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“Digital
Signal Processing” No. 3-2024 |
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Features of signal processing in radar classification of dangerous meteorological phenomena of cumulonimbus clouds
Vasiliev O.V., e-mail: vas_ov@mail.ru
Galaeva K.I., e-mail: ks.galaeva@mail.ru
Shepet’ I.P., e-mail: ship.1963@mail.ru
Nikonenko A.V., e-mail: nikon-74@mail.ru
The Moscow State Technical University of Civil Aviation (MSTU CA), Russian Federation, Moscow
Keywords: weather radar, classification by hazard level, dangerous meteorological phenomena of cumulonimbus clouds, signal processing, problem of distinguishing statistical hypotheses.
Abstract
The existing classification criteria for such dangerous meteorological phenomena as rainfalls, thunderstorms, hail in weather radars have obvious shortcomings, since they are formed separately for each phenomenon, based only on data on the altitude distribution of atmospheric radar reflectivity, air temperature. The paper shows how the criteria for classifying dangerous meteorological phenomena should be optimized in the following areas: using information on the altitude distribution of maximum values of not only reflectivity, but also atmospheric turbulence in the classification criteria; the classification should be built in accordance with a single selected criterion for distinguishing statistical hypotheses. The article presents the features of signal processing in the radar classification of dangerous meteorological phenomena. It is shown that radar classification of dangerous meteorological phenomena of cumulonimbus clouds is solved in the form of a problem of distinguishing statistical hypotheses under conditions of a priori uncertainty, which is eliminated experimentally by obtaining and processing training samples, calculating estimates of the probabilistic description of features and using them in the algorithm as true values.
Algorithm training is formed as a result of sequential execution of operations of probabilistic description of features and formation of thresholds. After training, decision thresholds are fixed for a limited set of features, the values of which are sent to the decision device (classifier) at the classification stage.
Thus, a single criterion for classification of the dangerous meteorological phenomena of cumulonimbus clouds: rainfall - thunderstorm - hail, based on the statistical theory of distinguishing hypotheses based on the information obtained from the output of the aerodrome near-field weather radar complex "Monocle", is proposed. The recognition task is associated with the sequence of preliminary signal processing, their digitization, primary, secondary and tertiary processing of information and its display.
The greatest difficulties are associated with the stage of training on a representative sample both in time and in the volume of processed information. The assumptions made in the process of eliminating a priori uncertainty are extremely important. Obviously, there is spatial variability in the initial data for constructing algorithms. This is due to the peculiarities of the climatic zone in which the radar weather sensor is installed. Thus, training and adjustment of classification algorithms should be carried out directly at the installation site. In addition, it is necessary to eliminate the existing temporal variability of atmospheric parameters. Thus, the learning process must proceed in parallel with the classification process.
References
1. Guidelines for the production of meteorological radar observations on DMRL-C on the Roshydromet network - St. Petersburg, 2013 - 164 p.
2. Guidelines for the use of information from the Doppler meteorological radar DMRL-C in synoptic practice - Moscow, 2019 - 129 p.
3. Guide to the production of observations and the use of information from non-automated radars MRL-1, MRL-2, MRL-5. RD 52.04.320-91. St. Petersburg. - 1993.
4. F. J. Yanovsky, “Evolution and Prospects of Airborne Weather Radar Functionality and Technology”, 18th International Conference on Applied Electromagnetics and Communications, 2005. DOI:10.1109/ICECOM.2005.204987.
5. V.N. Bringi, and V. Chandrasekar, “Polarimetric Doppler Weather Radar,” Cambridge University Press, 2004.
6. Kessler, E., Lee, J.T., Wilk, K.E. Associations between aircraft measurements of turbulence and weather radar measurements. Bulletin of American Meteorological Society, Vol.46, No 8, 1965, pp. 433-447.
7. A.Lupidi et al. Polarimetry applied to avionic weather radar: Improvement on meteorological phenomena detection and classification. Conference: Digital Communications - Enhanced Surveillance of Aircraft and Vehicles (TIWDC/ESAV), 2011 Tyrrhenian International Workshop on. pp.73-77.
