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“Digital
Signal Processing” No. 1-2025 |
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Detection Characteristics of the Two-Channel Displaced Phase Center Antenna MTI System
V.V. Kostrov, e-mail: kostrov.46@mail.ru
K.K. Khramov, e-mail: hramovkk@gmail.com
Murom Institute of Vladimir State University, Russia, Murom
Keywords: synthetic aperture radar (SAR), moving target indication (MTI), displaced phase center antenna, digital two-channel interferometer, radial velocity, detection characteristics.
Abstract
One of the effective methods of moving target indication (MTI) in space-borne synthetic aperture radars (SARs) is the displaced phase center antenna (DPCA) technique. Such systems use an interferometer with antennas that are displaced along the path of the SAR vehicle. The paper describes the MTI algorithm based on a digital two-channel interferometer. The simulation was performed for a space-borne X-band SAR. Results of processing holograms formed in receiving channels by trajectory signals of stationary and moving point targets are obtained. The moving targets have different constant radial velocities. It was found that at the output of the MTI system the response of a stationary object was compensated, and the responses of moving objects are present. Their amplitude and azimuth position depend on the radial velocity. Thus, at the output of the MTI system it is possible to perform threshold selection of a moving target.
Graphs of the change in the amplitude and position of the response of a moving target on a radar image are given, as well as graphs of the phase difference of the input signals of the MTI system are given. As follows from the simulation results, range migration correction in high-resolution space SARs leads to an additional shift in the response of a moving target in range. Such change in the position of a radially moving target causes blurring of the response and decrease in its amplitude on the radar image. It was also found that the phase difference of the output signals of the receiving channels depends linearly on the radial velocity of the target. The linearity of the functions of the azimuthal position of the response of a moving target to the radar imagery and the phase difference of the input signals of the MTI system allows them to be used to estimate the radial velocity of targets.
Statistical modeling of a two-channel displaced phase center MTI system is performed. The modeling was performed using a detection algorithm with a constant false alarm rate (CFAR processor). In this case, the stabilization of the false alarm probability is achieved by adaptive threshold formation based on the analysis of pixels surrounding the analyzed element of the radar image. Radar images were processed using the CFAR processor at different false alarm probability values. Moving target detection maps were obtained. It is shown that as the detection threshold decreases, the probability of detection increases, but false targets appear.
The detection characteristics of the MTI system were obtained. Analysis of these characteristics showed that with a constant signal-to-noise ratio and an increase in the radial velocity of the target, its probability of detection increases. The minimum signal-to-noise ratio that must be ensured at the input of MTI system based on a digital two-channel interferometer is about 22 dB.
References
1. Gruzdov V.V., Kolkovsky Yu.V., Krishtopov A.V., Kudrya A.I. Novye tekhnologii distancionnogo zondirovaniya Zemli iz kosmosa (New technologies for remote sensing of the Earth from space). M.: Tekhnosfera, 2019. 482 p.
2. Aviacionnye sistemy radiovideniya (Aviation radio vision systems) / Ed. by G.S. Kondratenkov. M: Radiotechnika, 2015. 648 p.
3. W. Xu, Y. Chen, P. Huang, W. Tan, Y. Qi, “Using the Displaced Phase Center Azimuth Multiple Beams Technique with Spaceborne Synthetic Aperture Radar Systems for Multichannel Reconstruction of Accelerated Moving Targets,” Electronics. 2023; 12(24):4954. https://doi.org/10.3390/electronics12244954
4. Verba V.S., Neronskyi L.B., Osipov I.G., Turuk V.E. Radiolokacionnye sistemy zemleobzora kosmicheskogo bazirovaniya (Space-borne Earth Surveillance Radar Systems). Ed. by Prof. V.S. Verba. M.: Radiotechnika, 2010; 680 p.
