Digital Signal Processing 
Russian 
Dynamic models of geometric image processing in Earth remote sensing systems Abstract 2. Egoshkin N.A., Moskvitin A.E. Improved accuracy of image correction based on filtration of angular position of scanning mirror measurements // Vestnik of RSREU. 2010. no. 3. pp. 711. 3. Gektin U.M., Egoshkin N.A., Eremeev V.V., Kuznetcov A.E., Kochergin A.M. Prelaunch photogrammetric calibration of russian satellite Elektrol imagery instruments // «The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences». Prague, Czech Republic, 2016. vol. XLIB6. 4. Akhmetov R. N., Eremeev V. V., Kuznecov A. E., Myatov G. N., Poshekhonov V. I., Stratilatov N. R. Organization of HighPrecision Geolocation of Earth Surface Images from the Spacecraft “ResursP” // Issledovanie Zemli is kosmosa. 2017. no. 1. pp. 4453. 5. Voronin A.A, Egoshkin N.A., Eremeev V.V., Moskatinyev I.V. Geometric data processing from Earth global observing space systems // Vestnik of RSREU. 2009. no. 1. pp. 1217. 6. Gektin Yu. M., Egoshkin N.A., Eremeev V.V., Kuznecov A.E., Moskatinyev I.V., Smelyanskiy M.B. Âasic geometric provision of earth observation systems geostationary and highly elliptical orbits // Issledovanie Zemli is kosmosa. 2016. no. 6. pp. 6881. 7. Ljung L. System Identification. Theory for the User. PTR Prentice Hall, Upper Saddle River, 1999. 609 p. 8. Eremeev V.V., Egoshkin N.A., Moskvitin A.E. Geostationary satellite images matching based on using the Earth disk edge points // Vestnik of RSREU. 2007. no. 22. pp. 1017. DEM fusion to improve the accuracy of reference Earth surface elevation data Keywords: : fusion, DEM, interferometric processing, stereophotogrammetric processing, reference data. References 2. Kuznetsov A.E., Poshehonov V.I. Informatsionnaya tehnologiya stereofotogrammetricheskoy obrabotki videodannyh ot mnogomatrichnyh skaniruyushchih ustroystv (Informational technology of multiline scanner videodata stereophotogrammetric processing) // Digital signal processing, 2010, no. 3. pp. 44–49. 3. Egoshkin N.A., Ushenkin V.A. Interferometricheskaya obrabotka radiolokatsionnoy informatsii na osnove kombinatsii metodov razvertyvaniya fazy (Interferometric processing of SAR data based on combining of phase unwrapping methods) // Vestnik of RSREU, 2015, no. 542, pp. 2131. 4. Tadono T., Nagai H., Ishida H., Oda F., Naito S., Minakawa K., Iwamoto H. Generation of the 30 mmesh global digital surface model by Alos PRISM // The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, vol. 41, part B4, pp. 157–162. 5. Farr T.G., Kobrick M. The Shuttle Radar Topography Mission // RTO Meeting Proceedings 61: SpaceBased Observation Technology, 2000, pp. 331–333. 6. Cuartero A., Felicisimo A.M., Ariza F.J. Accuracy of DEM generation from TerraASTER stereo data // The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2004, vol. 35, part B2, pp. 559–563. 7. Shepard D. A twodimensional interpolation function for irregularlyspaced data // Proc. of the 1968 ACM National Conference, 1968, pp. 517–524.
