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Microphone array signal processing. – Springer, 2008. – 250 p. 11. Monzingo R. A., Haupt R. L., Miller T. W. Introduction to adaptive arrays, 2nd ed. – SciTech Publishing, 2011. – 510 p. 12. Djigan V. I. Adaptivnoe vyravnivanie amplitudno-chastotnyh harakteristik kanalov rasprostraneniya akusticheskih voln v zakrytyh pomeshcheniyah (Adaptive equalization of frequency response of acoustic wave propagation channels in closed rooms) // Problemy razrabotki perspektivnyh mikro- i nanoelektronnyh sistem (MES) (Problems of Perspective Micro- and Nanoelectronic Systems Development). – 2021. Vypusk 2. – S. 61–68. 13. Djigan V. I. Ekvalajzery s drobnoj zaderzhkoj i obratnoj svyaz'yu na baze bystryh RLS-algoritmov (Fractionally spaced feed-backward equalizers, based on fast RLS adaptive filtering algorithms) // Problemy razrabotki perspektivnyh mikro- i nanoelektronnyh sistem (MES) (Problems of Perspective Micro- and Nanoelectronic Systems Development). – 2020. – Vypusk 2. – S. 126–131. 14. Djigan V. I. Prediskaziteli signalov s pryamym obucheniem dlya usilitelej moshchnosti (Direct learning digital predistorters for power amplifiers) // Problemy razrabotki perspektivnyh mikro- i nanoelektronnyh sistem (MES) (Problems of Perspective Micro- and Nanoelectronic Systems Development). – 2020. – Vypusk 3. – S. 151–157. 15. Elliot S. J., Nelson P. A. Active noise control // IEEE Signal Processing Magazine. – 1993. – Vol. 10. – ¹ 4. – P. 12–35. 16. Makino S. Acoustic echo cancellation // IEEE Signal Processing Magazine. – 1997. – Vol. 14. ¹ 5. – P. 39–41. 17. Gay S. L., Benesty J., Eds. Acoustic signal processing for telecommunications. – Springer, 2000. – 333 p. 18. Albu I., Anghel C., Paleologu C. Adaptive filtering in acoustic echo cancellation systems – a practical overview //Proceedings of the 9-th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). – Targoviste, Romania, June 29 – July 1, 2017. – 6 p. 19. Dzhigan V. I. 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Fast affine projection algorithm // Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. – Detroit, MI, USA, May 9 – 12. – 1995. – Vol. 5. – P. 3023–3026. 29. Tanaka M., Kaneda Y., Makino S., Kojima J. A fast projection algorithm for adaptive filtering // IEICE Transactions. Fundamentals. – 1995. – Vol. E78A. – ¹ 10. – P. 1355–1361. 30. Djigan V. I. Improved fast affine projection algorithm with gradient adaptive step-size // Proceedings of the 3-rd International Conference on Antennas, Radiocommunication Systems & Means (ICARSM-97). – Votonezh, Russia, 26 – 29 May, 1997. – Vol. 3. – P. 23–32. 31. Djigan V. I. Multichannel fast affine projection algorithm with gradient adaptive step-size and fast computation of adaptive filter output signal // Proceedings of the 12-th IEEE East-West Design & Test Symposium (EWDTS-2014). – Kiev, Ukraine, September 26 – 29, 2014. – P. 87–92. 32. Djigan V. I. Application of affine projection algorithm in adaptive arrays // Proceedings of the IEEE 3-rd International Conference on Smart Technologies (UKRCON-2021). – Lviv, Ukraine, August 26 – 28, 2021. – P. 208–212. 33. Allen J. B., Berkley D. A. Image method for efficiently simulation small-room acoustics // Journal of Acoustical Society of America. – 1979. – Vol. 64. – ¹ 4. – P. 943–950. 34. Lehmann E. A., Johansson A. M. Prediction of energy decay in room impulse responses simulated with an image-source model // Journal of Acoustical Society of America. – 2008. Vol. 124. – ¹ 1. – P. 269–277. 35. Lehmann E. A., Johansson A. M. Diffuse reverberation model for efficient image-source simulation of room impulse responses // IEEE Trans. Audio, Speech, and Language Processing. – 2010. – Vol. 18. – ¹ 6. – P. 1429–1439. 36. Lehmann E. A. Fast simulation of acoustic room impulse responses // https://www.mathworks.com/matlabcentral/fileexchange/25965-fast-simulation-of-acoustic-room-impulse-responses-image-source-method?s_tid=srchtitle
It is noted that the SSAS generation procedure is similar to the convolutional code generation procedure, and the demodulation procedure is similar to the sequential decoding procedure of a convolutional code using the Viterbi algorithm. It is shown that the number of operations required to generate SSAS signals in modulators and processing operations of these signals in demodulators increases linearly with an increase in the duration of signals in SSAS. Therefore, the complexity of the technical implementation of communication systems with SSAS turns out to be comparable to the complexity of the implementation of communication systems which use two-dimensional signal ensembles such as QAM and APSK to increase the reliability of message reception. It is noted that the use of SSAS allows the creation of communication systems with high energy and spectral efficiency.