8. Vasiliev O. The Design and Operation Features of the Near-airfield Zone Weather Radar Complex “Monocle”/ Vasiliev O., Bolelov E., Galaeva K., Gevak N., Zyabkin S., Kolesnikov E., Peshko A., Sinitsyn I.// 2021 XVIII Technical Scientific Conference on Aviation Dedicated to the Memory of N.E. Zhukovsky (TSCZh)
9. Vasiliev O.V., Korotkov S.S., Galaeva K.I., Boyarenko E.S. Decision criteria for the classification of meteorological phenomena in the weather radar complex of the near- airfield zone. Civil aviation high technologies. 2023. vol. 26. no.2, pp. 40-60. https://doi.org/10.26467/2079-0619-2023-26-2-49-60
10. Bolelov E.A., Vasiliev O.V., Zyabkin S.A., Chirov D.S. Development of a fuzzy-logical classifier of the phase state of hydrometeors for an algorithm for classifying zones of probable icing of aircraft in X-band weather radars // T-Comm: Telecommunications and transport. 2023. Vol. 17. No. 10. pp. 4-12.
11. D. S. Chirov, E. A. Bolelov, S. A. Zyabkin and O. V. Vasiliev, "Fuzzy-logical Classifier of the Phase State of Hydrometeors in X-band Weather Radars," 2023 Wave Electronics and its Application in Information and Telecommunication Systems (WECONF), St. Petersburg, Russian Federation, 2023, pp. 1-4, doi: 10.1109/WECONF57201.2023.10148003.
12. Vasiliev O.V., Zyabkin S.A., Nikonenko A.V., Chirov D.S. Functionally-oriented model for the formation of a meteorological product in the x-band from supercooled liquid hydrometeors with full polarization reception // Digital signal processing, No. 1, 2023. pp. 54-61.
13. Doviak, R., Zrnic D. Doppler radars and meteorological observations. Monograph / Ed. A.A. Chernikov. - L.: Gidrometeoizdat, 1988 - 512 p.
14. Duda R., Hart P. Pattern recognition and scene analysis. Moscow: Mir, 1976.
15. Repin V.G., Tartakovsky G.P. Statistical synthesis under a priori uncertainty and adaptation of information systems. Moscow: 1977.
16. Tikhonov V.I., Bakaev Yu.N. Statistical theory of radio engineering devices // Moscow: Publ. VVIA im. prof. N.E. Zhukovsky. - 1978.
17. Bekryaev V.I. Fundamentals of the theory of experiment. Study guide. - St. Petersburg: Publ. RSMU, 2001 – 266 p.
18. Degtyarev A.S., Drabenko V.A., Drabenko V.A. Statistical methods of processing meteorological information. Textbook. - St. Petersburg: OOO "Andreevsky Publishing House", 2015 - 225 p.
19. Approximation based on typical distributions [Electronic resource] / Approximation of the distribution law of experimental data URL: https://poznayka.org/s97706t1.html (Accessed: 12.02.2024)
20. Kremer N.Sh. Probability Theory and Mathematical Statistics. 2nd ed. 2004
21. Ayvazyan S.A., Enyukov I.S., Meshalkin L.D. Applied Statistics: Fundamentals of Modeling and Primary Data Processing. Reference publication. M.: Finance and Statistics, 1983 – 471 p.
Digital processing of electronic plantograms using artificial intelligence technolo-gies as a stage of automation of plantographic research
V.V. Mikhailishin, e-mail: mikhailishin_v@mail.ru
L.M. Smirnova
S.O. Cherkashin
Albrecht Federal Scientific and Educational Centre of Medial and Social Expertise and Rehabilitation, 50 Bestuzhevskaya Street, 195067 St. Petersburg, Russian Federation
Saint Petersburg State Electrotechnical University, 5 Professora Popova Street, 197376 St. Petersburg, Russian Federation
Keywords: computer plantography, artificial intelligence, image processing, foot diagnostics.
Abstract
The article considers an algorithm for digital preprocessing of computer-generated imagery using artificial intelligence technologies. Computer plantography is a study that results in obtaining a digital image of the plantar surface of the feet.
Image preprocessing is a necessary step in data analysis to unify images, which will facilitate further analysis of images. The article defines the main criteria that characterize the high-quality preprocessing of images of this type. Based on these criteria, an algorithm for preprocessing images was developed.