Abstract Permission of observation matrix with wide DC is several times worse than the permission of a matrix with narrow DC. Therefore there is a need to increase permission of a matrix with wide DC to permission of a matrix with narrow DC, having saved temperature characteristics of observation objects in two frequency ranges. It is possible to increase permission at the expense of the optical image of a controlled terrain section as reference image. However it is not always possible because of bad weather conditions and nighttime. For the radiometer with two antennas the reference image can be received by processing of observation matrix created for narrow DC. 2. SHarkov E.A. Radioteplovoe distancionnoe zondirovanie Zemli: fizicheskie osnovy: v 2 t. / T. 1. M.: IKI RAN, 2014. 544 p. 3. Klochko V.K., Kuznecov V.P. Metody vosstanovleniya izobrazhenij i ocenivaniya apparatnoj funkcii po prorezhennoj matrice nablyudenij // Avtometriya. 2016. no. 56. pp. 12 – 21. 4. Klochko V.K., Makarova O.N., Gudkov S.M., Koshelev A.A. Algoritm formirovaniya teplovogo izobrazheniya ob"ektov pri radiometricheskom nablyudenii // Cifrovaya obrabotka signalov. 2016. no. 3. pp. 34 – 36. 5. Gonsales R., Vuds R., EHddins S. Cifrovaya obrabotka izobrazhenij v srede MATLAB. M.: Tekhnosfera, 2006. 616 p.
Abstract The aim is to analyze the effectiveness of the ARF with the real weights, depending on the volume of training sample. The analysis led to the conclusion that, despite the loss filter with a complex weighting processing ARF with RWF with a priori unknown parameters clutter provides better performance than conventional maladaptive algorithms with variable weight in processing time. In the marginal efficiency gains rejection clutter data filters in comparison with the known has been installed. On the basis of the asymptotic properties of the maximum likelihood analyzes the convergence of adaptive algorithms and the value of the loss estimates due to the limited amount of training samples, and proposed adaptive notch filter algorithms analysis method depending on the volume of training sample. The criterion of the efficiency of adaptive rejection algorithms is averaged on the basis of a statistical description of the properties of estimates of unknown parameters of clutter. The resulting analytical expression averaged criterion establishes a connection with the suppression of clutter characteristics used estimates of parameters of clutter (interperiod correlation coefficients) and makes it relatively easy to select the volume of training sample, depending on the size of allowable loss in the filter efficiency. Height loss with increasing filter order is due to an increase in the number of correlation coefficients to be estimated for full adaptation. Increasing the width of the spectrum interference leads to increased evaluation rms deviations and also a cause of the loss increase. These expressions allow regardless of wobble settings to choose for the ARF to the RWF arbitrary order volume of training sample on the basis of admissible losses in the marginal efficiency. References 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. Repin V.G., Tartakovskiy G.P. Statistical synthesis for a priori ambiguity and adaptation on information systems (in Russian) / – Ìoscow: Sovetskoe Radio Publ. 1977. – 432 p. 4. Popov D.I. Optimalnaya obrabotka mnogochastotnjich signalov // Izvestiya vuzov Rossii. Radioelectronika. – 2013. – ¹ 1. – P. 32–39. 5. Popov D.I. Adaptive rejection filter with complex weight // Vestnik Koncerna PVO «Almaz – Antej». – 2015. – ¹ 2 (14). – pp. 2126. (in Russian) 6. Popov D.I. Avtokompensaciya doplerovskoj fazji passivnjich pomech // Cifrovaya obrabotka signalov. – 2009. – ¹ 2. – P. 30–33. 7. Popov D.I. Adaptivnoe podavlenie passivnjich pomech // Cifrovaya obrabotka signalov. – 2014. – ¹ 4. – P. 3237. 8. Popov D.I. Adaptivnije pegektornjie filtrij kaskadnogo tipa // Cifrovaya obrabotka signalov. – 2016. – ¹ 2. – P. 5356. 9. Gantmacher F.R. Matrix theory. M.: Nauka Publ., 1988. – 552 p. 10. Popov D.I. Analiz adaptivnjich system obnarugeniya signalov na fone passivnjich pomech // Radiotechnika. – 2016. – ¹ 11. – P. 7479.
