Digital
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To monitor the correct operation of the software, methods of visual assessment of output images, mathematical calculations, analysis of the completeness and integrity of data, as well as analysis of information about the operating parameters of the satellite and on-board imaging equipment are used. The approaches proposed in the work for monitoring the quality of target information make it possible to effectively supervise the correct functioning of ground-based means of processing satellite images, as well as to constantly maintain at the proper level and improve the quality of output information products. 2. Egoshkin N.A., Eremeev V.V., Moskvitin A.E., Ushenkin V.A. Obrabotka Informacii ot sovremennyh kosmicheskih sistem radiolokacionnogo nabludeniya Zemli – Moscow: FIZMATHLIT, 2019. 3. Egoshkin N.A., Eremeev V.V., Moskvitin A.E. Koordinatnaya privyazka izobrazheniy ot geostacionarnyh sputnikov po konturnym tochkam diska Zemli // Vestnik GRGTU. 2007. Issue 22. pp. 10-16. 4. Voronin A.A., Egoshkin N.A., Eremeev V.V., Moskatinyov I.V. Geometricheskaya obrabotka dannyh kosmicheskih sistem globalnogo nabludeniya Zemli // Vestnik GRGTU. 2009. Issue 27. pp. 12-17. 5. Egoshkin N.A., Moskvitin A.E. Povyshenie tochnosti korrekcii izobrazheniy na osnove fiultracii izmereniy uglovogo polozheniya skaniruyuschego zerkala // Vestnik GRGTU. 2010. Issue 33. pp. 7-11. 6. Egoshkin N.A. Dinamicheskie modeli geometricheskoi obrabotki izobrazheniy v sistemah distancionnogo zondirovaniya Zemli // Cifrovaya obrabotka signalov. 2017. Issue 1. Pp 3-7.
e-mail:foton@rsreu.ruRyazan State Radio Engineering University, Russia, Ryazan
2. Kuznecov A.E., Poshehonov V.I. Strukturno-parametricheskij sintez komponentov malogo kosmicheskogo apparata kartograficheskogo naznachenija (Structural and parametric synthesis of cartographic small spacecraft components) // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta (Vestnik of Ryazan State Radio Engineering University). 2019. Vol. 69. P. 185–192. 3. Kuznecov A.E. Sistemy i tehnologii obrabotki ajerokosmicheskoj informacii // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta (Vestnik of Ryazan State Radio Engineering University). 2012. Vol. 39-2. P. 7–14. 4. Vetrov A.A., Kuznecov A.E. Segmentacija oblachnyh ob’ektov na panhromaticheskih izobrazhenijah zemnoj poverhnosti (Cloud objects segmentation in Earth surface panchromatic images) // Cifrovaja obrabotka signalov (Digital Signal Processing). 2011. Vol 3. P. 32–36. 5. Eremeev V.V., Kochergin A.M., Kuznetcov A.E. Automatic detection of clouds in multispectral images subjected to interchannel parallax // IEEE International Geoscience and Remote Sensing Symposium. 2015. P. 4928–4930. 6. Li Z., Shen H., Cheng Q., Liu Y., You S., He Z. Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors // ISPRS J. of Photogram. and Rem. Sen. 2019. Vol. 150. P. 197–212. 7. Jiao L., Huo L., Hu C., Tang P. Refined UNet: UNet-Based Refinement Network for Cloud and Shadow Precise Segmentation // Remote Sensing. 2020. Vol. 12(12). 8. Mohajerani S., Saeedi P. Cloud-Net: An End-to-End Cloud Detection Algorithm for Landsat 8 Imagery // IEEE International Geoscience and Remote Sensing Symposium. 2019. P. 1029–1032. 9. Mohajerani S., Saeedi P. Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery via Filtered Jaccard Loss Function and Parametric Augmentation // IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021. Vol. 14. P. 4254–4266. 10. Eremeev V., Kuznetcov A., Kochergin A., Makarenkov A. Clouds segmentation on panchromatic high spatial resolution remote sensing images using convolutional neural networks // Proceedings of the SPIE. 2019. Vol. 11155. 11. Ronneberger O., Fischer P. and Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation // Medical Image Computing and Computer-Assisted Intervention. 2015. P. 234–241. 12. Ioffe S., Szegedy C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift // Proceedings of the 32nd International Conference on International Conference on Machine Learning. 2015. Vol. 37. P. 448–456.
