يعرض 1 - 10 نتائج من 85 نتيجة بحث عن '"Quoc-Khanh Nguyen"', وقت الاستعلام: 1.55s تنقيح النتائج
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    المصدر: Journal of the Russian Universities. Radioelectronics; Том 26, № 2 (2023); 101-119 ; Известия высших учебных заведений России. Радиоэлектроника; Том 26, № 2 (2023); 101-119 ; 2658-4794 ; 1993-8985

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    العلاقة: https://re.eltech.ru/jour/article/view/740/681Test; Боронахин А. М., Лукьянов Д. П., Филатов Ю. В. Оптические и микромеханические инерциальные приборы. СПб.: Элмор, 2008. 400 с.; Матвеев В. В., Распопов В. Я. Основы построения бесплатформенных инерциальных навигационных систем. СПб.: РНЦ РФ ОАО «Концерн «ЦНИИ "Электроприбор"», 2009. 208 с.; Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement / Sh. Han, Zh. Meng, O. Omisore, T. Akinyemi, Y. Yan // A Review. Micromachines. 2020. Vol. 11, iss. 11. P. 1021. doi:10.3390/mi11111021; Huang L. Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter // Sensors. 2015. Vol. 15, iss. 10. P. 25277–25286. doi:10.3390/s151025277; An improved Sage Husa adaptive robust Kalman filter for de-noising the MEMS IMU drift signal / M. Narasimhappa, A. D. Mahindrakar, V. C. Guizilini, M. H. Terra, S. L. Sabat // Proc. of the IEEE Conf. on Indian Control Conf. (ICC). Kanpur, India, 04–06 January 2018. IEEE, 2018. P. 229–234. doi:10.1109/INDIANCC.2018.8307983; Duan D. Study on modeling and filtering of random drift on FOG // Proc. of SPIE. 2011. Vol. 8191. P. 81912G. doi:10.1117/12.90323; ARMA model based adaptive unscented fading Kalman filter for reducing drift of fiber optic gyroscope / M. Narasimhappa, J. Nayak, M. H. Terra, S. L. Sabat // Sensor and Actuator A. 2016. Vol. 251. P. 42–51. doi:10.1016/j.sna.2016.09.036; Sage A. P., Husa W. Adaptive Filtering with Unknown Prior Statistics // Proc. of the Joint Automatic Control Conf., Washington, DC, USA, 22–24 June 1969. P. 760–769.; FOG random drift signal denoising based on the improved AR model and modified Sage-Husa adaptive Kalman filter / J. Sun, X. Xu, Y. Liu, T. Zhang, Y. Li // Sensors. 2016. Vol. 16, № 7. P. 1–19. doi:10.3390/s16071073; Julier S. J., Uhlmann J. K. Unscented filtering and nonlinear estimation // Proc. of the IEEE, 2004. Vol. 92, № 3. P. 401–422. doi:10.1109/JPROC.2003.823141; Viswanathan M. Wireless Communication Systems in Matlab. 2nd Ed. Independently published, 2020. 382 p.; Wang P., Li G., Gao Ya. A compensation method for gyroscope random drift based on unscented Kalman filter and support vector regression optimized by adaptive beetle antennae search algorithm // Applied Intelligence. 2022. Vol. 53. P. 4350–4365. doi:10.1007/s10489-022-03734-7; Yang Yu., Gao W. Comparison of Adaptive Factors in Kalman Filters on Navigation Results // The J. of Navigation. 2005. Vol. 58, iss. 3. P. 471–478. doi:10.1017/S0373463305003292; Yang Y., Xu T. An adaptive Kalman filter based on Sage windowing weights and variance components // The J. of Navigation. 2003. Vol. 56, iss. 2. P. 231–240. doi:10.1017/S0373463303002248; Sage windowing and random weighting adaptive filtering method for kinematic model error / Sh. Gao, W. Wei, Yo. Zhong, A. Subic // IEEE transactions on aerospace and electronic systems. 2015. Vol. 51, № 2. P. 1488–1500. doi:10.1109/TAES.2015.130656; Gao Sh., Hu G., Zhong Yo. Windowing and random weighting-based adaptive unscented Kalman filter // Int. J. Adapt. Control Signal Process. 2015. Vol. 29, iss. 2. P. 201–223. doi:10.1002/acs.2467; ARIMA models for time series forecasting. URL: https://people.duke.edu/~rnau/411arim3.htmTest (дата обращения 10.04.2022); Merwe R. Van der, Wan E. A. The square-root unscented Kalman filter for state and parameterestimation // 2001 IEEE Intern. Conf. on Acoustics, Speech and Signal Processing. Proc. (Cat. No.01CH37221). Salt Lake City, USA, 07–11 May 2001. IEEE, 2001. Vol. 6. P. 3461–3464 doi:10.1109/ICASSP.2001.940586; Radar Target Tracking for Unmanned Surface Vehicle Based on Square Root Sage–Husa Adaptive Robust Kalman Filter / Shuanghu Qiao, Yunsheng Fan, Guofeng Wang, Dongdong Mu, Zhiping He // Sensors. 2022. Vol. 22, iss. 8. P. 2924. doi:10.3390/s22082924; Moving Average Proofs. URL: https://realstatistics.com/time-series-analysis/moving-averageprocesses/moving-average-proofsTest/ (дата обращения 16.03.2022); Unscented Kalman filter: limitation and combination / L. Chang, B. Hu, A. Li, F. Qin // IET Signal Process. 2013. Vol. 7, iss. 3. P. 167–176. doi:10.1049/iet-spr.2012.0330; Datasheet SINS-2M. Electrooptika. URL: http://www.electrooptika.ru/index.php/bins/bins-mezhvidovogo-primeneniyaTest (дата обращения 15.02.2022); https://re.eltech.ru/jour/article/view/740Test

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    المصدر: European Journal of Psychology & Educational Research; Mar2024, Vol. 7 Issue 1, p45-53, 9p

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    المصدر: Dong Thap University Journal of Science; Vol. 11 No. 4 (2022): Social Sciences and Humanities Issue (Vietnamese); 51-61 ; Tạp chí Khoa học Đại học Đồng Tháp; Tập 11 Số 4 (2022): Chuyên san Khoa học Xã hội và Nhân văn (Tiếng Việt); 51-61 ; 2815-567X ; 0866-7675

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