Digital Signal Processing |
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Keywords: space-time adaptive processing (STAP), regularization procedure, LCMP beamformer, adaptive phased antenna array. Calculating the value of the RÑ of the sample matrix is one of the key issues for ensuring robustness of the optimal beamformer of adaptive phased array in the equipment. The article discusses the existing methods for estimating the optimal value of the RC, suitable for a blind beamformer, which have different efficiency and complexity of hardware implementation. TSVD-based methods are computationally intensive due to the need SVD transformations. Of interest are direct algorithms based on Tikhonov's regularization, which are not based on the statistical characteristics of the input signals. Blind LCMP beamformer was simulated under conditions of poorly conditioned SCM. Comparison of regularization methods depending on different signal-noise situations and the number of samples are shown in the graphs. 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with high spectral and energy efficiency (Part 1)
Abstract It is shown that with the optimal choice of its parameters - the mode of demodulation of sig-nals from QAM and ECC, it is possible to create a communication system that will have sufficiently high EE and SE. The study showed that relative to the “ideal” communication system, its SE (μs) and EE (μen) with a rational choice of parameters will be, respectively, μs=0.87 and EE μen=-2...-5 dB for 3≤ Rf≤10 bit/sec·Hz. Another ECC also considered, in which multidimensional optimal signal ensembles (ES) are used, which have a relatively small normalized duration. A communication system was also investigated in which N-dimensional optimal ES are used for signal transmission, and ECCs are not used. It is shown that with a sufficiently long normalized signal duration of such an ES, it can provide high reliability of communication with high SE and EE coefficients (μs=1 and μen≈0), i.e. the characteristics of such a system are close to those of the “ideal” Shannon system. 2. Shannon C. Probability of error for optimal codes in Gaussian channel. Bell System Techn. J., May, 1959. pp. 611-656 3. Bykhovskiy M.A. Giperfazovaya modulyatsiya – optimal'nyy metod peredachi soobshcheniy v gaussovskikh kanalakh svyazi. (Hyperphase modulation is the optimal method for transmitting messages in Gaussian communication channels) M.: Tekhnosfera, 2018. 310 p. 4. W. Wesley Peterson and E. J. Weldon, Jr. Error-Correcting Codes, Second Edition, The MIT Press, 1972. 576 p. 5. A.A. Frolov, V.V. Zyablov, Granitsy minimal'nogo kodovogo rasstoyaniya dlya nedvoichnykh kodov na dvudol'nykh grafakh, Problemy peredachi informatsii, (Boundaries of the minimum code distance for nonbinary codes on bipartite graphs, Problems of Information Transmission) 2011, vypusk 4, 27-42 6. European standard. ETSI EN 302 307-1 V1.4.1 (2014-11). Digital Video Broadcasting (DVB); Second generation framing structure, channel coding and modulation systems for Broadcasting, Interactive Services, News Gathering and other broadband satellite applications; Part 1: DVB-S2, 2014. pp. 1-80 7. EBU Tech 3348 r4, Frequency and network planning aspects of DVB-T2, version 4.1.1. Gene-va, October, 2014. pp. 1-83 8. Dolinar S. Divsalar D. Pollara F. Code Performance as a Function of Block Size. TMO Progress Report 42-133 May 15, 1998 9. Polyanskiy Y., Poor H.V., Verdu S. Channel Coding Rate in the Finite Block Length Regime. IEEE Trans. on Inf. Theory, Vol. 56, no. 5, 2010. Pp. 2307-2359 10. John G. Proakis. Digital communications/ New York : McGraw-Hill, 1989. 608 p. 11. Ayvazyan S.A., Yenyukov I.S., Meshalkin L.D. Prikladnaya statistika: Osnovy modelirorvaniya i pervichnaya obrabotka dannykh. (Ayvazyan S.A., Enyukov I.S., Meshalkin L.D. Applied Sta-tistics: Basics of Modeling and Primary Data Processing). M.: Finansy i statistic. 1983. 471 p. 12. Fink L.M. Teoriya peredachi diskretnykh soobshcheniy. (The theory of transmission of discrete messages) M.: Sovetskoye radio, 1970. 726 p. 13. Uryvsky L., Osypchuk S. The analytical description of regular LDPS codes correcting ability. Institute of Telecommunication Systems National Technical University of Ukraine, Kyiv Poly-technic Institute. Transport and Telecommunication, Vol. 15, ¹ 3, 2014. pp. 177–184 14. Vlastimil Benovsky, Eurovision. DVB-S extension higher spectral efficiency. WBU-ISOG Fo-rum Los Angeles, May, 2013. pp. 1–26 15. Eroz M., Sun F.-W., Lee L.-N. DVB-S2 low density parity check codes with near Shannon limit performance. Int. J. Satell. Commun. Networking, Vol. 22, no. 3, 2004. pp. 269–279 16. Recommendation ITU-R BO. 1784-1 (12/2016). Digital satellite broadcasting system with flex-ible configuration. (Television, Sound and Data). pp. 1–25 17. Berrou C. Glavieux A., Thitmajshima P., Near Shannon limit error-correcting coding and decod-ing: turbo-codes. In ICC, (Geneva, Switzerland), pp. 1064, May, 1993. pp. 1064-1070 18. Vishnevskiy V., Portnoy S., Shakhnovich I. Entsiklopediya WiMAX. Put' k 4G. (WiMAX En-cyclopedia. The road to 4G) M.: Tekhnosfera, 2009. 472 p.