5. G. Krieger, N. Gebert and A. Moreira, “Unambiguous SAR signal reconstruction from nonuniform displaced phase center sampling,” in IEEE Geoscience and Remote Sensing Letters, vol. 1, no. 4, pp. 260-264, Oct. 2004, doi: 10.1109/LGRS.2004.832700.
6. Popov D.I. Izmerenie radial'noj skorosti celi (Measuring the radial velocity of a target) // Izvestiya vuzov Rossii. Radioelektronika. 2015, Issue 1. P.53-59.
7. K. Suwa, K. Yamamoto, M. Tsuchida, S. Nakamura, T. Wakayama, and T. Hara “Image-Based Target Detection and Radial Velocity Estimation Methods for Multichannel SAR-GMTI,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 3, pp. 1325-1338, March 2017, doi: 10.1109/TGRS.2016.2622712.
8. Vityazev V.V., Kolod’ko G.N., Vityazev S.V. Selekciya nazemnyh dvizhushchihsya celej na osnove mnogoskorostnoj adaptivnoj obrabotki traektornogo signala (Selection of ground moving targets based on multi-speed adaptive processing of trajectory signal) // Cifrovaya obrabotka signalov (Digital signal processing). 2007, no. 1, pp. 41-50.
9. V.V. Kostrov and K.K. Khramov, “Statistical Characteristics of the Moving Target Indication in Space-borne Interferometry Synthetic Aperture Radar,” 2019 Dynamics of Systems, Mechanisms and Machines (Dynamics), Omsk, Russia, 2019, pp. 1-6, doi: 10.1109/Dynamics47113.2019.8944594.
10. El-Darymli, Khalid, et al. “Target detection in synthetic aperture radar imagery: A state-of-the-art survey,” Journal of Applied Remote Sensing 7.1 (2013): 071598-071598, doi: 10.1117/1.JRS.7.071598.
11. Sosulin Yu.G., Kostrov V.V., Parshin Yu.N. Ocenochno-korrelyacionnaya obrabotka signalov i kompensaciya pomekh (Estimation-correlation signals processing and interferences compensation). M.: Radiotekhnika, 2014. 632 p.
12. V. Kostrov, E. Tolstov and K. Khramov, “Automatic Detection of Thread-like Objects in SAR Using a Modified Radon Transform,” 2024 26th International Conference on Digital Signal Processing and its Applications (DSPA), Moscow, Russian Federation, 2024, pp. 1-6, doi: 10.1109/DSPA60853.2024.10510127.
13. Likharev V.A. Cifrovye metody i ustrojstva v radiolokacii (Digital methods and devices in radar). M.: Soviet Radio, 1973. 456 p.
Study of the Efficiency of Digital Predistortion Based on the Polynomial Memory Model and the Levenberg-Marquardt Algorithm for Power Amplifiers
Hoang Van Tuan, e-mail: khoang.vt@phystech.edu
Dvorkovich A.V., e-mail: dvork.alex@gmail.com
Department of Multimedia Technologies and Telecommunications, MIPT, Russia
Keywords: Digital Predistortion, Power Amplifier, Levenberg–Marquardt Algorithm, Memory Polynomial Model, OFDM.
Abstract
This article investigates the effectiveness of digital predistortion (DPD) for linearizing power amplifiers (PA) in systems employing OFDM signals. As wireless systems demand higher data rates and broader bandwidths, PA linearity and efficiency become increasingly important. Positioned at the final stage of the transmitter, the PA amplifies signals for transmission but often introduces nonlinear distortion, especially near saturation. These distortions degrade in-band quality and cause out-of-band emissions, making effective PA linearization essential for ensuring signal integrity and regulatory compliance.
Among modern modulation techniques, OFDM stands out due to its high spectral efficiency and robustness against multipath fading, making it a core technology in standards such as DVB-T2, LTE, and 5G…. However, OFDM signals inherently suffer from high peak-to-average power ratio (PAPR), which makes them particularly vulnerable to nonlinear distortions introduced by PAs. These distortions not only degrade signal quality but also result in spectral regrowth, violating emission masks and causing adjacent channel interference. The situation becomes even more challenging when the amplifier exhibits memory effects, which are prominent in broadband and high-frequency operations. Addressing these challenges is essential for ensuring both spectral compliance and system reliability in next-generation wireless networks.
