Relevant bibliographies by topics / OS-CFAR

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'OS-CFAR'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "OS-CFAR"

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Donohue, K. D., and N. M. Bilgutay. "OS characterization for local CFAR detection." IEEE Transactions on Systems, Man, and Cybernetics 21, no. 5 (1991): 1212–16. http://dx.doi.org/10.1109/21.120072.

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Longo, M., and M. Lops. "OS-CFAR thresholding in decentralized radar systems." IEEE Transactions on Aerospace and Electronic Systems 32, no. 4 (1996): 1257–67. http://dx.doi.org/10.1109/7.543847.

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Kim, Chang-Joo, Dong-Seog Han, and Hwang-Soo Lee. "Generalized OS CFAR detector with noncoherent integration." Signal Processing 31, no. 1 (1993): 43–56. http://dx.doi.org/10.1016/0165-1684(93)90100-o.

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Hammoudi, Z., and F. Soltani. "Distributed CA-CFAR and OS-CFAR detection using fuzzy spaces and fuzzy fusion rules." IEE Proceedings - Radar, Sonar and Navigation 151, no. 3 (2004): 135. http://dx.doi.org/10.1049/ip-rsn:20040560.

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Kamal, Mustafa Subhi, and Jiwa Abdullah. "New algorithm for multi targets detection in clutter edge radar environments." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (2020): 420. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp420-427.

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<p>This paper deals with the problem of multi target detection that appears inside clutter cloud which represent the worst radar environments by using constant false alarm rate CFAR algorithm, in order to achieve maximum probability of detection with constant false alarm rate, to detect target in such environments it need to construct robust constant false alarm CFAR algorithm that excise the target spikes from CFAR window and deal with clutter edges in order to give best possible estimation to the noise background. Modified cell averaged (CA-CFAR) is analyzed and compared with Two important algorithms which are cell averaged (CA-CFAR) and ordered statistics (OS-CFAR) algorithms in additional to the modified CA-CFAR algorithm. All these algorithms were simulated with mat lab and applied them to matlab clutter test model that represent different radar environment cases. Tradeoff among these algorithms depending on their responses.</p>
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Liu, Xu, Shuwen Xu, and Shiyang Tang. "CFAR Strategy Formulation and Evaluation Based on Fox’s H-function in Positive Alpha-Stable Sea Clutter." Remote Sensing 12, no. 8 (2020): 1273. http://dx.doi.org/10.3390/rs12081273.

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The problem of target detection in impulsive non-Gaussian sea clutter has attracted a lot of attention in recent years. The positive alpha-stable (PαS) distribution has been validated as a suitable model for the impulsive non-Gaussian sea clutter. Since the probability density function (PDF) of the PαS variable cannot be expressed as a closed-form expression, the research into constant false alarm rate (CFAR) detectors in PαS distributed sea clutter is limited. This paper formulates and evaluates some CFAR detectors, such as Greatest Of-CFAR (GO-CFAR), Smallest Of-CFAR (SO-CFAR), Order Statistic-CFAR (OS-CFAR) and censored mean level (CML) detectors, in PαS distributed sea clutter. Firstly, the Fox’s H-function is adopted to express the PDF of the PαS variable, and the cumulative density function based on Fox’s H-function is derived in this paper. Then, by use of the properties of the H-function and PαS distribution, exact expressions of the probabilities of false alarm and detection for CFAR detectors in the PαS background are derived. Some CFAR properties of these detectors in the PαS background are also explored. Numerical results based on derived expressions are given and verified by Monte Carlo simulation. Some analyses of detection performance from a practical perspective are also given.
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Hyun, Eu-Gin, and Jong-Hun Lee. "Advanced OS-CFAR Processor Design with Low Computational Effort." Journal of the Korean Institute of Information and Communication Engineering 16, no. 1 (2012): 65–71. http://dx.doi.org/10.6109/jkiice.2012.16.1.065.

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Didi, Joseph Haim, and Nadav Levanon. "Repeated sorting on the sliding window for OS‐CFAR." IET Radar, Sonar & Navigation 13, no. 8 (2019): 1272–78. http://dx.doi.org/10.1049/iet-rsn.2018.5507.

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Blake, S. "OS-CFAR theory for multiple targets and nonuniform clutter." IEEE Transactions on Aerospace and Electronic Systems 24, no. 6 (1988): 785–90. http://dx.doi.org/10.1109/7.18645.

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SK, Jainuddin, Satyanarayana P, and Aruna V.B.K.L. "Multiple targets detection in the marine environment using matlab." International Journal of Engineering & Technology 7, no. 4 (2018): 2397. http://dx.doi.org/10.14419/ijet.v7i4.14088.