Abstract Implementation of the adaptive to the level of acoustic noises of the coder of a source of VS with μcompanding based on an artificial neural network is offered. It was succeeded to reach lowering of level of dispersion of an acoustic noise on an output of neural network implementation of the coder of a source of the message in tens of times, on comparing with the standard codec, in case of increase in a signaltonoise ratio from 7 to 23 dB in case of different dispersion of an acoustic noise, without lowering of subjective appraisal of quality of VS on a scale of Mean Opinion Score (MOS). Implementation of an algorithm of a prediction of voice signals in voice coders based on ANN is offered. The analysis of a possibility of implementation of the predictor of the VS in voice coders based on ANN is carried out. Advantages of implementation of predictors based on ANN in comparison with the famous predictors based on nonrecursive FIR filters are shown. The possibility of reduction of an order of a prediction from 10% to 60% in case of the same error is proved, and lowering of an error of a prediction from 15% to 70% in case of the same order in case of implementation of predictors on the basis of ANN. Increase in subjective appraisal of quality of VS on MOS scale on 0,10,45 points that is essential advantage of these systems. References 2. Phillips C Loizou. Speech enhancement theory and practice: 1st ed. Boca Raton, FL.: CRC,2007. Releases Taylor & Francis. 3. J. Benesty, J. Chen, Y. Huang, I. Cohen Noise Reduction in Speech Processing // SpringerVerlag, 2009. 4. V. K. Zlobin, D. V. Grigorenko, V. N. Ruchkin, V. A. Romanchuk. Klusterizaciya I vosstanavlivaemost’ neyroprocessornyh sistem obrabotki dannyh (Clustering and restorability of neuroprocessor data handling systems)//Izvestiya TulGU. Tehnicheskie nauki. 2013. Issue 9. Ch.2 C. 125133. 5. S. N. Danilin, M. V. Makarov, S. A. Shchanikov. Proyektirovanie tehnicheskih sredstv s neyrosetevoy arhitekturoy pri iskazhenii shumami vhodnoy informacii (Design of technical means with neural network architecture in case of distortion by noise of input information.) 24th Mezhdunarodnaya Krumskaya konferenciya "SVCHtehnica I telekommunikacionnye tehnologii": materialy konf.: in 2 t.  Sevastopol, 2014. 6. Kirillov S. N., Sazonova E. S. Neyrosetevaya realizaciya predskazatelya rechevyh signalov v koderah rechi (Neural network implementation of the predictor of voice signals in voice coders)//Vestnik RGRTU No. 56 – 2016 – page 3137 7. Kirillov S. N., Popova E. S. Neyrosetevaya realizaciya kodera rechevyh signalov adaptivnogo k urovn’u akusticheskih shumov (Neural network implementation of the coder of voice signals of the adaptive to the level of acoustic noises). Vestnik RGRTU, 2015 No. 54, page 4044 8. Bolodurina, V. N. Reshetnikov, M. G. Taspayeva. Primeneniya i adaptaciya neyrosetevyh technologiy v zadache identifikacii dinamicheskih obyektov (Application and adaptation of neural network technologies in the task of identification of dynamic objects.) Programmnye produkty, sistemy i algoritmy No. 1, 2012 of. 9. Ciresan D., Meier U., Masci J and Schmidhuber J. Multicolumn Deep Neural Network for Traffic Sign Classification. Neural Networks, Vol. 34, August 2012, pp. 333 – 338È.Ï. 10. O. E. Karasyov Primenenie teorii nechetkih mnozhestv dlya obrabotki videoinformacii v telekommunikacionnyh sistemah (Applycation of the theory of indistinct sets for processing of video information in telecommunication systems.) VI Vserossiyskie nauchnye Zvorykinskie chteniya: sb.tez.dokl. Vserossiyskoy mezhvuzovskoy nauchnoy konferencii. Murom: Izd.  polygraphicheskiy center MI VLGU, 2014.  791 pages. 11. Eremin D. M., Gartseev I. B. 