2. Vasil'ev A.M. Algoritm szhatija izobrazhenij na osnove modificirovannogo diskretnogo kosinusnogo preobrazovanija (Algorithm of compression of images on the basis of modified discrete cosine transformations) // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta (Vestnik of Ryazan State Radio Engineering University). 2013. Vol. 46-2. P. 16–21. 3. Sahoolizadeh H., Suratgar A.A. Adaptive image compression using neural networks // 5th International Conference: Science of Electronics, Technologies of Information and Telecommunications. 2009. 4. Toderici G., Vincent D., Johnston N., Hwang S.J., Minnen D., Shor J., Covell M. Full resolution image compression with recurrent neural networks // IEEE Conference on Computer Vision and Pattern Recognition. 2017. 5. Islam K., Dang M., Lee S., Moon H. Image compression with recurrent neural networks and generalized divisive normalization // IEEE Conference on Computer Vision and Pattern Recognition. 2021. 6. Theis L., Shi W., Cunningham A., Huszar F. Lossy image compression with compressive autoencoders // International Conference on Learning Representations. 2017. 7. Mahoney M. Large Text Compression Benchmark. URL: http://www.mattmahoney.net/dc/text.html. 8. Goyal M., Tatwawadi K., Chandak S., Ochoa I. DeepZip: Lossless data compression using recurrent neural networks // Data Compression Conference. 2019. 9. Bellard F. Lossless Data Compression with Neural Networks. URL: https://bellard.org/nncp/nncp.pdf. 10. Ioffe S., Szegedy C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift // Proceedings of the 32nd International Conference on International Conference on Machine Learning. 2015. Vol. 37. P. 448–456. 11. Wang Z., Bovik A.C., Sheikh H.R., Simoncelli E.P. Image quality assessment: From error visibility to structural similarity // IEEE Transactions on Image Processing. 2004. Vol. 13(4). P. 600–612.
The well-known Kvasir-SEG digital image database was used to train and test deep machine learning algorithms. It contains images of 1072 polyps and offers both bounding rectangular frames and binary masks for segmentation. The resolution of the images in the specified dataset varies from 332x487 to 1920x1072 pixels. YOLOv6, YOLOR, YOLOv7, YOLOv7X, and YOLOv8 networks, previously trained on the basis of MS COCO images, were used as neural network architectures. Since the required volume of images in the Kvasir-SEG database used is relatively small, data augmentation (reproduction, enrichment) was used to increase it to the required size. It was performed taking into account the specifics of obtaining endoscopic images in real clinical practice. It is important to note that currently there is a noticeable lag in the size of the available databases of endoscopic images from the requirements of modern neural network algorithms and deep machine learning methods, which slows down the development of this important field for practical medicine. As a result of applying the studied neural network detection algorithms to a standard set of endoscopic images from the specified database, the highest values of the metrics AP@[0.25..0.75] - equal to 98.4; and AP@0.50 – equal to 98.6; for a neural network detector based on the YOLOv8 network were obtained. The obtained results can be used in the development of a video stream analysis system in an endoscopic system operating in real time during colonoscopic examinations.
