Digital Signal Processing 
Russian 
The comparative analysis of two methods combining signals in multichannel systems Abstract In many radio systems are widely used multichannel processing. In particular, when constructing a radar moving target detectors to improve their efficiency widely used the multichannel Doppler filters and notch multichannel filters. Thus, if in multichannel Doppler filters the target signal can appear in one of the Doppler channels, in the multichannel notch filters the target may be in all the channels, since the velocity transparency zone of channels coincide. In this context, in multichannel Doppler filter usually used the maximum selection in combining of channels. In the multinotch filters it is preferable to implement minimum selection in combining of channels. It is interesting to compare the characteristics of these two methods of channels combining in relation to a multichannel processing. Without loss of generality in solving the problem of comparing the effectiveness of two methods of channels combining the investigation was performed when type and efficiency of Doppler or multi notch filters in this study have been not considered. The attention was focused on different types of signal processing such as post detector integration, CFAR problem. It was assumed that at the input of minimum or maximum selection circuits in each channel was used in the quadrature square law detectors. And in each quadrature Gaussian noise acted with zero mean and unit variance. Noises in the channels were independent. Useful signals in all channels have the same random fluctuating amplitude and were also independent. It was shown, using analytical approach and MATLAB model verification of probability characteristics, that in the simple case the maximum detection method preferable. Using non coherent integration in each channel make both maximum and minimum methods equivalent. If after non coherent integration was used the adaptive constant false alarm rate device the minimum detection method become more affective for low detection probabilities. 2. Ryndyk, A.G. Ryabkov A.P. Multichannel notch filter with a minimum selection // News of Russian universities. Electronics. 2012. Vol. 4, p. 8185
. The research of the adaptive notch filter crosslinks’ impact on probing signals with intramodulation Keywords: : adaptive filter, spectral analysis, super resolution, the SteiglitzMcBride algorithm, chirpsignal, correlated interference, crosslinks, notch filter, MTI system, pulsecompression filter, Doppler filter, filter weights generation. References 2. Gordeev A.Yu., Bartenev V.G. Sposob adaptivnoj filtracii diskretnyx pomex. Zayavka na patent ¹ 201314267208 ot 19.09.13. Publikaciya FIPS v Byul. ¹9 ot 27.03.15. 3. Bartenev V.G. Adaptivnyj reshetchatyj filtr dlya podavleniya diskretnyx korrelirovannyx pomex. Doklad na 10j Mezhdunarodnoj konferencii «Cifrovaya obrabotka signalov i ee primenenie» DSPA2008. Moskva, 2628 marta, 2008,S.164168. 4. Bartenev V.G., Gordeev A.Yu. Primenenie metodov Proni i ShtejglicaMakBrajda dlya formirovaniya vesovyx koefficientov pri adaptivnoj filtracii neklassificirovannyx vyborok nablyudeniya. Trudy 14oj Mezhdunarodnoj konferencii «Cifrovaya obrabotka signalov i ee primenenie» DSPA2012. Moskva, 2012, S. 257260. 5. Bartenev V.G., Gordeev A.Yu. Novyj sposob formirovaniya vesovyx koefficientov pri adaptivnoj filtracii neklassificirovannyx vyborok nablyudenij // Cifrovaya obrabotka signalov. 2012, ¹2,S. 6567. 6. Bartenev V.G., Gordeev A.Yu. Sravnitelnyj analiz effektivnosti adaptivnoj filtracii po metodu Proni i ShtejglicaMakBrajda. // Sbornik trudov 61 NTK MIREA. 2012. Ch. 3. S. 5560. 7. Bartenev V.G., Gordeev A.Yu. Novyj sposob razrabotki dvuxchastotnogo klassifikatora diskretnyx korrelirovannyx pomex. // Sbornik dokladov Vserossijskoj konferencii RSPOVI 2013. Smolensk, 2013, S. 196199. 8. Gordeev A.Yu. Sravnitelnyj analiz effektivnosti razlichnyx metodov spektralnogo analiza sverxvysokogo razresheniya dlya filtracii neklassificirovannyx vyborok nablyudeniya. // Materialy 69j Mezhdunarodnoj konferencii «Radioelektronnye ustrojstva i sistemy dlya infokommunikacionnyx texnologij» REDS2014. Moskva, 2014. S. 3741. 9. Gordeev A.Yu., Yacyshen V.V. Perspektivnye metody povysheniya effektivnosti podavleniya passivnyx pomex sistemami selekcii dvizhushhixsya celej // Elektromagnitnye volny i elektronnye sistemy. 2015, T.20, ¹3. S. 4052. 10. Marple S. L. Digital Spectral Analysis with Applications: 1990. – 584 p. 11.Sergienko A. B. Digital Signal Processing. SPb.: BXVPeterburg, 2011. – 768 p. 12. Burg I. P. Maximum Entropy Spectral Analysis. Proc. 37th Meeting of the Society of Exploration Geophysicists. Oklahoma City, Okla., Îctober 1967. 13. Parks, Thomas W., and C. Sidney Burrus. Digital Filter Design. New York: John Wiley & Sons, 1987, pp 226–228. 14. Steiglitz, K., and L. E. McBride. "A Technique for the Identification of Linear Systems." IEEE® Transactions on Automatic Control. Vol. AC10, 1965, pp. 461–464. 15. Ljung, Lennart. System Identification: Theory for the User. 2nd Edition. Upper Saddle River, NJ: Prentice Hall, 1999, p. 354.