This algorithm included several stages of image processing, including changing brightness and contrast, cropping the edges of the image, highlighting the main objects in the image, classifying objects using artificial intelligence technologies, placing objects on separate images and centering objects in these images. This algorithm has shown high efficiency in processing the initial database of 4000 images of computer plantography.
Thus, the developed algorithm opens up new possibilities for analyzing the data of this study, allowing you to create databases from unified images and simplifying the work of healthcare professionals in analyzing such images. Thus, the results obtained can be applied for scientific purposes, practical medical activities, in the selection and manufacture of orthopedic products for the foot.
References
1. Vedenina A. S. et al. Screening of functional disorders of the feet using computer plantography and podometry //Medical equipment. – 2014. – No. 2. – p. 284.
2. GOST R 52623.1-2008 Technologies for performing simple medical services of functional examination: national standard of the Russian Federation: date of introduction 2009-09-01 / Federal Agency for Technical Regulation and Metrology. – Official edition. – Moscow: Standartinform, 2009 – 32 p. - URL: https://internet-law.ru/gosts/gost/47892 / (date of access: 03/18/2024).
3. De Raad K. B. et al. The effect of preprocessing on convolutional neural networks for medical image segmentation //2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). – IEEE, 2021. – Ñ. 655-658.
4. Albahra S. et al. Artificial intelligence and machine learning overview in pathology & laboratory medicine: A general review of data preprocessing and basic supervised concepts //Seminars in Diagnostic Pathology. – WB Saunders, 2023. – Ò. 40. – ¹. 2. – Ñ. 71-87.
5. Kumar B. K. S. Image denoising based on gaussian/bilateral filter and its method noise thresholding //Signal Image Video Process. – 2013. – Ò. 7. – ¹. 6. – Ñ. 1159-1172.
6. Uchida S. Image processing and recognition for biological images //Development, growth & differentiation. – 2013. – Ò. 55. – ¹. 4. – Ñ. 523-549.
7. He L. et al. Fast connected-component labeling //Pattern recognition. – 2009. – Ò. 42. – ¹. 9. – Ñ. 1977-1987.
8. Bindita Chaudhuri B. C. et al. Multilabel remote sensing image retrieval using a semisupervised graph-theoretic method. – 2018.
9. Cawley G. C., Talbot N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation //The Journal of Machine Learning Research. – 2010. – Ò. 11. – Ñ. 2079-2107.
Doppler centroid estimation in primary processing of spaceborne stripmap SAR raw data by amplitude analysis
Ushenkin V.A., e-mail: foton@rsreu.ru
Keywords: Doppler centroid, Doppler ambiguity, synthetic aperture radar (SAR), raw SAR data, satellite, primary processing.
Abstract
The paper proposes a new algorithm for high-precision Doppler centroid estimation from spaceborne StripMap synthetic aperture radar (SAR) data, based on amplitude analysis of images, obtained from raw data, divided into two non-overlapping subapertures (two looks). The estimation includes two aspects, which are the baseband Doppler centroid evaluation (within the pulse repeat rate) and the Doppler ambiguity evaluation. High accuracy of the baseband Doppler centroid estimates is achieved by analyzing the amplitude azimuthal spectrum not of the raw data, but of the images obtained from it, in which the impulse responses cease to be spatially widespread and become compact. The amplitude azimuthal spectrum analysis is performed independently in a set of non-overlapping small fragments that form one large fragment of the image, with subsequent weighted averaging of the obtained estimates within the large fragment. Higher accuracy of the Doppler ambiguity estimates is achieved due to a more precise mathematical model for recalculating range mismatch of the images obtained from two looks into Doppler corrections, which takes into account previously found corrections to the baseband Doppler centroid and the azimuthal SAR antenna pattern. The paper presents the results of the proposed algorithm obtained by processing the COSMO-SkyMed satellite data. It is shown that due to the higher accuracy of the proposed algorithm in most cases it is possible to reduce the number of iterations of raw data processing and evaluation of its parameters by 1–2 to obtain accurate estimates of the Doppler centroid. In addition, unlike the known amplitude algorithms, the proposed algorithm stably allows estimating the Doppler centroid when the baseband Doppler centroid error is close to the half of the pulse repeat rate.
References
1. Verba V.S., Neronskij L.B., Osipov I.G., Turuk V.Je. Radiolokacionnye sistemy zemleobzora kosmicheskogo bazirovanija / edited by V.S. Verba. Moscow: Radiotehnika, 2010. 680 p.