2. Nikonov E.L., Aksenov V.A., Kashin S.V., Nekhaykova N.V. International experi-ence in colorectal cancer screening // Evidence-based gastroenterology. 2017. Vol. 6. No. 3. pp. 30-35. 3. Lee J. Resection of diminutive and small colorectal polyps: what is the optimal technique? Clinical endoscopy. 2016. vol. 49 (4). p. 355. 4. Lebedev A., Khryashchev V., Stefanidi A., Stepanova O., Kashin S., Kuvaev R. Convolutional neural network for early detection of gastric cancer by endoscopic video analysis. Proc. SPIE 11433. Twelfth International Conference on Machine Vision (ICMV 2019). 5. Batukhtin D.M., Peganova E.V., Mitrakova N.N. Analysis of narrow-spectral endo-scopic images on the inner surface of the esophagus // Bulletin of the Volga State Technological University. Series: radio engineering and infocommunication systems. 2014. No. 4 (23). pp. 45-57. 6. Kovalenko D.A., Gnatyuk V.S. Association of scenes in endoscopic videos // GraphiCon 2017: Processing and analysis of biomedical images. Perm. 2017. pp. 269-274. 7. Matyja M., Pasternak A., Szura M., Wysocki M., Pedziwiatr M., Rembiasz K. How to improve the adenoma detection rate in colorectal cancer screening? Clinical factors and techno-logical advancements. Archives of medical science: AMS. 2019. vol. 15 (2). p. 424. 8. Lebedev A.A., Khryashchev V.V., Srednyakova A.S., Kazina E.M. Development of an algorithm for detecting polyps on endoscopic images using convolutional neural networks // Digital signal processing. 2021. No. 2. pp. 55-60. 9. Lebedev A.A. Research of neural network algorithms for detecting objects on video images in medical systems of applied television: dissertation for the degree of candidate of tech-nical sciences. Yaroslavl. 2022. 10. Karkanis S.A., Iakovidis D.K., Maroulis D.E., Karras D.A., Tzivras M. Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans. on Infor-mation Technology in Biomedicine. 2003. vol. 7 (3). pp. 141-152. 11. Nikolenko S.I., Kadurin A.A., Arkhangelskaya E.O. Deep learning. St. Petersburg: St. Petersburg, 2018. 480 p. 12. Tajbakhsh N., Shin J.Y., Gurudu S., Hurst R., Kendall C. et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging. 2016. vol. 35(5). pp. 1299-1312. 13. Shin H.-C., Roth H., Gao M., Lu L., Xu Z. et al. Deep Convolutional Neural Net-works for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Trans. on Medical Imaging. 2016. vol. 35 (5). pp. 1285-1298. 14. Wang Y., Tavanapong W., Wong J., Oh J.H., De Groen P.C. Polyp-alert: Near real-time feedback during colonoscopy. Computer Methods and Programs in Biomedicine. 2015. vol. 120 (3). pp. 164-179. 15. Shin Y., Qadir H.A., Aabakken L., Bergsland J., Balasingham I. Automatic Colon Polyp Detection Using Region Based Deep CNN and Post Learning Approaches. IEEE Access. 2018. vol. 6. pp. 40950-40962. 16. Lee J., Jeong J., Song E., Ha C., Lee H. et al. Real-time detection of colon polyps during colonoscopy using deep learning: systematic validation with four independent datasets. Scientific Reports. 2020. vol. 10 (1). pp. 1-9. 17. Yamada M., Saito Y., Imaoka H., Saiko M., Yamada S. et al. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Scientific reports. 2019. vol. 9 (1). pp. 1-9. 18. Li C., Li L., Jiang H., Weng K., Geng Y. et al. YOLOv6: A single-stage object de-tection framework for industrial applications. arXiv preprint arXiv:2209.02976, 2022. 19. Wang C.-Y., Bochkovskiy A., Liao H.-Y. M. Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696, 2022. 20. Jocher G., Chaurasia A., Qiu J. YOLO by Ultralytics. https://github.com/ultralytics/ultralytics, 2023. Accessed: February 30. 2023. 21. Jha D., Smedsrud P., Riegler M., Halvorsen P., Lange T. et al. Kvasir-seg: A Seg-mented Polyp Dataset. Proc. of International Conference on Multimedia Modeling. 