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Abstract 2. Antoniou A. Digital Filters: Analysis and Design. New York: McGraw Hill Higher Education, 1979. 524 p. 3. Cappellini V., Constantinides A. G., Emiliani P. Digital filters and their applications. London: Academic Press, 1978. 393 p. 4. Lim Y. C., Parker S. R. A discrete coefficient FIR digital filter design based upon an LMS criteria. Proc. IEEE ISCAS.1982.p. 796799. 5. Lim Y. C., Parker S. R., Constantinides A. G. Finite word length FIR filter design using integer programming over a discrete coefficient space //IEEE Trans.1982.Vol. ASSP30, ¹ 4. p. 661664. 6. Siohan P., Benslimane A. Design of optimal finite wordlength linear phase FIR filters: New applications//Proc. IEEE ICASSP. 1984.p. 30.1.130.1.4. 7. Artemev V.V., Bugrov V.N. IIR filter design with phase linearity. Moscow, Components and technologies, ¹ 7, 2013, p. 132134. 8. Bugrov V.N. Development of digital filters by methods of integer nonlinear programming. // Vestnik Newsletter NNSU, 2009, ¹ 6. p. 61 – 70. 9. Shkelev E.I., Bugrov V.N., Proidakov V.I., Artemev V.V. Integer digital filters  effective solution for 8bits digital platforms. Moscow, Components and technologies, ¹ 10, 2013, p. 104 – 110. 10. Voinov B.S., Bugrov V.N., Voinov B.B. Informacionnie tekhnologii i sistemi: poisk optimalnih, originalnih i racionalnih resheniy. Moscow.: Science, 2007, 730 p. 11. Semenov B.Y. MSP430 MCU. the first acquaintance., Ì.: «Solonpress», 2006, 120 p. 12. Lemm G. Analog and digital filters. . Moscow.: Mir, 1990, 590 p.