2. Egoshkin N.A., Eremeev V.V., Moskvitin A.Je., Ushenkin V.A. Obrabotka informacii ot sovremennyh kosmicheskih sistem radiolokacionnogo nabljudenija Zemli. Moscow: FIZMATLIT, 2019. 320 p.
3. Eremeev V.V., Egoshkin N.A., Makarenkov A.A., Moskvitin A.E., Ushenkin V.A. Problemnye voprosy obrabotki dannyh ot kosmicheskih sistem giperspektral'noj i radiolokacionnoj semki Zemli (The problems in processing of data from spaceborne hyperspectral and SAR systems of Earth survey) // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta (Vestnik of Ryazan State Radio Engineering University). 2017. Vol. 60. Pp. 54–64. DOI: 10.21667/1995-4565-2017-60-2-54-64. EDN: YSRVDV.
4. Poshekhonov V.I., Kuznecov A.E., Egin M.M. Ocenka tochnosti approksimacii strogoj modeli kosmicheskoj s#jomki racional'nymi polinomami (Estimation of approximation accuracy for strict model of space imaging by rational polynomials) // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta (Vestnik of Ryazan State Radio Engineering University), 2023. Vol. 83. Pp. 95–101. DOI: 10.21667/1995-4565-2023-83-95-101. EDN: HHESHN.
5. Kuznecov A.E., Poshehonov V.I. Ctrukturno-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. Pp. 185–192. DOI: 10.21667/1995-4565-2019-69-185-192. EDN: THYZZQ.
6. Kuznecov A.E., Poshehonov V.I. Metodika geometricheskoj kalibrovki kompleksa mnogozonal'noj skanernoj s#emki KA «Meteor-M» // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta (Vestnik of Ryazan State Radio Engineering University), 2010. Vol. 33. Pp. 12–18. EDN: THYZZQ.
7. Bamler R. Doppler frequency estimation and the Cramer-Rao bound // IEEE Transactions on Geoscience and Remote Sensing. 1991. Vol. 29 (3). Pp. 385–390.
8. Cumming I.G., Wong F.H. Digital processing of synthetic aperture radar data: algorithms and implementation. Norwood, MA: Artech house, 2005. 628 p.
9. Madsen S.N. Estimating the Doppler centroid of SAR data // IEEE Transactions on Aerospace and Electronic Sysntems. 1989. Vol. 25(2). Pp. 134–140.
10. Dragosevic M. On accuracy of attitude estimation and Doppler tracking // Proceedings of the CEOS SAR Workshop, Toulouse, 26–29 October 1999. ESA-SP. 2000. Vol. 450. Pp. 127–130.
11. Bamler R., Runge H. PRF-ambiguity resolving by wavelength divercity // IEEE Transactions on Geoscience and Remote Sensing. 1991. Vol. 29 (6). Pp. 997–1003.
12. Wong F.H., Cumming I.G. A combined SAR Doppler centroid estimation scheme based upon signal phase // IEEE Transactions on Geoscience and Remote Sensing. 1996. Vol. 34 (3). Pp. 696–707.
13. Cumming I.G., Kavanagh P.F., Ito M.R. Resolving the Doppler ambiguity for spaceborne synthetic aperture radar // Proceedings of the IGARSS’86. 1986. Pp. 1639–1643.
Interferometric DSM Glo 30 and stereophotogrammetric DSM AW3D30 fusion
Ushenkin V.A., Laryukov S.A., e-mail: foton@rsreu.ru
Keywords: digital surface model, digital terrain model, interferometry, stereophotogrammetry, digital maps.
Abstract
The work analyzes interferometric and stereophotogrammetric digital surface models GLO-30 and AW3D30. It is noted that GLO-30 has on average higher vertical accuracy, but in urban areas the heights of artificial objects are significantly underestimated. The algorithm of these digital surface models’ fusion is proposed. It allows increasing the accuracy of the interferometric digital surface model in urban areas using stereophotogrammetric data. OpenStreetMap digital maps are used to identify urban areas. The positions of artificial objects are extracted from digital maps. These objects are buildings, storage tanks, reservoir, bridges, overpasses, road junctions, cooling, water and communication towers, masts, pillars, chimneys, lighthouses, drilling platforms, embankments, etc. The positions of artificial objects are rasterized forming binary mask. Formed binary mask is additionally clarified on the base of digital surface model analysis. Final fusion is performed by filling areas in GLO-30 highlighted in binary mask with AW3D30 data corrected to seamless docking. The results of the proposed algorithm are presented.
