Relevant bibliographies by topics / CA-CFAR algorithm

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Academic literature on the topic 'CA-CFAR algorithm'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "CA-CFAR algorithm"

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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 impor
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Leila, Tabet, and Faouzi Soltan. "A modified environmental adaptive CFAR detector in non-homogenous gaussian clutter." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e10580. http://dx.doi.org/10.54021/seesv5n2-527.

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The Environmental Adaptive CFAR (EA-CFAR ) detector, which is based on both Cell-Averaging CFAR (CA-CFAR) and the ordered statistics CFAR (OS-CFAR) detectors, selects an optimal CFAR detector depending on the environment. A mathematical analysis of the EA-CFAR validates the robustness of the detector in homogenous and non homogenous background. Since the Censored Mean Level detector CFAR (CMLD-CFAR) has a better performance in a multiple target environment than the OS-CFAR, in this paper; we propose a new CFAR algorithm which is a generalization of the EA-CFAR., namely, the MEA-CFAR (Modified
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Radosavljević, Zvonko, and Dejan Ivković. "An approach of constant false alarm ratio for improved target tracking." Scientific Technical Review 70, no. 3 (2020): 17–23. http://dx.doi.org/10.5937/str2003017r.

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Each radar has the function of surveillance of certain areas of interest. In particular, the radar also has the function of tracking moving targets in that territory with some probability of detection, which depends on the type of detector. Constant false alarm ratio (CFAR) is a very commonly used detector. Changing the probability of target detection can directly affect the quality of tracking the moving targets. The paper presents the theoretical basis of the influence of CFAR detectors on the quality of tracking, as well as an approach to the selection of CFAR detectors, CATM CFAR, which en
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Qin, Tian-Ci, Zhong-Xun Wang, Yong Huang, Hao-Xuan Wei, and Yan Liu. "VIHCEMOS-CFAR Detector Based on Improved VI-CFAR." Journal of Physics: Conference Series 2613, no. 1 (2023): 012007. http://dx.doi.org/10.1088/1742-6596/2613/1/012007.

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Abstract A variability index heterogeneous clutter estimate modified ordered statistics constant false alarm rate (VIHCEMOS-CFAR) detector based on improved VI-CFAR is proposed. First calculate the VI and MR values and then judge the clutter background: If the background is uniform, cell averaging (CA) target detection strategy is adopted; HCE detection strategy is adopted if it is clutter edge background; If it is a complex background composed of multiple targets or strong clutter, the OS detection strategy is adopted. The method of judging the interfering targets and strong clutter in the re
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Li, Juan, Yunlong Dong, Ningbo Liu, Yong Huang, Xingyu Jiang, and Jinping Sun. "A Novel Copula-Based Multi-Feature CFAR Framework for Radar Target Detection." Remote Sensing 17, no. 13 (2025): 2299. https://doi.org/10.3390/rs17132299.

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Multi-feature radar target detection enhances the discrimination between targets and clutter, thereby improving detection accuracy. However, the complex nonlinear dependencies among features present significant challenges for precise control of the false alarm rate (FAR). In this paper, a novel constant false alarm rate (CFAR) framework for multi-feature detection is proposed. First, a Copula-CFAR theorem is established, which models the feature dependence structure and enables the derivation of closed-form expressions for probability of false alarm (PFA) and detection probability across vario
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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 an
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Putri, Farra Anindya, Dayat Kurniawan, Rahmawati Hasanah, et al. "Development of FMCW Radar Signal Processing for High-Speed Railway Collision Avoidance." Jurnal Elektronika dan Telekomunikasi 22, no. 1 (2022): 40. http://dx.doi.org/10.55981/jet.482.

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Collision is the main issue in safe transportation, including in the railway system. Sensor systems have been developed to detect obstacles to prevent a collision, such as using cameras. One disadvantage of the camera systems is that performance detection decreases in a not clean environment, like the target position behind the fogs. This paper discusses the development of frequency modulated continuous wave (FMCW) radar signal processing for high-speed railway collision avoidance. The development of radar signal processing combines a two-dimensional constant false alarm rate (2D-CFAR) and rob
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Sun Binbin, 孙宾宾, 沈涛 Shen Tao, 李洪鹏 Li Hongpeng, 崔晓荣 Cui Xiaorong, and 陈雨魁 Chen Yukui. "Improved CA-CFAR Algorithm Based on LFMCW Radar Multi-Target Detection." Laser & Optoelectronics Progress 58, no. 8 (2021): 0815005. http://dx.doi.org/10.3788/lop202158.0815005.