Abstract Recently the interest to empirical modes decomposition has been growing. The authors apply such decomposition as a signal preliminary processing, which allows increasing the signal/interference ratio, simplifying the algorithm of parametrical analysis due to transformation of a complex task of evaluation of the parameters of p model order into simple tasks of evaluation of components of first and second order, significantly reducing the period of analysis. The method proposed is based on suppression of highfrequency components at integration and on their accentuation at differentiation:  to extract the modes in ascending order of their frequencies multiple integration of signal is executed in order to suppress highfrequency components, till the termination of altering of number of extrema, i.e. only one (of the lowest frequency) component remains; the modes are extracted from integrated sequences by differentiation, subtraction of extracted component from integrated sequences of lower order, repetition of the same actions with an already withdrawn lowfrequency component with the sequences integrated, starting with the previous one; the components extracted from the integrated sequences are to be differentiated in accordance with Lanczos scheme as many times as the sequence has been integrated;  to extract the modes in the descending order of their frequencies multiple differentiation is executed to accentuate highfrequency components, till the sequence with alternating extrema is extracted; the modes are extracted from differentiated sequences by integration, subtraction of the extracted component from differentiated sequences of lowerorder, repetition of the same actions with an already withdrawn highfrequency component with differentiated sequences, starting with the previous one; the components extracted from the differentiated sequences are to be integrated with application of weighting as many times as the sequence has been differentiated. References 2. Myasnikova, N. V. , Timefrequency distributions on the basis of extreme filtering/ N. V. Myasnikova, M.P. Beresten // Sensors and systems. – 2013. – ¹ 10. – page 9–12. 3. Myasnikova N.V. Empirical modes decomposition on the basis of extreme filtration // Myasnikova N.V., Beresten Ì.P. Digital signal processing. 2014. ¹ 4. Pages 1317. 4. Marple, S.L., Jr., Digital Spectral Analysis with Applications, 584 p., Mir, Moscow, 1990. 5. Myasnikova, N.V. The algorithm of extraction of lowfrequency modes / N.V. Myasnikova, Ì.P. Beresten // The 17th International conference ‘Digital signals processing and its application». Moscow. March 25–27, 2015. The works of the Russian scientific and technical society of radio engineering, electronics and communication named after A.S.Popov. Digital signals processing and its application. Series. – Moscow.: Russian scientific and technical society of radio engineering, electronics and communication named after A.S.Popov, 2015. – Pages. 7882. 6. Myasnikova, N.V. Empirical modes decomposition on the basis of differentiation and integration / N.V. Myasnikova, Ì.P. Beresten / Collected articles: Perspective information technologies (PIT 2015) The works of International scientific and technical conference. Samara State Aerospace University. Samara, 2015. – Pages 101105. 7. Myasnikova, N.V. The combined method of Empirical modes decomposition on the basis of differentiation and integration / N. V. Myasnikova, M.P. Beresten, A.A. Primak // Modern society, science and education: collection of scientific papers on the materials of the International scientific  practical conference in 16 parts . 31 March 2015. Part 8 Publisher: OOO "Consulting company Ucom " ( Tambov ) p. 76–77.
Abstract Optimize energy criterion of coherent signal detection system is based on the extreme properties of the characteristic (own) matrices of numbers, and probabilistic criteria  on numerical methods of nonlinear programming. A twostage procedure to optimize systems for detection of coherent signals based on the RFMF combination. In the first stage on the energy or the probability criterion is optimized RF. The second step is optimized MF. In the case of a different weighting in the channels used analytical procedure optimization energy criterion of maximum ratio Rayleigh, which is an approximate version of optimization on probabilistic criterion, and in the case of the same