The aim of the work is to increase the computational efficiency of analysis algorithms and the accuracy of spectral estimation of radioengineering signals on the background of piecewise-stationary noises. Based on an estimate of the optimal value of the correction value based on an estimate of the noise power Experimental studies have shown that when using the proposed approach for spectral estimation, when compared with known autoregressive methods, it is possible to reduce the discrepancy between the control and estimated spectra by 7.4...9 times. When conducting a comparative analysis with a conventional autoregressive model, the decrease in the order of 2. Djuric PM. A MAP solution to off-line segmentation of signals. Proc of the international conference on acoustics, speech and signal processing. Adelaide, Australia, 1994. No. 4. pp. 505508. 3. Dobigeon N., Tourneret J-Y., Davy M. Joint segmentation of piecewise constant autoregressive processes by using a hierarchical model and a Bayesian sampling approach. IEEE Trans Signal Process, 2007. No. 4. Vol. 55. pp. 12511263. 4. Lavielle M. Optimal segmentation of random processes. IEEE Trans Signal Process. 1998. No. 5. Vol. 46. pp. 13651373. 5. Baevskij R.M., Kirillov O.I., Kleckin S.M. Matematicheskij analiz izmerenij serdechnogo ritma pri stresse. M.: Nauka, 1984, 221 p. 6. Basseville M., Nikiforov IV. Detection of abrupt changes: Theory and Application, Prentice-Hall, Englewood Cliffs, NJ, USA, 1993. 7. Baevskij R.M., Nikulina G.A. Holterovskoe monitorirovanie v kosmicheskoj medicine: analiz variabel'nosti serdechnogo ritma // Vestnik aritmologii. 2000. ¹ 16. pp. 616. 8. Andreev V.G. Optimizaciya avtoregressionnyx modelej meshayushhix radiootrazhenij // Izv. vuzov. Radioelektronika. 2008, vol. 51, no. 7, pp. 4047. 9. Koshelev V.I., Andreev V.G. Modelirovanie radiotehnicheskih signalov s uchetom ih fazovyh portretov // Cifrovaja obrabotka signalov i ee primenenie — DSPA 2008: tez. dokl. 10 Mezhdunar. konferencii i vystavki. No H-1. M.: Institut problem upravlenija RAN, 2008. pp. 418420. 10. Andreev V.G. Metod postroeniya modelej signalov s zadannymi amplitudno-fazovymi portretami. // Vestnik Ryazanskogo gosudarstvennogo radio-texnicheskogo universiteta. No. 1, Vypusk 31, Ryazan: RGRTU, 2010, pp. 1215. 11. Kolmogorov A.N., Proxorov Yu.V., Shiryaev A.N. Veroyatnostno-statisticheskie metody obnaruzheniya spontanno voznikayushhix e'ffektov // Tr. MIAN. 1988, vol. 182, pp. 423. 12. Savchenko V.V. Obnaruzhenie i prognozirovanie razladki sluchajnogo processa na osnove spektralnogo ocenivaniya // Avtometriya. 1996, vol. 2, pp. 7784. 13. Vorobejchikov S.E., Kabanova T.V. Obnaruzhenie momenta razladki processa avtoregressii pervogo poryadka // Vestnik Tomskogo Gosudarstvennogo Universiteta. 2003, vol. 280, pp. 170174. 14. Borovkov A.A. Ocenki momenta razladki po bol'shim vyborkam pri neizvestnyh raspredelenijah // Teorija verojatnostej i ee primenenija. 2008. vol. 53. Vypusk 3. pp. 437457. 15. Korkas, Karolos K., Fryzlewicz, Piotr Multiple change-point detection for nonstationary time series using wild binary segmentation. Statistica Sinica, 2017. No 1. Vol. 27. pp. 287311. 16. Rajs Dzh. R. Matrichnye vychisleniya i matematicheskoe obespechenie (Matrix calculations and software): per. s angl. O. B. Arushanyana. Moscow: Mir. 1984, 264 p.