2020. pp. 451-462. 22. Pogorelov K., Randel K.R., Griwodz C., Eskeland S.L., Lange T. et al. Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection. Proc. of the 8th ACM on Multimedia Systems Conference. 2017. pp. 164-169. 23. Pogorelov K., Randel K.R., Lange T., Eskeland S.L., Griwodz C. et al. Nerthus: A bowel preparation quality video dataset. Proc. of the ACM on Multimedia Systems Conference. 2017. pp. 170-174. 24. Borgli H., Thambawita V., Smedsrud P.H., Hicks S., Jha D. et al. Hyperkvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Scientific Da-ta. 2020. vol. 7 (1). pp. 1-14. 25. Bernal J., Sanchez F.J., Fernandez-Esparrach G., Gil D., Rodriguez C., Vilarino F. Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Computerized Medical Imaging and Graphics. 2015. vol. 43. pp. 99-111. 26. Jha D., Ali S., Emanuelsen K., Hicks S.A., Thambawita V. et al. Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy. International Conference on Multimedia Modeling. 2021. pp. 218-229. 27. Jha D., Ali S., Tomar N.K., Johansen H.D., Johansen D. et al. Real-Time Polyp De-tection, Localization and Segmentation in Colonoscopy Using Deep Learning. Computer Vision and Pattern Recognition. 2021. pp. 40496 40510.
V. P. Kuznetsov, e-mail: kuznetsovaitu@yandex.ru The Ryazan State Radio Engineering University (RSREU), Russia, Ryazan Keywords:
The main problem of complex neural networks construction consists in selection of activation function from complex variable function multitude. This problem is solved on the basis of the complex variable functions which keeps a phase of input complex signal and transforms his absolute value into single circle. The activation functions in the form of «algebraic sigmoid» [3] and «SoftSign» [4] have such properties for input complex signal. Complex neural network is meant for complex signals processing, which use widely for telecommunication systems analysis. Such neural networks may be used for identification, pre-distortion, prediction, control. The results of experimental research of complex neural network for identification of nonlinear power amplifier are presented.
2. Osowski S. Neural networks for information processing. M.: Finances and statistics, 2004. 3. Haykin S. Neural networks: a comprehensive foundation, second edition: Prentice Hall, Inc., 1999. 4. Nikolenko S., Kadurin A., Arhangelskay E. Deep learning. SPb.: Piter, 2018. 5. Churakov E.P. Prediction of econometric time series. M.: Finances and statistics, 2008.
al.an.smirnov@yandex.ru, e-mail: A.V. Smirnovant.vas.smirnov@yandex.ruThe Ryazan State Radio Engineering University (RSREU), Russia, Ryazan Keywords: parametric optimization, two-stage optimization method, dynamic programming method, quasi-Newtonian methods, space-time image filtering.
The effectiveness of using the proposed two-stage optimization method for weighted space-time image filtering based on the use of average rank order statistics (median filtering) on a set of 25 test grayscale images with different density distributions of the brightness of elements has been studied. The dimensions of the test images were 360x240 pixels. Image filtering was carried out under the influence of “white” impulse noise with probabilities of 0.1, 015, 0.2, 0.25, 0.3. The filter suppression coefficient, defined as the ratio of the standard deviation of the error at the filter input to the standard deviation of the error at the filter output, was used as an efficiency criterion.. It is shown that the use of the proposed two-stage algorithm for parametric optimization of weighted space-time image filtering provides high efficiency of impulse noise suppression over the entire range of probabilities.