weighting in the channels of the methods of nonlinear programming is the numerical solution of probabilistic criterion. Analysis of processing systems may also be carried out on the energy and probabilistic criterion. For a small dynamic range for clutter detection systems fixed coherent structure signals a preference should be given to the method of optimization on probabilistic criterion. By increasing the dynamic range of a convergence of parameters and efficiency of systems in comparable cases, that in view of the facilities of the analytical solutions of the optimization problem, as well as more opportunities for implementation of adaptive algorithms indicates the usefulness of the method of optimization on energy criterion. References 2. Radar Handbook / Ed. by M. I. Skolnik.  3rd ed.  McGraw–Hill, 2008.  1352 p. 3. Popov, D.I. Optimalnaya obrabotka mnogochastotnjch signalov // Izvestiya vuzov Russia. Radioelektronika.  2013.  Vjup. 1.  S. 3239. 4. Popov, D.I. Optimizaciys system kogerentnovesovoi obrabotki mnogochastotnjch signalov // Cifrovaya obrabotka signalov. – 2013.  ¹ 4.  S. 1721. 5. Popov, D.I. Adaptaciya nerekuesivnjch reghectornjch filtrov // Izvestiya vuzov. Radioelektronika.  2009.  T. 52. ¹ 4.  S. 4655. 6. Popov, D.I. Sintez adapnjch reghectornjch filtrov visokich porjadkov // Izvestiya vuzov. Radioelektronika.  1999.  T. 42. ¹ 6.  S. 4651. 7. A. s. 934816 SSSR, MPK6 G 01 S 7/36, G 01 S 13/52. Reghectornij filtr / D.I. Popov; zayavl. 30.10.1980; opubl. 27.11.1998, Byul. ¹ 33.  20 s. 8. Popov, D.I. Ocenivanie parametrov passivnych pomeh // Izvestiya vuzov. Radioelektronika.  2003.  T. 46. ¹ 3.  S. 7180. 9. Popov, D.I. Avtokompensaciya doplerovskoi fazy passivnyh pomeh // Cifrovaya obrabotka signalov. – 2009.  ¹ 2.  S. 3033. 10. A. s. 875960 SSSR, MPK6 G 01 S 7/36, G 01 S 13/52. Ustroistvo dlya podavleniya passivnyh pomeh / D.I. Popov; zayavl. 07.01.1980; opubl. 27.11.1998, Byul. ¹ 33.  11 s. 11. A. s. 1015757 SSSR, MPK6 G 01 S 7/36. Ustroistvo podavleniya passivnyh pomeh / D.I. Popov; zayavl. 05.12.1977; opubl. 27.11.1998, Byul. ¹ 33.12 s. 12. A. s. 1098399 SSSR, MPK6 G 01 S 7/36. Ustroistvo adaptivnoi rezhekcii passivnyh pomeh / D.I. Popov; zayavl. 12.06.1981; opubl. 20.12.1998, Byul. ¹ 35.  16 s. 13. Popov, D.I. Adaptivnoe podavlenie passivnyh pomeh // Cifrovaya obrabotka signalov. – 2014.  ¹ 4.  S. 3237. 14. Popov, D.I. Adaptivnye porogovye ustroistva // Izvestiya vuzov. Radioelektronika.  2006.  T. 49. ¹ 3.  S. 3035.
Abstract In this paper we analyze the structure of a radio signal with OFDM modulation and conduct a study (research) to reduce the Peak to Average Power Ratio (PAPR) by the use of some of the carrier frequencies (Tone reservation), by extending some modulation constellation points toward the outside and around of the constellation (fixed and adaptive  Active Constellation Extension and Adaptive Active Constellation Extension). As a result of joint research and such methods of processing, PAPR of OFDM signals is reduced significantly (about 5 dB) and the efficiency of using output amplifiers of the transmitting means is improved. 2. Korzhihin E.O, Vlasyuk I.V, Methods for reducing the crest factor in the terrestrial digital television broadcast system standard DVBT2 //T Comm Mobile communications systems and digital broadcasting. Release on the results of the 6th industry conference MTUCI "Information Society Technologies", Ì.: « Media Publisher » – 2012 . – ¹ 9–p.8386. 3. ETSI EN 302 755 V1.3.1 (201204) Digital Video Broadcasting (DVB); Frame structure channel coding and modulation for a second generation digital terrestrial television broadcasting system (DVBBT2). 4. Tellado, J., Cioffi, J.M., PAR Reduction in Multicarrier Transmission Systems. 5. DaeWoon Lim, HyungSuk Noh, JongSeon No, Near Optimal PRT Set Selection Algorithm for Tone Reservation in OFDM Systems // IEEE Transactions On Broadcasting, Vol. 54, No. 3, September 2008. 6. Grace R. Woo, Douglas L. Jones, Peak Power Reduction in MIMO OFDM via Active Channel Extension // IEEE 2636  2639 Vol. 4,1620 May 2005 7. Madhuri P., Dr Malleswari B. L., PeakToAverage Power Ratio Reduction by CBACE and Adaptive Ace Algorithms // ISSN 22502459, Volume 2, Issue 2, February 2012. 8. G.Karthikeyan, Dr.G.Indumathi, S.Kannadhasan, PAPR Reduction in OFDM Systems using Adaptive Active Constellation Extension Algorithm // ISSN 2320 – 9798, Vol. 1, Issue 4, June 2013.