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Endo, Koji, Kohei Yamamoto, and Tomoaki Ohtsuki. "A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar." Sensors 22, no. 23 (2022): 9401. http://dx.doi.org/10.3390/s22239401.

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A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefo
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Monakov, A. A. "CFAR Target Detector in Synthetic Aperture Radar." Journal of the Russian Universities. Radioelectronics 27, no. 3 (2024): 52–67. http://dx.doi.org/10.32603/1993-8985-2024-27-3-52-67.

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Introduction. Constant false alarm rate (CFAR) detectors have found application in synthetic aperture radar (SAR) systems. The operating principle of a classic cell averaging detector (CA-CFAR detector) is based on comparing the decision statistics in the test resolution element with an adaptive threshold, which is calculated from signals in the reference cells. The decision statistic is an estimate of the signal power. Therefore, target signal detection is based on the brightness contrast of the test and reference resolution cells. Such a detector is optimal provided that the noise background
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Conference papers on the topic "CA-CFAR algorithm"

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Zhao, Huining, and Sisan He. "Fast implementation of CA-CFAR algorithm for GPU." In Fourth International Conference on Computer Technology, Information Engineering, and Electron Materials (CTIEEM 2024), edited by Massimo Ficco and Yixian Yang. SPIE, 2025. https://doi.org/10.1117/12.3058601.

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Ge, Yinchao, Zu-Zhen Huang, Qiang Liu, and Qing Lan. "Fast implementation of CA-CFAR algorithm based on FFT." In 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2021. http://dx.doi.org/10.1109/iaeac50856.2021.9390788.

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Miftahushudur, Tajul, Dayat Kurniawan, and A. R. Budiman Putra. "Summed area table for optimizing of processing time on CA CFAR algorithm." In 2015 International Conference on Radar, Antenna, Microwave, Electronics and Telecommunications (ICRAMET). IEEE, 2015. http://dx.doi.org/10.1109/icramet.2015.7380784.

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Liang, Zhihuan, Yanghao Jin, Buge Liang, and Jinjun Mo. "A Modified CA-CFAR Multi-human Detection Algorithm in Complex Environment Using Radar." In 2024 International Conference on Electronic Engineering and Information Systems (EEISS). IEEE, 2024. http://dx.doi.org/10.1109/eeiss62553.2024.00022.

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Venter, C. J., H. Grobler, and K. A. AlMalki. "Implementation of the CA-CFAR algorithm for pulsed-Doppler radar on a GPU architecture." In 2011 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT). IEEE, 2011. http://dx.doi.org/10.1109/aeect.2011.6132514.

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Medeiros, Diego, Romulo Costa, Dimas Irion Alves, Renato J. Machado, and Osamu Saotome. "High-frequency surface wave radar performance analysis for CA-CFAR algorithm in Weibull-distributed clutter." In Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2022, edited by Charles R. Bostater and Xavier Neyt. SPIE, 2022. http://dx.doi.org/10.1117/12.2636035.

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Medeiros, Diego da Silva de, Rômulo Fernandes da Costa, Naiara Tieme Mippo, Dimas Irion Alves, and Renato Machado. "High-Frequency Surface Wave Radar Performance Analysis for CA-CFAR Algorithm in K-distributed Clutter." In XLI Simpósio Brasileiro de Telecomunicações e Processamento de Sinais. Sociedade Brasileira de Telecomunicações, 2023. http://dx.doi.org/10.14209/sbrt.2023.1570923553.

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Galushko, Vladimir G. "Analysis of the CA CFAR algorithm as applied to detection of stationary Gaussian signals against a normal noise background." In 2016 9th International Kharkiv Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves (MSMW). IEEE, 2016. http://dx.doi.org/10.1109/msmw.2016.7538162.

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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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Esen Temel, Ozlem, and Mucahit Kani Uner. "A stop criteria for genetic algorithms applied to distributed CA-CFAR radar systems." In 2008 IEEE 16th Signal Processing, Communication and Applications Conference (SIU). IEEE, 2008. http://dx.doi.org/10.1109/siu.2008.4632734.

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