References
1. Gomozov O.A., Kuznecov A.E., Pobaruev V.I., Poshehonov V.I. Programmno-matematicheskoe obespechenie sistemy obrabotki kosmicheskih stereoizobrazhenij // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta (Vestnik of Ryazan State Radio Engineering University). 2009. Vol. 27. Pp. 17–22. EDN: JWTZBX.
2. Apeh O., Uzodinma V., Ebinne E., Moka E., Onah E. Accuracy Assessment of Alos W3d30, Aster Gdem and Srtm30 Dem: A Case Study of Nigeria, West Africa // Journal of Geographic Information System. 2019. Vol. 11. Pp. 111–123.
3. Egoshkin N.A., Ushenkin V.A. Sovmeshhenie vysokodetal'nyh izobrazhenij s ispol'zovaniem opornoj cifrovoj modeli rel'efa pri interferometricheskoj obrabotke radiolokacionnoj informacii (DEM-assisted high resolution image coregistration for InSAR processing) // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta (Vestnik of Ryazan State Radio Engineering University). 2015. Vol. 51. Pp. 72–79. EDN: TTWDSD.
4. Egoshkin N.A., Ushenkin V.A. Interferometricheskaja obrabotka radiolokacionnoj informacii na osnove kombinacii metodov razvertyvanija fazy (Interferometric processing of SAR data based on combining of phase unwrapping methods) // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta (Vestnik of Ryazan State Radio Engineering University). 2015. Vol. 54-2. Pp. 21–31. EDN: VNYZCT.
5. Ushenkin V.A., Egoshkin N.A. Ispol'zovanie apriornoj informacii pri interferometricheskoj obrabotke vysokodetal'noj radiolokacionnoj informacii (Using apriori information in interferometric processing of high resolution SAR data) // Vestnik Samarskogo gosudarstvennogo ajerokosmicheskogo universiteta imeni akademika S.P. Koroljova (nacional'nogo issledovatel'skogo universiteta). 2016. Vol. 15. No. 2. Pp. 208–219. DOI: 10.18287/2412-7329-2016-15-2-208-219. EDN: WILXZF.
6. Eremeev V.V., Egoshkin N.A., Makarenkov A.A., Moskvitin A.E., Ushenkin V.A. Problemnye voprosy obrabotki dannyh ot kosmicheskih sistem giperspektral'noj i radiolokacionnoj s#emki Zemli (The problems in processing of data from spaceborne hyperspectral and SAR systems of Earth survey) // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta (Vestnik of Ryazan State Radio Engineering University). 2017. Vol. 60. Pp. 54–64. DOI: 10.21667/1995-4565-2017-60-2-54-64. EDN: YSRVDV.
7. Copernicus DEM – Global and European Digital Elevation Model (COP-DEM). URL: https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model.
8. Jeksport OpenStreetMap – URL: https://wiki.openstreetmap.org/wiki/RU:Ýêñïîðò.
9. Egoshkin N.A., Ushenkin V.A. Kompleksirovanie cifrovyh modelej rel'efa s cel'ju povyshenija tochnosti opornoj informacii o vysote ob#ektov zemnoj poverhnosti // Cifrovaja obrabotka signalov (Digital Signal Processing). 2017. Vol. 1. Pp. 13–17. EDN: YPBYIH.
Åvaluation of video cameras sensitivity
V.N. Drynkin, e-mail: drynkinv@gosniias.ru
T.I. Tsareva, e-mail: tsareva@gosniias.ru
State research institute of aviation systems (“GosNIIAS”), Russia, Moscow
Keywords: digital image processing, signal-to-noise ratio, increasing the sensitivity of video cameras, binning, restoration of spatial resolution.
Abstract
The paper presents an algorithm for estimating the sensitivity of video cameras in the spectral domain based on calculating the signal-to-noise ratio based on the distribution of the spectral density of the signal and noise power in real video images.
The algorithm has been tested in numerous experiments with real images of the visible range in various daily and seasonal shooting conditions.
