro.2019.107241.

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Dissertations / Theses on the topic "Radar targets. Pattern recognition systems"

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Wang, Yuanxun. "Radar signature prediction and feature extraction using advanced signal processing techniques /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Djouadi, Abdelhamid. "Analysis of the performance of a parametric and nonparametric classification system : an application to feature selection and extraction in radar target identification /." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487324944214317.

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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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Pham, Quoc Henry. "Hierarchical processing algorithms for object recognition." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/13562.

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Chamberlain, Neil Frederick. "Recognition and analysis of aircraft targets by radar, using structural pattern representations derived from polarimetric signatures /." The Ohio State University, 1989. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487599963593822.

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Pope, Glenn William. "Application of shape-from-shading to synthetic aperture radar." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29755.

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This thesis investigates the viability of applying a shape-from-shading technique to SAR imagery. A shape-from-shading algorithm is derived and tested on a single site for which both a Seasat SAR image and Digitial Elevation Model (DEM) were available. The shape-from-shading technique used in this thesis follows an approach proposed by Frankot and Chellappa for processing slant range SAR imagery. The algorithm incorporates a one-step technique for projecting non-integrable surface orientation estimates onto an integrable set in the frequency domain along with the iterative convergent shape-f
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Gurbuz, Ali Cafer. "Feature detection algorithms in computed images." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24718.

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Thesis (Ph.D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: McClellan, James H.; Committee Member: Romberg, Justin K.; Committee Member: Scott, Waymond R. Jr.; Committee Member: Vela, Patricio A.; Committee Member: Vidakovic, Brani
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Khabou, Mohamed Ali. "Improving shared weight neural networks generalization using regularization theory and entropy maximization /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9953870.

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Kakarlapudi, Swarna. "APPLICATION OF IMAGE ANALYSIS TECHNIQUES IN FORWARD LOOKING SYNTHETIC VISION SYSTEM INTEGRITY MONITORS." Ohio University / OhioLINK, 2004. http://www.ohiolink.edu/etd/view.cgi?ohiou1090265512.

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Raynal, Ann Marie. "Feature-based exploitation of multidimensional radar signatures." 2008. http://hdl.handle.net/2152/17725.

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An important problem in electromagnetics is that of extracting, interpreting, and exploiting scattering mechanisms from the scattered field of a target. Termed “features”, these physics-based descriptions of scattering phenomenology have many and diverse applications such as target identification, classification, validation, and imaging. In this dissertation, the feature extraction, analysis, and exploitation of both synthetic and measured multidimensional radar signatures are investigated. Feature extraction is first performed on simulated data of the highfrequency electromagnetics solver Xpa
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Books on the topic "Radar targets. Pattern recognition systems"

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

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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). SPIE, 2013.

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

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Sadjadi, Firooz A. Automatic target recognition XVIII: 19-20 March 2008, Orlando, Florida, USA. Edited by Society of Photo-optical Instrumentation Engineers. SPIE, 2008.

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Sadjadi, Firooz A. Automatic target recognition XXI: 25-27 April 2011, Orlando, Florida, United States. Edited by SPIE (Society). SPIE, 2011.

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Sadjadi, Firooz A., and Abhijit Mahalanobis. Automatic target recognition XXII: 23-24 April 2012, Baltimore, Maryland, United States. Edited by Society of Photo-optical Instrumentation Engineers. SPIE, 2012.

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Ranney, Kenneth I. Radar sensor technology XII: 18-19 March, 2008, Orlando, Florida, USA. SPIE, 2008.

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Ranney, Kenneth I. Radar sensor technology XIII: 13-15 April 2009, Orlando, Florida, United States. Edited by SPIE (Society). SPIE, 2009.

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

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

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Book chapters on the topic "Radar targets. Pattern recognition systems"

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Murni, A., N. Darwis, M. Mastur, and D. Hardianto. "A texture classification experiment for SAR radar images." In Pattern Recognition in Practice IV - Multiple Paradigms, Comparative Studies and Hybrid Systems. Elsevier, 1994. http://dx.doi.org/10.1016/b978-0-444-81892-8.50023-7.

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Ghazal, Peter. "Pathway Biology Approach to Medicine." In Handbook of Research on Systems Biology Applications in Medicine. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-076-9.ch001.