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Abstract The use of partially overlapping signals based on the proposed signals in parallel transmission in comparison with non-overlapping sinusoidal orthogonal signals allows us to obtain better specific band costs, increase the rate of decline of the side lobes of the spectral power density (reduce out-of-band radiation). At the same time, the noise immunity practically does not deteriorate. The purpose of the work is to consider the formation of the transmitted signal and the processing of the received signal using a complex envelope. At the same time, the main part of the algorithm for forming and processing is carried out in the low-frequency region. An expression for the complex envelope of the transmitted signal is obtained, and an algorithm for generating the transmitted signal is given. An algorithm for obtaining and processing the complex envelope of the received signal is described. An analytical expression is obtained for the error probability when processing the received signal using a complex envelope. Modeling of signal generation and processing in the Matlab environment is carried out. The simulation results confirm the obtained expression for determining the error probability. References 2. Prokis Dzhon. Cifrovaja svjaz' [Digital communication]. Per. s angl./ Pod red. D.D. Klovskogo.– M.: Radio i svjaz'. 2000.– 800 p. (in Russian) Steganographic use of the structure of the signal of the digital image Abstract Steganography hides the very fact of information transmission. The transmitted information is masked by transmission as part of other information. A simple tabular encoding method using the binary structure of the digital image signal is proposed. The carrier of hidden information is the number of units in the signal code. Fast decoding is performed by sorting the received binary signal at the rate of its arrival and can be used for rapid information exchange. The procedures in question do not require complex algorithms to be implemented and can be implemented with an FPGA. Its experimental verification was performed. The described technology is protected by a Russian patent. References 2. Agranovsky, A. V., Balakin A. B., Gribunin V. G., Sapozhnikov S. A. Steganography, digital watermarking and steganalysis. Moscow: Vuzovskaya kniga, 2009. 3. Konakhovich G. F., Puzyrenko A. I. Computer steganography. Theory and Practice. K.: MK-Press, 2006. 4. Koronovsky A. A., Moskalenko O. I., Popov P. V., et al. A method of secret transmission of information. Patent of the Russian Federation 2295835 // Bulletin of Inventions No. 8, 2007. 5. Moskalenko O. I., Koronovsky A. A., Khramov A. E. A method of hidden transmission of information with changing characteristics of a noise generator. Patent of the Russian Federation 2421923 // Bulletin of Inventions No. 17, 2010. 6. Nazimov A. I., Pavlov A. N. Method of protected information transmission using pulse coding. Patent of the Russian Federation 2493659 // Bulletin of inventions No. 26, 2013. 7. Lobov N. N. A method for classifying signals and a device for its implementation. Patent of the Russian Federation 2207733 // Bulletin of Inventions No. 08, 2005. 8. Kudelsky A. Method of encoding and decoding the video signal. Patent of the Russian Federation 2132114 // Application of the PCT WO 91/13517, 1999. 9. Gribunin V. N., Okov I. N., Turintsev I. V. Digital steganography. Moscow: Solon-press, 2009 10. Kustov V. N., Fedchuk A. A. Methods of embedding hidden messages.// Data protection. Confidant. No. 3, 2000. 11. Yadykin I. M. Device for determining the number of units in an ordered binary number. Patent of the Russian Federation 2522875 // Bulletin of Inventions No. 20, 2014. 12. Kottsov V. A. Increasing the dynamic range of the video system by logical addition of digital images // Digital signal processing No. 3, 2019. 13. Kottsov P. V., Kottsov V. A. A simple way of hidden transmission of video information // Seventh Scientific and Technical Conference "Technical Vision in Control Systems-2016”, Moscow, March 15-17, 2016 14. Kottsov V. A., Kottsov P. V. Method of hidden transmission of digital information. Patent of the Russian Federation 2636690 // Bulletin of inventions No. 33, 2017. 15. Steshenko V. B. Plis of ALTERA: design of signal processing devices. M. Dodeka, 2000.