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Abstract The performance analysis for binary MTDs over Gaussian channel is fulfilled in this paper. It is shown best MTDs can provide coding gain comparable or better than coding gain for known practical implemented errorcorrection methods as Viterbi decoder for short convolutional codes, decoders for turbo and lowdensity paritycheck codes. The symbolic errorcorrection codes over qary symmetric channel are discussed. It is shown the symbolic MTDs (qMTDs) solve fully all problems for digital world in transmission, storage and recovering digital data in any information system. The qMTDs are many times better than decoders for ReedSolomon (RS) codes in error correction and implementation complexity. It allows to claim the qMTDs can replace RS codes in any applications. In the paper it is shown the MTD algorithms have also no concurrent in complexity and performance over erasure channels. The methods, properties and possibilities of codes and algorithms allowing improve the MTD and qMTD performance in a simple way and forming very powerful perspective intellectual space for decoding methods developing are discussed additionally. These new paradigms of OT have allowed to create methods for different coding clusters working effectively near to channel capacity. The development of these methods transfer to new code structure rapidly. The results presented testify successful decision of main scientific and technological problems for our informational digital civilization – developing of simple methods for achievement of high reliable transmission, storage and recovering for digital information. Solution of these complex problems allows to switch efforts of engineers and scientists for solve new important tasks for our digital world. References 2. Zolotarev V.V. Teoriya i algoritmy mnogoporogovogo dekodirovaniya (Theory and algorithms for multithreshold decoding). Moscow: Radio i svyaz', Goryachaya liniya – Telekom, 2006, second edition  2014. 3. Zolotarev V.V., Zubarev Yu.B., Ovechkin G.V. Mnogoporogovye dekodery i optimizatsionnaya teoriya kodirovaniya (Multithreshold decoding and optimizing coding theory). Moscow: Goryachaya liniya – Telekom, 2012. 238 p. 4. Zolotarev V., Zubarev Y., Ovechkin G. Optimization Coding Theory and Multithreshold Algorithms. Geneva, ITU, 2015, 159 p. 5. Zolotarev V.V., Ovechkin G.V. Primenenie mnogoporogovykh metodov dekodirovaniya pomekhoustoichivykh kodov v vysokoskorostnykh sistemakh peredachi dannykh, Elektrosvyaz'. Moscow, 2014, No. 12, P. 1014 6. Kuznetsov N.A., Zolotarev V.V., Ovechkin G.V., Ovechkin P.V. Nedvoichnye mnogoporogovye dekodery i drugie metody korrektsii oshibok v simvol'noi informatsii, Radiotekhnika. Moscow, 2010, No.6, Issue 141, P. 49. 7. Zolotarev V.V., Ovechkin G.V., O sopostavlenii novykh metodov pomekhoustoichivogo kodirovaniya, Proc. of 18th Int. Conf. "Digital Signal Processing and Its Applications". Moscow, 2016, P. 59–65. 8. Massey J., Threshold decoding, Cambridge, Massachusetts: M.I.T. Press, 1963. 9. Clark G., Jr., Cain B. ErrorCorrection Coding for Digital Communications. New York: Plenum Press, 1981. 10. Zolotarev V.V., Nazirov R.R., Nikiforov A.V., Chulkov I.V. Novye vozmozhnosti mnogoporogovogo dekodirovaniya po vysokodostovernoi peredache dannykh DZZ, Sovremennye problemy distantsionnogo zondirovaniya zemli iz kosmosa. Sbornik nauchnykh statei. Moscow, OOO "Azbuka2000", 2009, Issue 6, Vol. I. P. 167173. 11. Zolotarev V.V., Nazirov R.R., Chulkov I.V., Ovechkin G.V. Algoritmy MPD, Rossiiskii kosmos. Moscow, 2009, No. 1, P. 60–63. 12. Zolotarev V.V., Ovechkin G.V., Nazirov R.R., Ovechkin P.V., Chulkov I.V. Effektivnoe nedvoichnoe mnogoporogovoe dekodirovanie pomekhoustoichivykh kodov dlya sistem distantsionnogo zondirovaniya Zemli, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, Sbornik statei. Moscow, 2010, Vol. 7, No. 2, P. 269274. 