A comparison has been made with known methods for estimating sensitivity based on contrast and statistical characteristics of images. The advantages of the proposed algorithm in comparison with known methods are demonstrated when the scene illumination decreases immediately before darkness sets in. To eliminate the influence of difficult weather conditions, the experiments were conducted in clear weather conditions.
It is noted that the implementation of the algorithm as an element of the video surveillance system will allow correctly determining the moment when the signal-to-noise level decreases below a certain threshold when the scene illumination decreases to automatically turn on the high sensitivity mode of video cameras.
To increase the sensitivity of video cameras, it is proposed to use a method based on hardware binning with restoration of spatial resolution.
The results obtained can serve as a basis for improving video cameras in various surveillance conditions.
References
1. Budzinskiy N.V., Zaytsev A.G., Gonta A.S., Mikhaylov A.A. Osnovnyye parametry videokamery. Metodicheskoye posobiye po sistemam okhrannogo televideniya (Basic parameters of a video camera. Methodological manual on security television systems). URL: https://mip-sb.ru/info/camera-recorder/.
2. Sposoby uluchsheniya chuvstvitel'nosti kamer videonablyudeniya (Ways to improve the sensitivity of CCTV cameras) // Nastroyka videonablyudeniya (Setting up video surveillance). 15, avgust, 2019. URL: https://zapishemvse.ru/sposoby-uluchsheniya-chuvstvitelnosti-kamer-videonablyudeniya/.
3. Metodika izmereniya otnosheniya signal/shum kanalov s analogovoy i tsifrovoy modulyatsiyey priborami serii IT-08 i mini-IT (Methodology for measuring the signal-to-noise ratio of channels with analog and digital modulation using IT-08 and mini-IT series devices). OOO «Planar». 2015.
4. Drynkin V.N. i dr. Metod povysheniya chuvstvitel'nosti videokamer na osnove binninga s vosstanovleniyem prostranstvennogo razresheniya (Method for increasing the sensitivity of video cameras based on binning with restoration of spatial resolution) // Tsifrovaya obrabotka signalov (Digital signal processing). 2020, no. 4, pp. 58-63.
5. Golikov E. N. Izmereniye kharakteristik shumov i otnosheniya Signal/shum v televizionnykh izobrazheniyakh s ispol'zovaniyem programmnykh sredstv (Measuring noise characteristics and signal-to-noise ratio in television images using software).
6. Stark K. Otnosheniye signal/shum: osmysleniye, izmereniye, uluchsheniye (chast' 1) (Signal-to-Noise Ratio: Understanding, Measuring, Improving (Part 1)). 5, April, 2010.
7. Fan et al. Brief review of image denoising techniques // Visual Computing for Industry, Biomedicine, and Art. 2019, 08 July. 12 p. URL: https://doi.org/10.1186/s42492-019-0016-7.
8. Katulev A.N., Khramichev A.A., Yagol'nikov S.V. Tsifrovaya obrabotka 2D slabokontrastnykh izobrazheniy, formiruyemykh optiko-elektronnym priborom v slozhnykh fonotselevykh usloviyakh. Obnaruzheniye, raspoznavaniye, soprovozhdeniye dinamicheskikh ob"yektov (Digital processing of 2D low-contrast images generated by an optical-electronic device in complex background conditions. Detection, recognition, tracking of dynamic objects). Monografiya. – M.: Radiotekhnika (Radio Engineering), 2018, 408 p.
9. Yaroslavskiy L.P. Vvedeniye v tsifrovuyu obrabotku izobrazheniy (Introduction to digital image processing). M.: Sov. Radio, 1979. 312 p.
10. Mal'tsev G.N. Vybor rezhima registratsii izobrazheniy v opticheskikh informatsionnykh sistemakh s matrichnymi fotopriyemnikami (Selection of image registration mode in optical information systems with matrix photodetectors) // Informatsionno-upravlyayushchiye sistemy (Information and control systems). 2004, no. 2, pp. 2-5.
11. Drynkin V.N. Razrabotka i primeneniye mnogomernykh tsifrovykh fil'trov (Development and application of multidimensional digital filters). M. (Moscow): FGUP «GosNIIAS». 2016, 180 p.