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An increasing number of biological experiments and more recently clinical based studies are being conducted using large-scale genomic, proteomic and metabolomic techniques which generate high-dimensional data sets. Such approaches require the adoption of both hypothesis and data driven strategies in the analysis and interpretation of results. In particular, data-mining and pattern recognition methodologies have proven particularly useful in this field. The increasing amount of information available from high-throughput experiments has initiated a move from focussed, single gene and protein investigations abstract Systems biology provides a new approach to studying, analyzing, and ultimately controlling biological processes. Biological pathways represent a key sub-system level of organization that seamlessly perform complex information processing and control tasks. The aim of pathway biology is to map and understand the cause-effect relationships and dependencies associated with the complex interactions of biological networks and systems. Drugs that therapeutically modulate the biological processes of disease are often developed with limited knowledge of the underlying complexity of their specific targets. Considering the combinatorial complexity from the outset might help identify potential causal relationships that could lead to a better understanding of the drug-target biology as well as provide new biomarkers for modelling diagnosis and treatment response in patients. This chapter discusses the use of a pathway biology approach to modelling biological processes and providing a new framework for experimental medicine in the post-genomic era.
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Conference papers on the topic "Radar targets. Pattern recognition systems"

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Aldhubaib, F., N. V. Shuley, and I. D. Longstaff. "On the application of pattern recognition to identification of simple targets based on resonance and polarization diversity." In IET International Conference on Radar Systems 2007. IEE, 2007. http://dx.doi.org/10.1049/cp:20070655.

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Notkin, Eran, Tomer Cohen, and Akiva Novoselsky. "Classification of Ground Moving Radar Targets with RBF Neural Networks." In 8th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007254203280333.

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Shao, Yongshe, Ying Chen, and Jing Li. "Automated airport targets detection and location based on radar imagery." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Tianxu Zhang, Carl A. Nardell, Duane D. Smith, and Hangqing Lu. SPIE, 2007. http://dx.doi.org/10.1117/12.745835.

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Jdey, I., M. Dhibi, A. Khenchaf, and A. Toumi. "The contribution of fusion techniques in the recognition systems of radar targets." In IET International Conference on Radar Systems (Radar 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.1663.

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Perju, Veaceslav L. "Adaptive high speed targets recognition systems controlled by the image's parameters." In Pattern Recognition and Tracking XXXI, edited by Mohammad S. Alam. SPIE, 2020. http://dx.doi.org/10.1117/12.2559616.

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Cai, Yiheng, Jie Ma, Hui Li, and ShaoBin Hu. "Automatic Classification of Ice Sheet Subsurface Targets in Radar Sounder Data based on the Capsule Network." In ICCPR '19: 2019 8th International Conference on Computing and Pattern Recognition. ACM, 2019. http://dx.doi.org/10.1145/3373509.3373585.

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Goncalves Manzan, Jose Ricardo, Shigueo Nomura, and Keiji Yamanaka. "Orthogonal bipolar vectors as multilayer perceptron targets for biometric pattern recognition." In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2015. http://dx.doi.org/10.1109/fskd.2015.7382107.

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Wu, Shiyou, Qiong Huang, Shengwei Meng, Jie Chen, Guangyou Fang, and Hejun Yin. "The detection and localization of targets in 2D or 3D scene using ultra-wideband short-pulse through-the-wall radar." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Jayaram K. Udupa, Nong Sang, Laszlo G. Nyul, and Hengqing Tong. SPIE, 2009. http://dx.doi.org/10.1117/12.832588.

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Tian, Yuanyuan, Luyu Lan, and Linna Sun. "A Review of Sonar Image Segmentation for Underwater Small Targets." In PRIS 2020: 2020 International Conference on Pattern Recognition and Intelligent Systems. ACM, 2020. http://dx.doi.org/10.1145/3415048.3416098.

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Liaqat, S., S. A. Khan, M. B. Ihsan, S. Z. Asghar, A. Ejaz, and A. I. Bhatti. "Automatic recognition of ground radar targets based on target RCS and short time spectrum variance." In 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA). IEEE, 2011. http://dx.doi.org/10.1109/inista.2011.5946127.

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