2. Pratt W.K. Digital image processing, 4th edition. Wiley, 2007. – 807 p, 3. Huang T.S. (ed.) Bystrye algoritmy v cifrovoj obrabotke izobrazhenij. Preobrazovaniya i mediannye fil'try. M: Radio i svyaz', 1984. – 221 p. 4. Yashin V.V., Kalinin G.A. Obrabotka izobrazhenij na yazyke Si dlya IBM PC: Algoritmy i programmy. M.: Mir, 1994. - 241 p. 5. Graboskwi S., Bienieck W. A two-pass median-like filter for impulse noise removal in multi-channel images. KOSYR, 2003. pp. 195-200. 6. Reikleitis G., Ravindran A., Ragsdel K. Optimizaciya v tekhnike. V 2-h knigah. M.: Mir. 1986. – 670 p. 7. Matorin A.V., Smirnov A.A. Algoritm parametricheskogo sinteza mnogoelementnyh tonkoprovolochnyh antenn i ustrojstv SVCH // Vestnik RGRTA. 1997. Issue. 2. pp. 85-92. 8. Matyurin A.V., Smirnov A.A. Ocenka effektivnosti dvuhetapnogo metoda nelokal'noj optimizacii na osnove resheniya testovyh zadach // Vestnik RGRTA. 1998. Issue 5. pp. 42-45. 9. Matorin A.V., Smirnov A.A. Rezul'taty razrabotki metodiki i uchebno-issledovatel'skogo programmnogo kompleksa parametricheskogo sinteza i statisticheskogo analiza ustrojstv SVCH // Vestnik RGRTA. 1998. Issue 4. pp. 71-82. 10. Smirnov A.A. Issledovanie i razrabotka algoritmov parametricheskogo sinteza ustrojstv SVCH v radiotekhnicheskih sistemah: Avtoreferat dis. kand. tekhn. nauk. Ryazan: RGRTA, 2000. – 24 p. 11. Matorin A.V., Smirnov A.A. Dvuhetapnyj chislennyj metod resheniya zadach sinteza mnogoelementnyh tonko-provolochnyh antenn i ustrojstv sverhvysokih chastot // Radiotekhnika i elektronika. 2001. Vol. 46. No. 6. 12. Matorin A.V., Smirnov A.A. Ocenka vliyaniya sovremennyh Si-kompilyatorov na proizvoditel'nost' vychislitel'nyh programm // Vestnik RGRTA. 2000. Issue 6.
The main objective of any hybrid precoding method is to maximize the spectral efficiency of the MIMO system, which implies that the RF and baseband precoding and combining matrices should be designed jointly. Unfortunately, optimizing all four matrices simultaneously is quite difficult. Therefore, various strategies are proposed for achieving a suboptimal solution while minimizing computational overhead. The common problem with the existed precoding approaches is that they are either complex and require several mathematical operations to improve the spectral efficiency, or that they require additional information about the radio channel, which is undesirable, or that they employ matrix decomposition, which is also not a simple operation and does not provide high performance in the cases where the number of RF chains is higher than the number of data streams. Our hybrid precoder/combiner is designed to use the additional RF chains to reduce the distance between the unconstrained precoding matrix and the products of the hybrid RF and baseband precoding/combining matrices, resulting in an approach to maximum spectral efficiency. Our proposed method does not require full channel knowledge or complex decomposition techniques, which reduces the complexity and amount of feedback information. The obtained results investigated that the spectral efficiency gap between the optimal fully-digital design and the existed schemes is reduced when the number of streams is less than the number of RF chains. 2. S. A. Busari, K. M. S. Huq, S. Mumtaz, L. Dai, and J. Rodriguez, “Millimeter-wave massive MIMO communication for future wireless systems: A survey,” IEEE Commun. Surveys Tuts., vol. 20, no. 2, pp. 836–869, 2nd Quart., 2017. 3. A. F. Molisch et al., “Hybrid beamforming for massive MIMO: A survey,” IEEE Commun. Mag., vol. 55, no. 9, pp. 134–141, Sep. 2017 4. O. E. Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi and R. W. Heath, "Spatially Sparse Precoding in Millimeter Wave MIMO Systems," in IEEE Transactions on Wireless Communications, vol. 13, no. 3, pp. 1499-1513, March 2014 5. L. Dai, X. Gao, J. Quan, S. Han and C. -L. I, "Near-optimal hybrid analog and digital precoding for downlink mmWave massive MIMO systems," 2015 IEEE International Conference on Communications (ICC), London, UK, 2015, pp. 1334-1339. 