12. Gorbachev V.A., Grodzitskiy L.V., Danilov S.YU. i dr. BD ¹ 2023621349 Baza dannykh registratsii ob"yektov, poluchennykh s ispol'zovaniyem programmnykh imitatorov raspredelennogo monitoringa na osnove gruppy bespilotnykh letatel'nykh apparatov s tselevymi poleznymi nagruzkami v vidimom, infrakrasnom i radiolokatsionnom diapazonakh (Database of registration of objects obtained using software simulators of distributed monitoring based on a group of unmanned aerial vehicles with target payloads in the visible, infrared and radar ranges), declared 11.04.2023, published 27.04.2023.
Modeling and correction of structural radiometric distortions in satellite images using wavelet packets
K.I. Sokolov, e-mail: skisokolow@yandex.ru
N.V. Makarova, e-mail: makarova.n.v@rsreu.ru
Military representative, Russia, Ryazan
Ryazan State Radio Engineering University (RSREU), Russia, Ryazan
Keywords: image, structural radiometric noise, additive-multiplicative model, wavelet transform, Daubechies wavelet.
Abstract
The article discusses an approach to modeling and correcting structural noise ("banding") in images from Earth observation systems by constructing a wavelet-packet decomposition tree and convolutional neural networks. The features of solving the problem in the presence of blur and defocus in the images are considered.
References
1. Antonushkina S.V., Gurov V.S., Egoshkin N.A. i. dr. Sovremennye tekhnologii obrabotki dannyh dis-tancionnogo zondirovaniya Zemli. Pod. red. V.V. Eremeeva. Moscow: FIZMATLIT. 2015. 460 p.
2. Bekhtin Yu.S. Primery primeneniya teorii vejvlet-kodirovaniya zashumlennyh izobra-zhenij na praktike // Vestnik RGRTU. 2017, no. 60, pp. 45-62.
3. SHaronov A.V., Novoselov S.V.. Algoritm obrabotki rastrovyh izobrazhenij, os-novannyj na vejvlet-preobrazovanii // Vestnik RGRTU. 2009, ¹ 4 (iss. 30), pp.12-16.
4. Bekhtin Yu.S. Algoritm vejvlet-fil'tracii zashumlennyh izobrazhenij // Vestnik RGRTU. 2004, no. 1 (iss. 15), pp .22-26.
5. Sokolov K.I., Makarova N.V. Analiz strukturnyh radiometricheskih iskazhenij na sput-nikovyh snimkah s ispol'zovaniem vejvlet-paketov // Vestnik RGRTU. 2024, no. 88, pp.15-20.
6. Eremeev V.V., Zenin V.A. Modeli korrekcii dinamicheskih strukturnyh iskazhe-nij na kosmicheskih izobrazheniyah // Vestnik RGRTU, 2010, no. 33, pp. 3-7.
7. Malla S. Vejvlety v obrabotke signalov (Wavelets in signal processing). M.: Mir, 2005.
8. Dobeshi I. Desyat' lekcij po vejvletam (Ten lectures on wavelets). Izhevsk: RHD, 2001.
9. Xu L., Ren J.S., Liu C., Jia J. Deep convolutional neural network for image deconvolu-tion // Advances in Neural Information Processing Systems. 2014, pp.1790-1798.
10. Egoshkin N.A., Eremeev V.V. Korrekciya smaza izobrazhenij v sistemah kosmiche-skogo nablyudeniya Zemli // Cifrovaya obrabotka signalov. 2010, no.4, pp. 28-32.
11. Egoshkin N.A. Korrekciya smaza i rasfokusirovki sputnikovyh izobrazhenij s uchetom geometricheskih iskazhenij // Cifrovaya obrabotka signalov. 2016, no.3, pp. 37-41.
Adaptive signal detection on the background clutter
D.I.Popov, e-mail: adop@mail.ru
The Ryazan State Radio Engineering University (RSREU), Russia, Ryazan
Keywords: adaptation, analysis, Doppler phase, matrix filter, detection, evaluation, clutter, signal, synthesis.
Abstract
The article poses the problem of optimal detection of coherent-pulsed signals of moving targets against a background of clutter with a priori uncertainty of the signals and clutter parameters. The aim of the article is the synthesis and analysis of adaptive signal detection systems. The algorithm for optimal detection of signals against a background of clutter is determined by calculating the likelihood ratio.