2. Springer Handbook of Global Navigation Satellite Systems / ed. Pet.J.G. Teunissen, O. Montenbruck. Springer International Publishing. 2017. 1327 p. 3. Kashkin V.B., Kokorin V.I., Mironov V.L., Sizasov S.V. Jeksperimental'noe opredelenie jelektrofizicheskih parametrov lesnogo pokrova s ispol'zovaniem signalov global'nyh navigacionnyh sistem GLONASS i GPS (Experimental determination of electrophysical parameters of forest cover using signals from global navigation systems GLONASS and GPS) // Radiotehnika i jelektronika. 2006. V. 51. no 7. pp. 825–830. 4. GNSS Remote Sensing / S. Jin, E. Cardellach, F. Xie. Dordrecht, Heidelberg, New York, London, Springer. 2014. 286 p. 5. Mihajlov M.I., Muzalevskij K.V., Mironov V.L. Izmerenie tolshhiny l'da na presnovod-nom prude i reke s ispol'-zovaniem signalov GLONASS i GPS (Measuring ice thickness in a fresh-water pond and river using GLONASS and GPS signals) // Sovremennye problemy distancionnogo zondirovanija Zemli iz kosmosa. 2017. V. 14. no 2. pp. 167–174. 6. Padohin A.M., Kurbatov G.A., Nazarenko M.O., Smolov V.E. GNSS-reflektometrija urovnja Chernogo morja v jeksperimentah na stacionarnoj okeanograficheskoj platform (GNSS re-flectometry of the Black Sea level in experiments on a stationary oceanographic platform) // Vestnik Moskovskogo universiteta. Serija 3. Fizika. Astronomija. 2018. no 4. pp. 80–86. 7. Makarov D.S., Sorokin A.V., Harlamov D.V. Ispol'zovanie signalov navigacionnyh sput-nikov v monitoringe zemnyh pokrovov (The use of signals from navigation satellites in monitoring the earth's cover) // Sibirskij zhurnal nauki i tehnologij. 2019. V. 20. no 1. pp. 8–19. 8. Sorokin A.V., Kuzmin E.V. Izmenenija amplitudno-vremennyh zavisimostej superpozicii sig-nalov navigacionnyh sputnikov v processah otrazhenija i rassejanija zemnymi pokrovami (Changes in the amplitude-time dependences of the superposition of signals from navigation satel-lites in the processes of reflection and scattering by earth covers) // Enisejskaja Fotonika – 2020: Te-zisy dokladov Pervoj Vserossijskoj nauch. konf. s Mezhdunar. uch. Krasnojarsk: Izd-vo IF SO RAN. 2020. pp. 186–187. 9. Sorokin A.V., Kuzmin E.V., Makarov D.S., Harlamov D.V. Reflektometrija ledovyh pokrovov pri razlichnyh sezonnyh sostoja-nijah po signalam navigacionnyh sputnikov v L1-diapazone (Reflectometry of ice cover at different seasonal conditions by signals of navigation sat-ellites in L1-range) // Regional'nye problemy distancionnogo zondirovanija Zemli: materialy VII Mezhdunar. nauch. konf. Krasnojarsk: Sib. Feder. un-t. 2020. pp. 286–289. 10. Cifrovaja obrabotka signalov. Prakticheskoe rukovodstvo dlja inzhenerov i nauch-nyh rabotnikov (Digital signal processing. A Practical Guide for Engineers and Scientists) / S. Smit. per. s angl. M.: Dodjeka-ÕÕI. 2012. 720 p. 11. Cifrovaja obrabotka signalov: ucheb. posobie. 2-e izd. pererab. i dop. (Digital signal pro-cessing: textbook. 2nd ed. rev. and add.) / A.S. Glinchenko. Krasnojarsk: IPC KGTU. 2005. 482 p. 12. Cifrovoj spektral'nyj analiz i ego prilozhenija (Digital Spectrum Analysis and its Applica-tions) / S.L. Marpl.-ml. Per. s angl. M.: Mir. 1990. 584 p. 13. Teoreticheskie osnovy statisticheskoj radiotehniki. 3-e izd. pererab. i dop. (Theoretical Foundations of Statistical Radio Engineering. 3d ed. rev. and add.) / B.R. Levin. M.: Radio i svjaz'. 1989. 656 p. 14. Radiotehnicheskie cepi i signaly: ucheb. dlja vuzov. 3-e izd. pererab. i dop. (Radio engi-neering circuits and signals: textbook for universities. 3d ed. rev. and add.) / S.I. Baskakov. M.: Vysshaja shkola. 2000. 462 p. 15. Tablicy neopredeljonnyh integralov (Indefinite Integral Tables) / Ju.A. Brychkov, O.I. Marichev, A.P. Prudnikov. 2-e izd. isprav. M.: FIZMATLIT. 2003. 200 p.