6. C. -E. Chen, "An Iterative Hybrid Transceiver Design Algorithm for Millimeter Wave MIMO Systems," in IEEE Wireless Communications Letters, vol. 4, no. 3, pp. 285-288, June 2015, 7. X. Liu et al., "Hybrid Precoding for Massive mmWave MIMO Systems," in IEEE Access, vol. 7, pp. 33577-33586, 2019. 8. D. Zhang, P. Pan, R. You and H. Wang, "SVD-Based Low-Complexity Hybrid Precoding for Millimeter-Wave MIMO Systems," in IEEE Communications Letters, vol. 22, no. 10, pp. 2176-2179, Oct. 2018, 9. S. Wang, L. Li, R. Ruby, and P. Li, “A general hybrid precoding scheme for millimeter wave massive MIMO systems,” Wireless Netw., vol. 26, pp. 1331–1345, Mar. 2020. 10. S. Wang, M. He, J. Wang, R. Ran, H. Ji and V. C. M. Leung, "A Family of Hybrid Precoding Schemes for Millimeter-Wave Massive MIMO Systems," in IEEE Systems Journal, vol. 16, no. 3, pp. 4881-4891, Sept. 2022 11. S. Wang et al., "A Joint Hybrid Precoding/Combining Scheme Based on Equivalent Channel for Massive MIMO Systems," in IEEE Journal on Selected Areas in Communications, vol. 40, no. 10, pp. 2882-2893, Oct. 2022, 12. H. Lou, M. Ghosh, P. Xia, and R. Olesen, “A comparison of implicit and explicit channel feedback methods for MU-MIMO WLAN systems,” in Proc. IEEE Pers. Indoor Mobile Radio Commun. (PIMRC), Sep. 2013, pp. 419–424.
The need for sustained detection of the latter over the entire distance is significantly complicated in view of their mass character, diversity and relatively low cost, in view of which they have recently become very widespread in various spheres of society. The power of the radar signal reflected from «distributed» local objects has a significant impact on the detection characteristics of an aerial target. In the conditions of the use of technologies to reduce their radar visibility or the use of small-sized unmanned aerial vehicles, the total differential effective scattering area becomes commensurate, and in some cases predominant over the level of the signal reflected from the target. At the heart of mass-produced radars, a system time control with a known control law is mainly used. The widespread introduction of multipath radars has led to the fact that the resulting viewing area is formed based on the results of processing several beams, each of which has a separate receiving channel and its own geometric features. When reflections from «distributed» local objects such as rain, sea surface, etc. prevail in the channel, detection becomes problematic. This is due to the fact that reflections significantly overload the receiving path of the radar station. A feature of modern radar stations is their multi-channel nature. One of the main ways to reduce the power level of reflected signals is the use of individual circuits with gain control of the receiving path in each channel according to a known law. At the same time, there are no recommendations on the procedure for preliminary evaluation of the parameters of the gain control law of the receiving path, taking into account the geometry of the location of the beams, the tactical and technical characteristics of the radar station and the specifics of its application. In the course of the work, such an approach is proposed and its correctness is shown. 2. Bakulev, P.A., Stepin V.M. Methods and devices of selection of moving targets. M.: Radio and communications, 1986. 288 p. 3. Bakulev, P.A. Radar systems. 2nd Ed., reprint. and additional Ser. Textbook for universities. M.: Radio Engineering, 2007. 375 p 4. Skolnik, M. Handbook of radar. Book 1/ Edited by M. Skolnik. 3rd edition. Translated from English under the general editorship of B.C. Verba. In 2 books. Moscow: Sov. radio, 2014. 528 p. 5. Skolnik, M. Handbook of radar. Volume 3/ Edited by M. Skolnik. Translated from English under the general editorship of K.N. Trofimov. In 4 volumes. M.: Technosphere, 1979. 