13. Zolotarev V.V., Chulkov I.V. Maloizbytochnoe kodirovanie dlya vysokoskorostnykh kanalov, Proc. of 11th Russian open conf. "Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa". Moscow, 2013. 14. Ovechkin G.V., Teoriya kaskadnogo dekodirovaniya lineinykh kodov dlya tsifrovykh radiokanalov na osnove mnogoporogovykh algoritmov: dis. dra tekhn. nauk (Theory of concatenated decoding of linear codes based on multithreshold algorithms for digital radiochannels: Doct. tech. sci. thesis). Ryazan, 2011, 301 p. 15. Ovechkin P.V. Razrabotka algoritmov povysheniya effektivnosti nedvoichnykh mnogoporogovykh dekoderov v sistemakh peredachi i khraneniya bol'shikh ob"emov informatsii: dis. kand. tekhn. nauk (Development of algorithms for improving performance of symbolic multithreshold decoders in high volume data transmission and storage systems: Cand. tech. sci. thesis). Ryazan, 2009, 131 p. 16. Zolotarev V.V., Ovechkin G.V., Satybaldina D.Zh., Tashatov N.N., Adamova A.D. Sposob myagkogo mnogoporogovogo dekodirovaniya pomekhoustoichivogo koda. Patent Kazakhstan Respublic 93989. Priority date 15.10.2014. 17. Zolotarev V.V., Ovechkin P.V. Sposob kodirovaniya i dekodirovaniya blokovogo koda s ispol'zovaniem algoritma Viterbi. Patent RF 2608872, Priority date 25.01.2017. 18. Zolotarev V.V., Ovechkin G.V. Sposob raboty simvol'nogo porogovogo elementa v simvol'nom mazhoritarnom dekodere. Patent RF 2573741. Priority date 22.12.2015. 19. Zolotarev V.V. Sposob dekodirovaniya pomekhoustoichivogo koda. Patent RF 2557454. Priority date 25.06.2015. 20. Zolotarev V.V., Ovechkin G.V. Ustroistvo mnogoporogovogo dekodirovaniya lineinykh kodov dlya gaussovskikh kanalov. Patent RF 44215. Priority date 27.02.2005. 21. Zolotarev V.V. Vysokoskorostnoe ustroistvo mnogoporogovogo dekodirovaniya lineinykh kodov. Patent RF 44216. Priority date 27.02.2005. 22. Zolotarev V.V., Averin S.V. 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The article analyzes the methods of calculating the frequency characteristics of the filter for uncompleted transient processes. It is shown that the known methods for calculating the dynamic frequency characteristics of IIR filters in unsteady (transient) mode with increasing sample size yield results that are close in terms of the mean square error, but they differ significantly by the criterion of the maximum deviation. In general, the proposed method with an equivalent length of the sequence leads to smaller errors, and the frequency response is closer to the static characteristic. The initialization method allows to bring the dynamic frequency characteristics closer to static ones and substantially shorten the duration of transient processes in the filter. 2. Fletcherml., Burleigh D. Metod predvaritelnoy obrabotki signalov dlj uluchschenia raboty ustroistva selekziy dvigugihsj zelei v radiolokatorash s FAR //TIIER. 1972.– ¹12.– C. 137 138. 3. Koshelev V.I. ARSS modeli sluchainysh processov. Prikladnye zadachi sinteza i optimisazii. M.: Radio i svjas, 2002. 116 s. 4. Vitajzev V.V. Mnogoskorostnaj obrabotka signalov. M.: Gorjachaja liniaj telecom, 2017. 336s. 5. Koshelev V.I., Andreev V.G. Optimizaziaj avtoregressionych modeley processov s polimodalnym spektrom/ Izvestia vuzov. Radioelectronika. 1996. Ò. 39. ¹ 5. s. 4348. 6. Koshelev V.I. Ozenka kachestva modelej sluchainych processov pri korotkoj vyborke //Vestnic RGRTA. Ryazan. 2001, Vyp 8. s. 3135. 7. Koshelev V.I., Andreev V.G. Adaptivnaj obrabotka signalov v filtrach s perestraivaemymi porjdkami //SVChtechnica i sputnicovy priem: Materialy 3ei Crymskoi conferenzii: V 6ti t., g. Sevastopol, 2023 sentajbraj 1993 g. Sevastopol: ECOSI Gidrofizika, 1993 – T.3 s. 285287. 8. Director S., Rohrer R. Vvedenie v teoriu system: Per. s angl. ped N.P. Buslenko. – M.: Mir, 1974. – 464 s.