This work is carried out within the framework of the study of processes occurring at different stages of seismic activity in the Kamchatka region. The source of information for the performed observations is the acoustic field. In particular, one of the indicators of seismic activity is a change in the characteristics of geoacoustic emission, which is generated by near-surface sedimentary rocks. Changes in these signals are connected with earthquake preparation. However, the study of these changes is associated with certain difficulties in processing and analyzing geoacoustic emission signals. These signals are flows of pulses of various shapes, durations, with different interpulse intervals. At the same time, these characteristics change noticeably over short time intervals in a wide range of values. In order to better understand the behavior of pulses in group dynamics, the authors proposed a stochastic model of the geoacoustic emission signal. The signal is recorded by a point receiver in a homogeneous and isotropic medium. The model parameters are three random variables. These are pulse amplitude, interpulse interval, and pulse duration. To analyze the proposed model, a technique for constructing three-dimensional graphs of the distributions of each of the parameters was developed. It makes it possible to reveal the features of their behavior over time. Approbation of the technique was carried out on an artificial signal with different laws of parameters distribution. Applying the developed technique to real data, the authors revealed anomalies in the parameters distribution of geoacoustic pulses flow. As a result, new knowledge was obtained about the effect of seismic processes on the acoustic field in near-surface sedimentary rocks. This work is carried out within the framework of the State task on the topic (2021-2023) «Physical processes in the system of near space and geospheres under solar and litospheric impact», registration number AAAA-A21-121011290003-0.
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Magomedov R.A., Musaev M.A. Otsenka seysmotektonicheskogo potentsiala blokov Vostochnogo Kavkaza (Assessment of the seismotectonic potential of the east caucasus blocks) // Geologiya i geofizika Yuga Rossii (Geology and geophysics of the south of Russia), 2021, vol. 11, no. 4, pp. 43–57. 8. Vladimirova I.S., Lobkovsky L.I., Alekseev D.A., Gabsatarov Y.V. Osobennosti seysmicheskogo protsessa v zapadnoy chasti Aleutskoy subduktsionnoy zony i ikh vozmozhnaya svyaz' s klimaticheskimi izmeneniyami v Arktike (Features of the seismic process in the western part of the Aleutian subduction zone and their possible relationship with climate changes in the Arctic) // Arktika: ekologiya i ekonomika (Arctic: ecology and economy), 2022, vol. 12, no. 1, pp. 58–67. 9. Marapulets Y., Rulenko O. Joint Anomalies of High-Frequency Geoacoustic Emission and Atmospheric Electric Field by the Ground–Atmosphere Boundary in a Seismically Active Region (Kamchatka) // Atmosphere, 2019, no. 10, art. no. 267. 16 p. 10. Gapeev M.I., Marapulets Yu.V. Modelirovanie zon otnositel'nykh sdvigovykh deformatsiy pered sil'nymi zemletryaseniyami na Kamchatke, proizoshedshimi v period 2018–2021 gg. (Modeling of relative shear deformation zones before strong earthquakes in Kamchatka from 2018–2021) // Vestnik KRAUNTs. Fiziko-matematicheskie nauki (Bulletin KRASEC. Physical and Mathematical Sciences), 2021, vol. 37, no. 4, pp. 53–66 11. Kuptsov A.V., Larionov I.A., Shevtsov Â.Ì. Osobennosti geoakusticheskoy emissii pri podgotovke kamchatskikh zemletryaseniy (Geoacoustic emission during the precursory periods of Kamchatka earthquakes) // Vulkanologiya i seysmologiya (Journal of Volcanology and Seismology), 2005, no. 5, pp. 45–59. 