672 p 6. Fitasov, E.S. Spatio-temporal signal processing in small-sized mobile radar systems for detecting low-flying aerial objects: dis. Doctor of Technical Sciences. Nizhny Novgorod State University named after N.I.Lobachevsky, 2018. 378 p. 7. Stepanov, M.A. Vliyanie fluktuacij skorosti vetra v turbulentnoj atmosfere na harakteristiki obnaruzheniya RLS s SDC: dis. kand. fiz.-mat.nauk. Krasnoyarsk, 2009. 187 s. 8. Mezhobzornaya kompensaciya diskretnyh meshayushchih otrazhenij s formirovaniem karty pomekh i nakopleniem reshenij/ A.S. Solonar, S.A. Gorshkov, P.A. Hmarskij, V.A. Vashkevich // Doklady BGUIR. - 2015. - ¹ 4 (90). - S. 74 - 79. 9. Lyashenko, V.A. Radar station 19G6 (ST-68U). V.A. Lyashenko, L.Y. Boskutis, G.I. Tylets. – Textbook of the Ministry of Defense of the USSR. M.: Military Publishing House. – 1992. 10. Sumanta, Pal. A Novel Scheme of Digital Instantaneous Automatic Gain Control (DIAGC) for Pulse Radars. Pal. Sumanta, Shanmugam. Nirmala,Kumar. Mohit, P. Radhakrishna. Presented at International Symposium of India 2011. 11. Mahafza, B. R., Radar Signal Analysis and Processing Using MATLAB, Taylor & Francis Group, Boca Raton, 2009.
Keywords: empirical mode decomposition, modified empirical mode decomposition, informative components, information criteria, classification of components.AbstractThe present paper discusses the issues of adaptive empirical mode decomposition. We represent the modified empirical mode decomposition method that allows us to eliminate decomposition redundancy. The properties of empirical modes are studied. We represent the results of comparing the empirical mode decomposition, Fourier analysis, and wavelet analysis. Some experimental data are shown that deal with studying harmonic signals. The classification of empirical modes is introduced and we suggest the technique of selecting informative components based on information criteria. The techniques expounded in the paper, namely empirical mode decomposition and some aspects of the wavelet analysis are mainly applied to processing non-stationary signals. Non-stationary signals are encountered in such fields as, for example, speech technologies, hydroacoustics, processing vibrational signals, and geophysical and biomedical research. Further improvement of the accuracy and reliability of such analysis requires us to employ certain approaches that are adaptive to the signal under study. Empirical mode decomposition, its modifications, and the wavelet transform meet these requirements and therefore can be widely used for dealing with various types of non-stationary signals. The paper consists of several sections and below you can find the brief contents of each one:
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Keywords: meteorological radar, airfield zone, method and algorithms, specific velocity of turbulent energy dissipation, horizontal wind shear, vertical wind shear, hazard gradation, standard deviation of estimates of wind shear and turbulence.AbstractThis article presents the developed method and algorithms for estimating wind shear and turbulence used in the ground-based meteorological radar complex of the aerodrome zone MRLC AZ. Wind shear refers to horizontal wind shear by 600 m and vertical wind shear by 30 m, turbulence refers to the parameter of the eddy dissipation rate of turbulent energy EDR, these terms comply with modern requirements of ICAO, WMO, RF Roshydromet. The method for estimating wind shear and turbulence consists of 3 stages, during which the measured values of the radial velocity, the width of the radial velocity spectrum become the initial data for calculating wind shear and turbulence, followed by classification by hazard degree. 