To address this issue, the study applies a memory polynomial (MP) model combined with Levenberg–Marquardt (LM) and BFGS algorithms to estimate and adaptively optimize the DPD parameters. The article presents a detailed description of the indirect learning architecture (ILA) for adaptive predistortion and compares the performance of several optimization algorithms, including LM, BFGS, and RLS. The modeling results show that the LM- and BFGS-based algorithms significantly outperform RLS in terms of normalized mean square error (NMSE) and adjacent channel power ratio (ACPR). Simulation with 16-QAM OFDM signal demonstrates that the proposed approach enables effective compensation of both nonlinear distortion and memory effects, resulting in improved spectral efficiency and amplifier linearity.
References
1. Kadhim A. A., Mohammed S. J., Al-Gayem Q. Digital Video Broadcasting T2 Lite Performance Evaluation Based on Rotated Constellation Rates //Journal of Internet Services and Information Security. – 2023. – Ò. 13. – ¹. 3. – Ñ. 127-137.
2. Hong Z. H. et al. In-band full-duplex communications in ATSC 3.0 single frequency network //IEEE Transactions on Broadcasting. – 2023. – Ò. 69. – ¹. 2. – Ñ. 560-568.
3. Behdad N. et al. PAPR Reduction in OFDM System Using Metaheuristic Algorithm //Full Length Article. – 2023. – Ò. 4. – ¹. 2. – Ñ. 28-8-35.
4. Abed G. A. A New Approach to Improve Transmitting and Receiving Timing in Orthogonal Frequency Division Multiplexing (OFDM) Systems //Iraqi Journal For Computer Science and Mathematics. – 2023. – Ò. 4. – ¹. 2. – Ñ. 83-96.
5. H. Jiang et al., "Aggressive Look-Up Table Compression by Princióìl Component Analysis for Digital Pre-Distortion in Optical Communication," in Journal of Lightwave Technology, vol. 40, no. 24, pp. 7718-7726, 15 Dec. 2022.
6. J. Ren, "Digital Predistorter for Short-Wave Power Amplifier With Improving Index Accuracy of Lookup Table Based on FPGA," in IEEE Access, vol. 7, pp. 182881-182885, 2019, doi: 10.1109/ACCESS.2019.2960092.
7. L. Sun et al., "A Low Complexity LUT-Based Digital Predistortion Block With New Pruning Method," in IEEE Microwave and Wireless Components Letters, vol. 32, no. 9, pp. 1131-1134, Sept. 2022, doi: 10.1109/LMWC.2022.3163890.
8. J. Lv, G. Xu, C. Zhang, H. Zhang and T. Liu, "Full-Wave Digital Predistortion Linearization Based on Memory Polynomials for HF Power Amplifiers," in IEEE Access, vol. 12, pp. 29362-29372, 2024, doi: 10.1109/ACCESS.2024.3368858.
9. J. Ren, "A New Digital Predistortion Algorithms Scheme of Feedback FIR Cross-Term Memory Polynomial Model for Short-Wave Power Amplifier," in IEEE Access, vol. 8, pp. 38327-38332, 2020, doi: 10.1109/ACCESS.2020.2976152.
10. Ahmed, Serien, Majid Ahmed, Souheil Bensmida, and Oualid Hammi. 2024. "Power Amplifier Predistortion Using Reduced Sampling Rates in the Forward and Feedback Óìths" Sensors, vol. 24, no. 11: 3439. https://doi.org/10.3390/s24113439.