Abstract Description and analysis of the parameters of fractal phenomena are considered in context of the theory of optimal statistical solutions. A statistical interpretation of the correlation integral is used, as the probability of a nonexceeding of a given scale value by distances between vectors formed by signal or image samples in a pseudophase space. The correlation dimension is chosen as a fractal measure of signals and images. The algorithms for object and edge detection on the image are obtained by maximum likelihood method. When solving the task of detecting objects on images, the methods of texture analysis are used, where the correlation dimension is chosen as the texture property. The fractal image is given in the form of a twodimensional fractal Brownian motion  a fractal Brownian surface, as well as a set of distances between vectors in a pseudophase space. A refinement of the description of the distances between vectors in a pseudophase space with considering of their given topological dependence, and their probabilistic description is proposed. Expressions for the likelihood ratio logarithm are obtained, the error probabilities are calculated for the object and edge detection in case of Gaussian and nonGaussian approximations. It is shown that the maximum likelihood estimate of the correlation dimension is sufficient statistics for the detection problem. It is proposed to determine the quality of the edge detection by a system of error probabilities  the probability of false edge fields and the probability of skipping the edge. Algorithm of correlation dimension estimation is modified for case of depended distances between vectors. Geometrical dependence affects the value of dimension estimate. It is shown, that proposed algorithms are effective for correlation dimension estimation due to large amount of data, obtained from radar with synthesized aperture of antenna. Despite of high computational complexity of fractal dimension estimation it is possible to simplify computational process by usage of statistical description. 2. Culter C.D. A Review of the Theory and Estimation of Fractal Dimension // Nonlinear Time Series and Chaos. V.1. Dimension Estimation and Models, 1993. P. 1107. 3. Parker T.S., Chua L.O. Chaos: A Tutorial for Engineers // Proceedings of the IEEE, 1987, T. 75, ¹ 8. Ñ. 9821008. 4. Sosulin Yu.G. Theory of Detection and Estimation of Stochastic Signals. M.: Sovetskoe radio, 1978. 320 p. 5. Sosulin Yu.G., Parshin Yu.N. EstimatingCorrelationCompensating Processing of Multidimensional Signals // Radiotechnica i electronica. 1981. V..26, ¹8. pp. 16351643. 6. Sosulin Yu.G., Kostrov V.V., Parshin Yu.N. EstimatingCorrelation Processing of Signals and Interference Compensation. M.: Radiotechnica, 2014. 632 p. 7. R.M. Crownover. Introduction to Fractals and Chaos. Jones and Bartlett, 1995. 8. Pisarenko D. V., Pisarenko V.F. Statistical Estimation of the Correlation Dimension // Physics Letters, A b(197), 1995. P.3139. 9. Luca L., Lasocki S., Luzio D., Vitale M. Fractal Dimension Confidence Interval Estimation of Epicentral Distribution // Annali di Geofisica, 42(5), 1999. P.911925 10. Luciana De Luca, Dario Luzio, Massimo Vitale. A ML Estimator of the Correlation Dimension for LeftHand Truncated Data Samples // Pure and Applied Geophysics, V.159, ¹ 1112, 2002. P. 27892803. 11. Parshin A.Yu., Parshin Yu.N. Complexing of Energy and Fractal Detectors // Problems of transmission and processing of information in telecommunications networks and systems. 17th International scientific and technical conference. Part 2. Ryazan: RSREU, 2012. P. 3739. 12. Parshin A., Parshin Yu. Usage of nonGaussian Statistics for RF Signals Detection by Complex Energy and Fractal Detector // International Radar Symposium  IRS 2013, Proceeding, volume I, II, Drezden, Germany. – German Institute of Navigation, 2013, pp. 779784. 