Optimization of rejection filters by the probabilistic criterion The problem of optimizing the weight vector of RF is solved by the method of nonlinear programming. For the convergence of the solution to the unimodal extremum, it is proposed to introduce restrictions on the form of the RF frequency response, setting it in the form of equidistant frequency samples and assuming the phase characteristic to be linear. The amplitude-frequency characteristic outside the rejection band (non-transmission) to be monotonic, taking into account its sym-metry in the range of unambiguity. The inverse discrete Fourier transform of the frequency samples determines the filter weights. In the cascade form of the RF implementation, it is proposed to optimize the weight coefficients of individual links directly. The corresponding iterative optimization procedure is given. The results of the optimization of the RF on by the energy and probabilistic criteria are compared. Significant gains in the signal-to-noise threshold ratio were found when optimizing the RF parameters of high orders according to the probabilistic criterion in comparison with the energy criterion. The principles of RF adaptation under a priori uncertainty of clutter parameters are proposed and the corresponding block diagram of adaptive RF is presented. The considered method of optimizing two or more RF parameters of high orders according to the probabilistic criterion opens up new opportunities in improving the efficiency of detecting signals of moving targets against the background of clutter, providing significant gains in comparison with similar results of optimizing the RF according to the energy criterion. 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. Adaptive notch filter with complex weight // Vestnik Kontserna PVO «Almaz – Antej». 2015. no 2 (14). pp. 21-26. (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. Adaptive suppression of clutter // Cifrovaja obrabotka signalov. 2014. no. 4. pp. 32-37. (in Russian). 8. Popov D.I. Adaptivnije regektornjie filtrij kaskadnogo tipa // Cifrovaya obrabotka signalov. 2016. no. 2. pp. 53-56. (in Russian). 9. Popov D.I. Adaptive notch filter with real weights // Cifrovaya obrabotka signalov. 2017. no. 1. pp. 22-26. (in Russian). 10. Popov D.I. Optimizacja nerekursivnjih regektornjie filtrov s chastichnoj adaptaciej // Cifrovaya obrabotka signalov. 2018. no. 1. pp. 28-32. (in Russian). 11. Rabiner L., Gold B. Theory and application of digital signal processing. - Moscow: Mir, 1978. - 848 p. (in Russian). 12. Himmelblau D. Applied nonlinear programming. - Moscow: Mir, 1975 – 536 p. (in Russian).
2. Bartenev V. G. Radar reflections from the clear sky compel to improve the parameters of the radar / / Modern electronics. 2014. No. 7, C. 18-20. 3. Bartenev V. G. Application of the Wishart distribution for the analysis of the effectiveness of adaptive systems of SDTS / / Radio Engineering and Electronics. 1981. T. XXVI, No. 2, C. 356-361. 4. Bartenev V. G. Method of classification and blanking of discrete interference. Patent No. 2710894 on application No. 2018134712 registered in the State Register of the Russian Federation 14.01.2020. 5. Bartenev V. G. On the use of three signal signs for classification and blanking of discrete interfering reflections. 2020. No. 4. pp. 54-57. 6. Bartenev V. G. Model-oriented design of programmable radio engineering devices. Practical course// Hotline-Telecom, Moscow, 2019, C. 48-64. If you have any question please write: info@dspa.ru |