11. Somasundaram, Gokul, Jill C. Mayeda, Clint Sweeney, Donald Y. C. Lie, and Jerry Lopez. 2023. "Effective Digital Predistortion (DPD) on a Broadband Millimeter-Wave GaN Power Amplifier Using LTE 64-QAM Waveforms" Electronics, vol. 12, no. 13: 2869. https://doi.org/10.3390/electronics12132869.
12. S. Boumaiza and F. Ghannouchi, “Thermal memory effects modeling and compensation in RF power amplifiers and predistortion linearizers,” IEEE Trans. Microw. Theory Tech., vol. 51, pp. 2427-2433, Dec. 2003.
13. Abdelrahman AE, Hammi O, Kwan AK, Zerguine A, Ghannouchi FM (2016) A novel weighted memory polynomial for behavioral modeling and digital predistortion of nonlinear wireless transmitters. IEEE Trans Industrial Electron 63(3):1745–1753
14. Le D. H. et al. Linearization of RF power amplifiers in wideband communication systems by adaptive indirect learning using RPEM algorithm //Mobile Networks and Applications. – 2020. – Ò. 25. – Ñ. 1988-1997.
15. Hussein MA, Bohara VA, Venard O (2012) On the system level convergence of ILA and DLA for digital predistortion. In: Proc. 2012 international symposium on wireless communication systems (ISWCS), pp 870–874.
16. Chani-Cahuana J, Landin PN, Fager C, Eriksson T (2016) Iterative learning control for RF power amplifier linearization. IEEE Trans Microw Theory Tech 64(9):2778–2789.
17. Vu V. K., Tran V. N. Adaptive digital predistortion of RF power amplifiers based on memory polynomial model and indirect learning architecture //2020 International Conference Engineering and Telecommunication (En&T). – IEEE, 2020. – Ñ. 1-5.
Convolutional neural network and a network with vision transformer architecture accuracy comparison in radar images classification task
Kupryashkin I.F., e-mail: ifk78@mail.ru
Military Educational and Scientific Center of the Air Force "N.E. Zhukovsky and Y.A. Gagarin Air Force Academy" (Voronezh)
Keywords: deep convolutional neural network, transformer, radar image, classification accuracy.
Abstract
The paper presents comparative results of MSTAR objects noisy radar images classification by a VGG-type convolutional neural network and a network with the ViT architecture with different hyperparameters.
On the test set, with a signal-to-noise ratio of about 20 dB, the convolutional network and ViT demonstrated accuracies of 97.84% and 95.43%, and with a signal-to-noise ratio of 10 dB, they demonstrated accuracies of 95.59% and 92.79%, respectively. That is, with a comparable of weights number (2,223,082 for the convolutional network and 2,379,914 for ViT), the transformer demonstrated slightly worse results. It is also noted that the convolutional network is also characterized by a higher learning speed.
To assess the influence of the transformer hyperparameters on the obtained results, two more of its variants were trained and tested, differing significantly in the number of weights - 614,090 and 9,372,810. In the first case, the accuracy on the test set was 91.79%, and in the second - 95.03%.
Thus, a comparison of MSTAR images classification results using a convolutional neural network and a network with the ViT architecture demonstrated lower performance quality of the latter in all cases. However, both a fourfold increase and a fourfold decrease of weights number in the ViT did not lead to an improvement in classification accuracy compared to the original version, comparable in terms of the number of weights with the convolutional network.
Of course, the obtained result does not indicate a lower quality of transformers in classifying radar images task in general. However, it appears that ViT architecture benefits over the convolutional network, as in the optical images case, can be realized by using significantly larger and variety of examples datasets compared to the well-balanced, homogeneous, and relatively small base MSTAR dataset.
References
1. Zhu X., Montazeri S., Ali M., Hua Yu., Wang Yu., Mou L., Shi Yi., Xu F., Bamler R. Deep Learning Meets SAR // arXiv:2006.10027.
2. Chen S., Wang H., Xu F., Jin Y.-Q. Target Classification Using the Deep Convolutional Networks for SAR Images // IEEE Transaction Geoscience and Remote Sensing, 2016, vol. 54, no. 8, pp. 4806-4817.