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This paper implements multiple targets detection against sea clutter by mathematically modeling target as a single point target and sea-clutter as k-distribution model and observing the deterioration in the effective operation of radar using signal processing by matlab. A Variability Index (VI) algorithm is suggested for detection on the basis of constant false-alarm rate (CFAR) property even in heteroge-neous conditions. Comparative analysis of VI-CFAR against various CFAR methods (CA, GO, SO, OS) for multiple targets detection in heterogeneous environment is done in this paper. Comparison and evaluation is done by means of the data which is simulated in MATLAB. The logical operation of the method is verified in heterogeneous condition.
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Dissertations / Theses on the topic "OS-CFAR"

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Nilsson, Mikael. "Implementation och prestandaanalys av radarsignalbehandlingsalgoritmer på GPU." Thesis, Linköpings universitet, Institutionen för systemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-110595.

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Det här examensarbetet utvärderar om det är möjligt att använda en eller flera GPUs för att under realtidsförhållanden utföra radarsignalbehandling i ett pulsdopplerradarsystem. En kedja med radarsignalbehandlingsalgoritmer som används för att utföra detektion har implementerats med CUDA och sedan prestandaanalyserats med fokus på låg exekveringstid. Två CFAR-detektionsalgoritmer, CA- och OS-CFAR, har inkluderats i analysen. För CFAR-algoritmerna har flera alternativ formulerats och implementerats för att utvärdera hur de bäst kan anpassas för att exekvera på en GPU. Prestandaanalysen av de implementerade algoritmerna visar att det är möjligt för det tänkta systemet att använda grafikkort för att utföra radarsignalbehandlingen i realtid. Implementationslösningar har presenterats både för CA- och OS-CFAR som uppfyller tidskraven för systemet, i vissa fall med god marginal. Lägst exekveringstider erhölls när vissa kompromisser gjordes med algoritmernas flexibilitet. För CA-CFAR erhölls lägst exekveringstider när ett Summed Area Table användes för tröskelvärdesberäkningen. För OS-CFAR uppmättes de lägsta exekveringstiderna när en rankjämförelse gjordes istället för en full sortering. Prestandaanalysen visar även att det på ett effektivt sätt går att skala upp implementationen för att utnyttja fler än en GPU.
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Conference papers on the topic "OS-CFAR"

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Hyun, Eugin, and Jong-Hun Lee. "A New OS-CFAR Detector Design." In 2011 First ACIS/JNU International Conference on Computers, Networks, Systems and Industrial Engineering (CNSI). IEEE, 2011. http://dx.doi.org/10.1109/cnsi.2011.16.

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Shin, Dongseung, Jongdeog Kim, Jonguk Kim, Junhak Bang, and Kee Koo Kwon. "Anchor based insertion sorting algorithm for OS-CFAR." In 2014 IEEE Radar Conference (RadarCon). IEEE, 2014. http://dx.doi.org/10.1109/radar.2014.6875621.

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Melebari, Asem, Amer Melebari, Waleed Alomar, M. Y. Abdul Gaffar, R. De Wind, and J. Cilliers. "The effect of windowing on the performance of the CA-CFAR and OS-CFAR algorithms." In 2015 IEEE Radar Conference. IEEE, 2015. http://dx.doi.org/10.1109/radarconf.2015.7411889.

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Magaz, B., and M. L. Bencheikh. "An efficient FPGA implementation of the OS-CFAR processor." In 2008 International Radar Symposium (IRS). IEEE, 2008. http://dx.doi.org/10.1109/irs.2008.4585737.

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Sor, Ravindra, Juilee S. Sathone, Seema U. Deoghare, and M. S. Sutaone. "OS-CFAR Based on Thresholding Approaches for Target Detection." In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE, 2018. http://dx.doi.org/10.1109/iccubea.2018.8697389.

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Xiangwei, Meng, and Zhao Qiang. "Binary Integration of OS-CFAR detection in a nonhomogeneous background." In 2007 IEEE Aerospace Conference. IEEE, 2007. http://dx.doi.org/10.1109/aero.2007.353056.

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Yuan Sun, Bo Zhang, Chao Wang, and Fan Wu. "Ship detection based on eigenvalue-eigenvector decomposition and OS-CFAR detector." In 2012 International Conference on Computer Vision in Remote Sensing (CVRS). IEEE, 2012. http://dx.doi.org/10.1109/cvrs.2012.6421288.

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Sun, Xiang, Ran Tao, and Xia Bai. "A fast order method on OS-CFAR detector in SAR images." In 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar (APSAR). IEEE, 2009. http://dx.doi.org/10.1109/apsar.2009.5374218.

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Abdou, L., O. Taibaoui, A. Moumen, and A. Taleb Ahmed. "Threshold optimization in distributed OS-CFAR system by using simulated annealing technique." In 2015 4th International Conference on Systems and Control (ICSC). IEEE, 2015. http://dx.doi.org/10.1109/icosc.2015.7153287.

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Tien, Vu Viet, Tran Vu Hop, Luu Hoai Nam, Nguyen Van Loi, and Tran Thi Thanh. "AN ADAPTIVE 2D-OS-CFAR THRESHOLDING IN CLUTTER ENVIRONMENTS: TEST WITH REAL DATA." In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2018. http://dx.doi.org/10.1109/spin.2018.8474143.

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Journal articles
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