An algorithm for the received BPSK signal quality assessing is developed. It allows to distinguish the signal constellation distortion effects and AWGN acting on the signal. Since the power of the received signal does not depend on signal constellation rotation, the distortion identification using the reference metric is difficult. The power control system does not effective in this case. It is necessary to use the nonreference metrics such as EVM. The main feature of the algorithm is the use of a received signal quality mixed metric, which includes the elements of the standard metric (BER) and the nonreference metric (EVM). The proposed algorithm makes possible to distinguish the effect of AWGN signal constellation and compensate it by analyzing the error vector. Another important feature of this algorithm is its ability to compensate the distortions introduced by the analog RF path that now is an actual task in mind of reducing the quality of the electronic components in the domestic market. 2. Kenington P.B. RF and Baseband Techniques for Software Defined Radio / Artech House, 2005. 352 p. 3. Software Defined Radio. Edited by Walter Tuttlebee. John Wiley & Sons, Ltd 2002. 402 p.1 4. Lam G. Digital and analog filters. Theory and design. Moscow. Mir, 1982. 586 p. 5. Mathuranathan V. Simulation of Digital Communication Systems Using Matlab. Second edition. / Mathuranathan V. Ebook, Mathuranathan V. at Smashwords, Published at 2013. 6. Umar H. Rizvi, Gerard J. M. Janssen and Jos H. Weber. BER Analysis of BPSK and QPSK Constellations in the Presence of ADC Quantization Noise // Proceedings of APCC2008, Kioto, Japan 2008 IEICE 08 SB 0083. 7. Amin A. Computation of BitError Rate of Coherent and NonCoherent Detection MAry PSK With Gray Code in BFWA Systems // International Journal of Advancements in Computing Technology, Vol. 3, Nu 1, February 2011. 8. Martirosov V.Å. Optimal receive in digital communication systems. Moscow. Radiotehnika, 2010. 208 p. 9. Chile C.M. Bounds and Approximations for Rapid Evaluation of Coherent MPSK Error Probabilities // IEEE Trans. Commun., Vol/ COM33, pp. 271–273, March 1985. 10. Sklyar B. Digital communications. Williams, 2007. 1104 p. 11. McKinley M.D. EVM Calculation for Broadband Modulated Signals // 64th ARFTG Conf. Dig., Orlando, Florida. 2004. pp. 45–52. 12. Hassun R., Flaherty M., Matreci R., and Taylor M. Effective evaluation of link quality using error vector magnitude techniques. In Wireless Communications Conference, 1997. 13. Jensen T.L., & Larsen T. (2013). Robust Computation of Error Vector Magnitude for Wireless Standards // IEEE Trans. Commun., 61(2), 648–657. 10.1109/TCOMM.2012.022513.120093. 14. Dubov Ì.À., Priorov À.L. Metodika neetalonnoi otsenki sootnoshenia signal/shum i veroyatnosti bitovoi oshibki // DSPA. 2012. ¹ 4. P. 37–43. 15. Zivkovic M. and Mathar R. Preamblebased SNR estimation in frequency selective channels for wireless OFDM systems. In IEEE VTC 2009, 2009. 16. Georgiadis A. Gain, phase imbalance, and phase noise effects on error vector magnitude // IEEE Transactions on Vehicular Technology, 53(2):443–449, 2004. 17. Schmogrow R., Nebendahl B., Winter M., Josten A., Hillerkuss D., Koenig S., Meyer J., Dreschmann M., Huebner M., Koos C., Becker J., Freude W., and Leuthold J. Error vector magnitude as a performance measure for advanced modulation formats // Photonics Technology Letters, IEEE, 24(1): 61–63, Jan 2012. 18. Rao C.R. Handbook of statistics. Vol. 24. Data mining and data visualization. ELSEVIER B.V., 2005. 644 p. 19. Martushev Yu. Yu. Digital modeling of radio devices. Practical experience. Moscow. Goryachaya liniya – Telekom, 2012. 188 p. 20. Goldsmith A. Wireless communications. Stanford University, 2004. 419 p.