3. Anas H., Majdoulayne H., Chaimae A., Nabil S.M. Deep Learning for SAR Image Classification // Intelligent Systems and Applications. 2020. pp. 890-898.
4. Chen S., Wang H. SAR Target Recognition Based on Deep Learning // 2014 International Conference on Data Science and Advanced Analytics (DSAA), 2014, pp. 541-547.
5. Coman C., Thaens R. A Deep Learning SAR Target Classification Experiment on MSTAR Dataset // 2018 19th International Radar Symposium (IRS), 2018, pp. 1-6.
6. Furukawa H. Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery // arXiv:1801.08558.
7. Profeta A., Rodriguez A., Clouse H.S. Convolutional Neural Networks for Synthetic Aperture Radar Classification // Proc. SPIE 9843, Algorithms for Synthetic Aperture Radar Imagery XXIII, 98430M (14 May 2016).
8. Amrani M., Bey A., Amamra A. New SAR Target Recognition Based on YOLO and Very Deep Multi-Canonical Correlation Analysis // arXiv:2110.15383.
9. Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., Kaiser L., Polosukhin I. Attention Is All You Need // arXiv:1706.03762.
10. Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X., Unterthiner T., Dehghani M., Minderer M., Heigold G., Gelly S., Uszkoreit J., Houlsby N. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale // arXiv:2010.11929.
11. Srivastava S., Sharma G. OmniVec: Learning robust representations with cross modal sharing // arXiv:2311.05709.
12. Li K., Zhang M., Xu M., Tang R., Wang L., Wang H. Ship Detection in SAR Images Based on Feature Enhancement Swin Transformer and Adjacent Feature Fusion // Remote Sensing 2022, 14, 3186.
13. Wickramasinghe S., Parikh D., Zhang B., Kannan R., Prasanna V., Busart C. VTR: An Optimized Vision Transformer for SAR ATR Acceleration on FPGA // arXiv:2404.04527.
14. Fein-Ashley J., Ye T., Kannan R., Prasanna V., Busart C. Benchmarking Deep Learning Classifiers for SAR Automatic Target Recognition // arXiv:2312.06940.
15. Kupryashkin I.F. Sravnitel'nyye rezul'taty tochnosti klassifikatsii radiolokatsionnykh izobrazheniy ob"yektov nabora MSTAR svertochnymi neyronnymi setyami s razlichnymi arkhitekturam (Comparative results of the classification accuracy of MSTAR dataset radar images by convolutional neural networks with different architectures). Zhurnal radioelektroniki (Journal of radioelectronics), 2021, ¹11.
16. https://www.sdms.afrl.af.mil/index.php?collection=mstar.
17. Chollet F. Deep Learning with Python. Saint-Petersburg:Piter, 2018. 400 p.
18. Simonyan K., Zisserman A. Very Deep Convolutional Networks For Large-Scale Image Recognition. arXiv:1409.1556.
19. Hendrycks D., Gimpel K. Gaussian Error Linear Units (GELUs) // arXiv:1606.08415v5.
20. Nikolenko S., Kadurin A., Arkhangel’skaya E. Glubokoye obucheniye (Deep Learning). Saint-Petersburg:Piter, 2018. 480 p.
Estimation of radiation pattern distortions of a radar station under the non-stationary effect of thermal processes on a low-noise amplifier of the receiving path
A.V. Timoshenko, e-mail: u567ku78@gmail.com
A.S.Zakharov, e-mail: zakharov.as17@physics.msu.ru
Baikalov I.A, e-mail: ibaikalov@npodr.ru
Ursegov A.K., e-mail: aursegov@npodr.ru
Savchuk A.M, e-mail: savchuk@cosmos.msu.ru
Moscow State University, Russia, Moscow
JSC «Scientific and production association Long-range Radar named after A.L.Mints», Russia, Moscow
Keywords: radar station, receiving path, thermal conductivity, radiation pattern.