13. Parshin A., Parshin Yu. Using the Maximum Likelihood Estimates of Fractal Dimension in nonGaussian Statistics for Detecting Radio Signals // Digital Signal Processing. 2013. ¹1. p. 4246. 14. Parshin A.Yu., Parshin Yu.N. Synthesis and Analysis of Efficiency of Algorithm for Objects Edges Detection by Maximum Likelihood Method // 2nd Mediterranean Conference on Embedded Computing (MECO). Budva. Montenegro. June 15th21st 2013. 282 p. 15. Sosulin Yu.G., Russkin A.B. Fractal Detection of Extensive Low in Contrast Objects on Images // Radiotechnica. 2009. ¹ 12. P. 4857. 16. Pesin Ya. B. On Rigorous Mathematical Definitions of Correlation Dimension and Generalized Spectrum for Dimensions // Journal of Statistical Physics, V.71, 1993. P.529547. 17. 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Budapest, Muszaki Konyvkiado, 1972. 260 p. 25. Provenzale A., Smith J.A., Vio R., Murate G. Distinguishing Between LowDimensional Dynamics and Randomness in Measured Time Series // Physica D 58. 3149. 1992. 26. Shuster G. Determined chaos: Introduction. M.: Mir. 1988. 240 p. 27. Parshin A.Yu., Parshin Yu.N. The Maximum Likelihood Estimation of the Correlation Dimension, Taking into Account the Influence of the Estimation Bias and Truncation of the Range of Scales // Bulletin of Ryazan State Radioengineering University. 2013. ¹41 (46.). P.1825. 28. Hong Qian, Gary M Raymond, James B Bassingthwaighte. On TwoDimensional Fractional Brownian Motion and Fractional Brownian Random Field // Journal of Physics A: Mathematical and General, V.31, No. 28 (1998) P. L527L535.
Abstract When OFDM signal is propagated through multipath channel, estimation of channel parameters is necessary for its demodulation. Even the best estimation methods have a nonzero error. As a result, bits which are transmitted through the multipath channel by constellation points with higher amplitude and all other things being equal, have lower reliability. Proposed method allows to specify loglikelihood ratio for OFDM signal with constellation rotation technique by taking into account multiplicative noise component of signal caused by estimation of channel parameters. Unlike the common method, in the proposed method I and Q components are not considered as onedimension modulated, but method still have low computational complexity. Results of competitive modeling using terrestrial broadcasting system RAVIS model in DRM+ multipath propagation channel models are listed for cases when constellation rotation technique is used and without it. SNR advantage of using proposed demodulation method is determined. Modeling shows that for QPSK constellation SNR advantage is more than 1 dB with coderate ¾, in range from 0.2 to 0.5 with coderate ½. For 16QAM constellation SNR advantage is more than 0.3 dB with coderate ¾, with coderate ½ there is no SNR advantage. Thus, using of constellation rotation technique makes sense with high data rate and low constellation order and can be achieved without significant calculation complexity increase. 2. GOST R 543092011. Realtime Audio Visual Information System (RAVIS). Process of frame structure generation, channel coding and modulation of digital terrestrial narrowband broadcasting VHFband system. Technical specifications. 3. ETSI TS 102 831: «Digital Video Broadcasting (DVB); Implementation guidelines for a second generation digital terrestrial television broadcasting system (DVBT2)», 2012. 4. Levchenko A. S. Demodulator COFDM signala s multiplicativnimi schumami // Radiotechnicheskie i telecommunikacionnie sistemi. ¹1, 2016. 5. ETSI ES 201 980 V3.1.1 (200908). Digital Radio Mondiale (DRM); System Specification.