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In signal processing modelling several approaches to delay a signal can be adopted. First, increasing of sampling frequency allows to control signal delay in smaller steps. Main downside of this approach is its high computational load. Second technique is based on Farrow filters. Drawbacks are nonlinear distortion and interpolation error increase with frequency. Finally, signal delay can be implemented based of finite impulse response (FIR) filters. The problem is to design a filter that has a flat amplitude response and linear phase response with required slope. To obtain filter coefficients least squares criterion is used. Sum of squares of differences of desired and actual frequency responses at discrete points in a required frequency band is used as a loss function. Based on this criterion, equations for filter coefficients calculation are presented. Article also contains a numerical example of filter design and an analysis of the frequency response deviation dependence on filter order. Frequency response deviation is defined as a maximum difference of actual and desired filter response (amplitude response and group delay). It is shown that solving linear system of equations using LU matrix decomposition with complete pivoting gives smaller frequency response deviation than direct matrix inversion. 2. Senchenko A.A., LCMV algorithm under spoofing conditions, // Prospects of fundamental sciences development, volume 7, p. 110-113, Tomsk State University of Control Systems and Radioelectronics, 2021. 3. V. Valimaki, T.I. Laakso. Fractional Delay Filters-Design and Applications.2001. 10.1007/978-1-4615-1229-5_20. 4. Signal resampling based on polynomial interpolation. Farrow filter (n.d.) DSPLIB. Retrieved November 5, 2023, from https://ru.dsplib.org/content/resampling_lagrange/resampling_lagrange.html 5. M.I. Spazhakin, V.D. Repnikov, A.B. Tokarev, Resampling by using Farrow filter – evaluation of distortion // Voronezh State Technical University, 2013 6. Algorithms for the Constrained Design of Digital Filters with Arbitrary Magnitude and Phase Responses, Mathias Lang, 1999. 7. The GNU Multiple Precision Arithmetic Library: https://gmplib.org/
, e-mail: dvoryankov.d.a@mail.ruAndrosov V.V.^{2},Volchenkov V.A.^{1}, e-mail: volchenkov.v.a@tor.rsreu.ruVityazev S.V.^{1}, e-mail: vityazev.s.v@tor.rsreu.ru^{1}The Ryazan State Radio Engineering University (RSREU), Russia, Ryazan^{2}Ryazan State Instrument-making Enterprise Keywords: radar systems, interpolation, AR model, radar image, filling gaps in radar signal.AbstractThe autoregressive analysis algorithm is described in relation to the signal shape restoration, which was distorted due to the helicopter rotor blades influence. Alternative restoring methods are also provided in this paper. The AR analysis method is compared with other recovery methods by modeling the reflected radio signals processing. The ability of AR analysis to recover both simple and more complex distorted signals is shown. The dependences of the recovery quality on the speed and bearing angle of the radar are given, and the computational costs are estimated. The computational costs and algorithm ability to work in real time are also evaluated. References1. Dvoryankov D.A., Androsov V.V., Vityazev S.V., Vityazev V.V. Radar system imaging in conditions of periodic shading. GraphiCon 2022 32nd International Conference on Computer Graphics and Vision, pp. 774-783. 2. Box G., Jenkins G. Time series analysis: Forecasting and control. San Francisco: Holden-Day, 1970. 3. S. Orfanidis, Optimum Signal Processing, 2nd Ed. Macmillan, 1988, Chapter 5. 4. J.P. Burg. Maximum entropy spectral analysis. Proc. 37th Meet.Soc. Explorational Geophys., Oklahoma City, OK, 1975. 5. V.V. Voronin, V.I. Marchuk, S.P. Petrosov, I. Svirin, S. Agaian and K. Egiazarian. Image restoration using 2D autoregressive texture model and structure curve construction. Proc. SPIE 9497 Mobile Multimedia/Image Processing Security and Applications, 2015. EDN: UFDCOF 6. Koen Vos. A Fast Implementation of Burg's Method, 2013. |