Abstract
Modern radar stations strive to increase the resolution to accurately determine the coordinates and characteristics of objects. One of the promising methods to achieve this goal is the use of a super-resolution mode. This is possible even when using quasi-continuous signals with long pulses and low frequency for radar tracking. To operate in the super-resolution mode, it is necessary to ensure uniformity of the amplitude-frequency characteristics of the signal transmission and reception system. The choice of a signal to create a radar portrait depends on the capabilities of the radar hardware and software complex, the uniformity of the frequency response of the system, and the uncertainty function, which helps restore the range and speed profile. The uncertainty function plays a key role in analyzing the resolution of signals in these coordinates.However, thermal distortions occur during the operation of the radar, which significantly reduce the efficiency of superresolution.
This article presents a new model for the analysis and compensation of these distortions, based on the use of the original integro-differential equation.It is shown that the unsteady behavior of thermal processes leads to distortions of the amplitude-phase distribution, amplitude-frequency response and radiation pattern, which changes the resolution characteristics. It is proposed to compensate for the influence of thermal processes by means of operational calibration of amplitude and phase, taking into account the distortions of the radiation pattern.
As a demonstration experiment, we will consider the effect of thermal processes on low-noise amplifiers, the temperature of which is evenly distributed, but at the same time there is a linear increase in temperature over time. As a super-resolution signal, we take a discretely frequency-encoded (DCH) signal and divide it into 198 codes according to the Costas diagram. The simulation took place in the MatLab environment. Initially, the uncertainty functions on the time-frequency canvas were obtained, after which slices were taken and graphs were converted to the distance and radial velocity plane for resolution analysis.
References
1. Zubarev Ju.B., Ovechkin G.V. Pomehoustojchivoe kodirovanie v cifrovyh sistemah peredachi dannyh (Error-correction coding in digital communication systems) // Jelektrosvjaz'. M., 2008. no. 12. pp. 2–11.
2. Shevtsov, V. A., Timoshenko A.V., Razinkov S.N. Ocenka sostoyaniya bezopasnosti poleta vozdushnogo sudna na osnove analiza riskov aviacionnyh incidentov // Izvestiya vysshih uchebnyh zavedenij. Aviacionnaya tekhnika. – 2024. – ¹ 1. – Ñ. 39-44. – EDN GELBAJ.
3. Zverev G.P., Timoshenko A. V., Perlov A.Yu. i dr. Primenenie teorii nechetkih mnozhestv pri reshenii zadach upravleniya vremennym resursom radiolokacionnoj stancii monitoringa kosmicheskogo prostranstva // Nauchno-tekhnicheskij vestnik informacionnyh tekhnologij, mekhaniki i optiki. – 2024. – Ò. 24, ¹ 3. – Ñ. 513-519. – DOI 10.17586/2226-1494-2024-24-3-513-519. – EDN STVNST.
4. D.S. Chirov and Y.A. Kochetkov. Assessment of the Accuracy of Determining the Coordinates and Speed of Small-Size UAV of a Multi-Position Radar with Omnidirectional Antenna Elements // 2020 Systems of Signals Generating and Processing in the Field of on Board Communications, Moscow, Russia, 2020, pp. 1-6. - doi: 10.1109/IEEECONF48371.2020.9078571. - EDN STFMIM.
5. Vasil'ev O.V., Zyabkin S.A., Nikonenko A.V., Chirov D.S. Funkcional'no-orientirovannaya model' formirovaniya meteorologicheskogo produkta v X-diapazone ot gidrometeorov pereohlazhdennoj zhidkosti pri polnom polyarizacionnom prieme // Cifrovaya obrabotka signalov. 2023. ¹ 1. Ñ. 54-61. - EDN VGWTVC.