12. Gordienko V.A., Gordienko T.V., Krasnopistsev N.V. et al. Anomaliya vysokochastotnykh signalov geoakusticheskoy emissii kak operativnyy predvestnik zemletryaseniya // Akusticheskiy zhurnal (Acoustical Physics), 2008, vol. 54, no. 1, pp. 97–109. 13. Aleksandrov D.V., Dubrov M.N., Larionov I.A., Marapulets Y.V., Shevtsov B.M. Earth strain and acoustic monitoring of geodynamic processes in seismic and aseismic zones using highly sensitive, spatially separated instruments // Journal of volcanology and seismology, 2019, no. 3, pp. 72–80. 14. Malkin E.I., Lukovenkova O.O., Solodchuk A.A. i dr. Predseysmicheskie effekty v signalakh geoakusticheskoy i elektromagnitnoy emissii po dannym nablyudeniy na Kamchatke v 2013 godu // Teoriya i praktika sovremennykh gumanitarnykh i estestvennykh nauk. Vypusk 10: sbornik nauchnykh statey ezhegodnoy mezhregional'noy nauchno-prakticheskoy konferentsii, Petropavlovsk-Kamchatskiy, 10–14 fevralya 2020 g. / otv. red. M. A. Frizen, R. I. Parovik; KamGU im. Vitusa Beringa, 2020, pp. 130–133. 15. LukovenkovaO., MarapuletsY., SolodchukA. Adaptive Approachto Time-Frequency Analysis of AE Signals of Rocks // Sensors, 2022, vol. 24, art. no. 9798. 16. Nesterov S.V., Akulenko L.D., Gavrikov A.A. Determination of dynamic density of a granulated medium impregnated with a liquid // Doklady physics, 2011, vol. 436, no. 6, pp. 760–763. 17. Mountain Encyclopedia. Acoustic properties of rocks. www.miningenc.ru/a/akusticheskie-svojstva. 18.web-site www.astronet.ru/db/msg/1173309/ 19. Berezin Yu.A., Spodareva L.A. Propagation of acoustic signals in soils // Journal of applied mechanics and technical physics, 2001, vol. 42, no. 4, pp. 177–183. 20. Kan Sh.Ch, Mikulovich A.V., Mikulovich V.I. Analiz nestatsionarnykh signalov na osnove preobrazovaniya Gil'berta-Khuanga // Informatika, 2010,no. 2, pp. 25–35. 21. SvetlakovM., KovalevI., KonevA. etal. RepresentationLearningforEEG-BasedBiometricsUsingHilbert–HuangTransform // Computers, 2022, vol. 11, no. 3, art. no. 47. 22. 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A. I. Gladyshev^{1}, e-mail: tolyagladyshev@yandex.ru A. M. Telegin^{2}, e-mail: talex85@mail.ru E. A. Shchelokov^{2}, e-mail: riddick41666@mail.ru ^{1}Russian New University (RosNOU), Moscow, Russia ^{2}Samara University, Samara, Russia; Keywords:
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The application of the minimax principle to the magnitude of relative losses in marginal efficiency leads to the determination of a weight vector in each period of repetition, in which minimal losses are provided over the entire optimization range compared to optimal processing. The numerical results of optimization are given, from which it follows that the vector of weight coefficients in each period is asymmetric and varies from period to period. For different values of the noise/clutter ratio, the gains in marginal efficiency provided by a filter with optimized weighting coefficients compared to known non-optimized coefficients are determined. The principles of implementing an optimized filter based on a system function in the form of cascading inclusion of 1st and 2nd order links with time-variable weighting coefficients are considered, and a block diagram of the filter with partial adaptation to the Doppler phase of passive clutter and switching from period to period of weighting coefficients is presented. According to the criterion determining the effectiveness of interference suppression, a comparative analysis of the effectiveness of optimized filters was carried out and the volume of the training sample was estimated in case of their adaptation to the Doppler phase of clutter.
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