The implementation of the obtained algorithm for optimal linear filtering is assumed on the basis of the adaptive matrix filter and the non-adaptive multichannel filter. A quasi-optimal algorithm for estimating the Doppler phase of the signal from the output samples of the adaptive matrix filter is synthesized. Modeling on a PC has established that the asymptotic properties of the obtained estimates are acceptable for their use in adaptive signal accumulation. A detection algorithm with adaptive signal accumulation is proposed. This algorithm combines adaptation to the clutter parameters and to the Doppler phase of the signal.
Adaptation to the clutter parameters is carried out in an adaptive matrix filter, from the output samples of which an estimate of the Doppler phase of the signal is calculated, used during its adaptive accumulation. A block diagram of the adaptive signal detection system is given. The optimal size of the detuning of Doppler channels of adaptive signal accumulation is determined by PC simulation. It is established that with an allowable loss level of up to 2 dB, the number of Doppler channels can be reduced by a factor of four.
The use of Doppler signal estimation in detection systems with adaptive signal accumulation allows reducing the number of Doppler channels or, with the same number of Doppler channels, detuning between channels, eliminating intercanal losses.
References
1. Skolnik M.I. Introduction to Radar System, 3rd ed., New York: McGraw-Hill, 2001. 862 p.
2. Richards M.A., Scheer J.A., Holm W.A. (Eds.). Principles of Modern Radar: Basic Principles. New York: SciTech Publishing, IET, Edison. 2010. 924 p.
3. Melvin W. L., Scheer J.A. (Eds.). Principles of Modern Radar: Advanced Techniques. New York: SciTech Publishing, IET, Edison, 2013. 846 p.
4. Radar Handbook / Ed. by M.I. Skolnik. 3rd ed. McGraw–Hill, 2008. 1352 p.
5. Popov D.I. Adaptacija nerekursivnyh rezhektornyh fil'trov // Izvestija vuzov. Ra-diojelektronika. 2009. vol. 52. no. 4. pp. 46-55. (in Russian).
6. Popov D.I. Autocompensation of the Doppler phase of clutter // Cifrovaja obrabotka signalov. 2009. no 2. pp. 30–33. (in Russian).
7. Popov D.I. Avtokompensacija doplerovskoj fazy mnogochastotnyh passivnyh pomeh // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta. 2018. no. 65. pp. 32–37.
8. Popov D.I. Adaptive suppression of clutter // Cifrovaja obrabotka signalov. 2014. no. 4. pp. 32-37. (in Russian).
9. Popov D.I. Adaptivnije regektornjie filtrij kaskadnogo tipa // Cifrovaya obrabotka signalov. 2016. no. 2. pp. 53-56. (in Russian).
10. Popov D.I. Adaptive notch filter with real weights // Cifrovaya obrabotka signalov. 2017. no. 1. pp. 22-26. (in Russian).
11. Popov D.I. Optimizacja nerekursivnjih regektornjie filtrov s chastichnoj adaptaciej // Cifrovaya obrabotka signalov. 2018. no. 1. pp. 28-32. (in Russian).
12. Popov D.I. Optimizacija rezhektornyh fil'trov po verojatnostnomu kriteriju // Cifrovaja obrabotka signalov. 2021. no. 1. pp. 55-58. (in Russian).
13. Kuz'min S.Z. Cifrovaja radiolokacija. Vvedenie v teoriju (Digital radar. Introduction to Theory). Kiev: KViC, 2000. 428 p. (in Russian).
14. Cifrovaja obrabotka signalov v mnogofunkcional'nyh radiolokatorah. Metody. Algoritmy. Apparatura: monografija (Digital signal processing in multifunctional radars. Methods. Algorithms. Equipment: monograph) / pod red. G.V. Zajceva. M.: Radiotehnika, 2015. 376 p. (in Russian).
15. Klochko V.K., Kuznecov V.P., Levitin A.V. i dr. Algoritmy opredelenija koordi-nat dvizhushhihsja celej na baze mnogokanal'noj doplerovskoj RLS // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta. 2015. no. 53. pp. 3-10. (in Russian).
16. Klochko V.K., Kuznecov V.P., Vu Ba Hung. Ocenivanie parametrov radiosignalov ot podvizhnyh malovysotnyh ob#ektov // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta. 2022. no. ¹ 80. pp. 12-23. (in Russian).
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