Abstract Let s_{0} be the modulation binary sequence, expressed by row vector, with n elements ±1, let s denote the sequence of length N containing s_{0} as a central part with m proceeding and m subsequent zeros, and let x be a real row vector of the filter pulse response. Filter output is a crosscorrelation function of sequences x and s : The paper investigates properties of filter output for this solution. Several families of modulation sequences are considered: M sequences, Legendre sequences, and sequences up to length 271 with best known integrated sidelobe level of autocorrelation function (bestACF family). For each sequence family main characteristics of the filter output are calculated and analyzed: integrated sidelobe level, peak sidelobe level (PSL), loss in signaltonoise ratio in comparison with matched filer. For bestACF family roughly half of the sequences demonstrates approximately linear growth in decibels of the quality criterion Q(m/n), the slope of the function being 8 16 dB for each unity step of the ratio m/n. Loss in signaltonoise ratio for these sequences is rather low, about 0.51.5 dB for m≤2n. Sequences with slope greater than 14 dB are tabulated in the paper. Main characteristics for Legendre sequences are somewhat worse but they may be used for arbitrary large , where bestACF sequences are not known. Msequences have poor parameters so they are not recommended for use in the method considered. Average values for characteristics mentioned above are tabulated in the paper for all three sequence families. Thus extending the length of the filter response up to (3  5)n leads to increasing ISL up to 2545 dB and PSL up to 4560 dB for large amount of sequences, keeping the loss in signaltonoise ratio less than 1 dB. So mismatched filtering minimizing ISL is a useful method applicable not only to shortlength sequences but (despite common opinion) to long sequences as well. Modern signal processors enable straightforward implementation of the discussed mismatched filtering in real time. 2. A.A. Trukhachev. Radiolokatsionnye signaly i ih primenenie (Radar signals and their applications). Ì. Voenizdat. 2005. 320 p. 3. M. A. Nasrabadi, M. H. Bastani. A Survey on the Design of Binary Pulse Compression Codes with Low Autocorrelation. Trends in Telecommunications Technologies. Christos J Bouras (Ed.). ISBN: 9789533070728. InTech. 2010. 4. J. Jedwab. A Survey of the Merit Factor Problem for Binary Sequences. Sequences and Their Applications. Proceedings of SETA 2004. ed. T. Helleseth et al. Lecture Notes in Computer Science. vol. 3486. pp. 30–55. SpringerVerlag. Berlin Heidelberg. 2005. 5. P. Stoica, J. Li, and M. Xue. Transmit codes and receive filters for radar. IEEE Signal Processing Magazine. vol. 25. pp. 94–109. November 2008. 6. N. Levanon. CrossCorrelation of Long Binary Signals with Longer Mismatched Filters. IEE Proceedings. Radar, Sonar, and Navigation. vol. 152. pp. 377–382. Dec. 2005. 7. R. A. Horn and C. R. Johnson. Matrix Analysis. Cambridge University Press. 1985. 652 p. 8. J. Knauer. Merit Factor Records. URL: http://www.cecm.sfu.ca/~jknauer/labs/records.html. Nov. 2004. 9. W.W. Peterson, E.J. Weldon. Errorcorrecting codes. MIT Press. 1972. 590 p. 10. J. Jedwab, D.J. Katz, K.U. Schmidt. Advances in the Merit Factor Problem for Binary Sequences. Journal of Combinatorial Theory. Volume 120. No 4. pp. 882906. May 2013.
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