Abstract The problem is connected with a priori unknown apparatus function (AF) of the radiometer. Such function (AF) characterizes influence of the directional pattern of the antenna, path of preprocessing of the radiometer and external factors on the required image of objects. In actual practice operation of the radiometer the value of AF will differ from its value measured in laboratory conditions owing to blurring of the AF form that also reduces efficiency of the radiometer. The purpose of work is development of algorithms for estimation of the unknown AF in the conditions of aprioristic uncertainty. The objective is achieved by the solution of the following task: development of new algorithms of AF estimation allowing to increase the accuracy of AF estimates in real practice of supervision due to application of optimum methods of estimation on the basis of radio thermal image standard. The largest accuracy of estimation of AF was shown by modification of the algorithm based on a matrix method and answering to criterion of an optimality. At the same time application of this algorithm demands knowledge of the standard image that in actual practice it isn't always feasible. Another algorithm in which the initial description of AF is given on the basis of the antenna pattern which characteristic is usually known is represented to more realistic. However automation of selection of the AF parameters in the course of recovery of the image is complicated at the small relation a signalnoise and demands participation of the person operator. The offered algorithms can find application in the existing radiometric systems of microwave range, and also in the optical systems of the Infrared range intended for detection and recognition of objects according to their restored image. 2. Vasilenko G. I., Taratorin A.M. Recovery of images. M.: Radio and communication, 1986. 304 pp. 3. Klochko V. K. Recovery of object’s mages in the conditions of atmospheric distortions // the Bulletin of the Ryazan state radio engineering university, no. 33. 2010, pp. 24 – 28. 4. Klochko V. K., Kuznetsov V.P. Recovery of object’s images on the thinnedout matrix of observations // the Bulletin of the Ryazan state radio engineering university. 2016, no. 55, pp. 111 – 117. 5. Gonsalez R., Woods R., Eddins S. Digital image processing in the environment of MATLAB. M.: Technosphere, 2006. 616 pp. 6. Konewhov A.L., Kostevich A.G., Kuryachy M. I. Determination of function of dispersion of a point by characteristic fragments of images // Log "Reports of Tomsk State University of Management Systems and Radiotronics" No. 26, part 1, 2012, pp. 116 – 120. 7. Pirogov Yu.A., Timanovsky A.L. Superpermission in systems of passive radiovision of the millimetric range / Radio technician, 2006, no. 3, pp. 14 – 19. 8. Voskoboynikov Yu.E. Combined nonlinear algorithm of recovery of contrasting images in case of inaccurately given hardware function // Avtometriya. 2007, no. 6, pp. 3 – 16. 9. Passive radiolocation: methods of object’s detection / Under the editorship of R.P. Bystrov and A.V. Sokolov. M.: Radio engineering. 2008. 320 pp.
Abstract 2. Vasilev V. A., Popova T.S., Tropskaya N.S. Ocenka dvigatelnoj aktivnosti organov zheludochnokishechnogo trakta // Rossijskij zhurnal gastroenterologii, gepatologii, koloproktologii. 1995, ¹ 4, s. 4854. 3. Lebedev N.N. Biorhythms of digestive system. – M.: Medicine, 1987.256p. 4. Tropskaya N.S., Popova T.S. Nekotorye aspekty regulyacii motornoj funkcii zheludka i tonkoj kishki // Klinicheskie perspektivy gastroenterologii, gepatologii, koloproktologii. 2008. ¹4. 1216. 5. Groh W. J. et al. Computerized analysis of spikeburst activity of the upper gastrointestinal tract //Digestive diseases and sciences. – 1984. – Ò. 29. – ¹. 5. – Ñ. 422426. 6. Husebye E., Hellstrom P.M., Sundler F., Chen J., Midtvedt T. Influence of microbial species on small intestinal myoelectric activity and transit in germfree rats. Am. J. Physiol. Gastrointest. Liver Physiol. 2001; 280 (3): G368–G380. 7. Sarna S. Myoelectrical and Contractile Activities of the Gastrointestinal Tract / In: Schuster M.M., Crowell M.D., Kenneth L.K. Schuster Atlas of Gastrointestinal Motility in Health and Desease.  London: BC Decker Inc., Hamilton, 2002. P. 118. 8. Stam R. et al. Computer analysis of the migrating motility complex of the small intestine recorded in freely moving rats //Journal of pharmacological and toxicological methods. – 1995. – Ò. 33. – ¹. 3. – Ñ. 129136. 9. Van Schelven L. J., Nieuwenhuijs V. B., Akkermans L. M. A. Automated, quantitative analysis of interdigestive small intestinal myoelectric activity in rats //Neurogastroenterology & Motility. – 2002. – Ò. 14. – ¹. 1. – Ñ. 1523. 10. Yakovlev V. G. The algorithm for detection of peaks in physiological curves //Avtomatika i Telemekhanika. – 1977. – ¹. 12. – Ñ. 94105.
Abstract 7. http://www.sinapseinstitute.org/projects/neurochip/
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