6. Porsev V.I., Gelesev A.I., Kras'ko A.G. Uglovoe sverhrazreshenie signalov s ispol'zovaniem «virtual'nyh» antennyh reshetok // Vestnik Koncerna VKO «Almaz – Antej». 2019;(4):24-34. https://doi. org/10.38013/ 2542-0542-2019-4-24-34
7. Chizhov A.A. Sverhrazreshenie radiolokacionnyh celej pri vozdejstvii aktivnyh shumovyh pomekh po osnovnomu i blizhnim bokovym lepestkam diagrammy napravlennosti antenny RLS // Informacionno-upravlyayushchie sistemy.— 2016 .— ¹1 .— Ñ. 94-98 .— doi: 10.15217/issn1684-8853.2016.1.88
8. Mironov V.A. Filonovich A.V. K voprosu o sverhrazreshenii stohasticheskih signalov v radiolokacionnyh sistemah // «Vozdushno-kosmicheskie sily. Teoriya i praktika» ¹ 16, 2020. ñ.272-281.
9. Haoxuan Y., Qiangyu Z., Jianxin H., Weather Radar Image Superresolution Using a Nonlocal Residual Network // Journal of Mathematics, 2021, 4483907, 11 pages, 2021.doi. org/10.1155/2021/4483907
10. Munoz-Ferreras, J.M., Perez-Martinez, F. Superresolution versus Motion Compensation-Based Techniques for Radar Imaging Defense Applications // EURASIP J. Adv. Signal Process. 2010, 308379 (2010). https://doi.org/10.1155/2010/308379
11. Il'chuk A.R., Kargashin Yu.D., Merkulov V.I., Chernov V.S. Uglovoe sverhrazreshenie v bor-tovyh radiolokacionnyh sistemah vozdushnogo bazirovaniya // Zhurnal radioelektroniki. 2021. ¹11. https://doi.org/10.30898/1684-1719.2021.12.3
12. Wang, Y., Zhang Ñ., Yongshun Í. etc. Space Precession Target Classification Based on Radar High-Resolution Range Profiles // International Journal of Antennas and Propagation. 2019. 1-9. 10.1155/2019/8151620.
13. Chapurskiy V.V. Selected problems of the theory of ultra-broadband radar systems // Moscow – Publishing House of Bauman Moscow State Technical University – 2012. – 279 p (in Russian)
14. Zakharov A.S., Perlov A.Y., Timoshenko A.V., Sazonov V.V. Model for calculating changes in the radiation pattern and amplitude-phase distribution in the subarray of a large-aperture apaa based on a modified thermal conductivity equation // 2024 Systems of Signals Generating and Processing in the Field of on Board Communications. — IEEE: 2024. — P. 1-4, Moscow, Russia. DOI:10.1109/ieeeconf60226.2024.10496770
15. Pilkov A.V., Perlov A.Yu., Tret'yakova T.D., Buhanec D.I. Raschetno-analiticheskaya ocenka vliyaniya shumovoj temperatury priemnogo trakta na energeticheskij potencial RLS dal'nego obnaruzheniya // Elektromagnitnye volny i elektronnye sistemy. – 2023. – Ò. 28, ¹ 2. – Ñ. 36-42. – DOI 10.18127/j5604128-202302-05
16. Shafir R.S., Perlov A.Yu., Timoshenko A.V., Korpusov M.O. Modelirovanie teplovyh processov v vysokopotencial'nyh radiolokacionnyh stanciyah s plotnoj komponovkoj radioelektronnoj apparatury s uchetom neravnomernogo raspredeleniya temperatury vnutri blokov // Vychislitel'nye tekhnologii. 2023. Ò. 28. ¹ 5. Ñ. 33-44.
17. Matseevich S.V., Vladko U.A., Zyuzina A.D., Mochalov M.N., Zakharov A.S. Primenenie pokazatelya kognitivnoj nagruzki graficheskogo elementa dlya obosnovaniya trebovanij k sisteme vizualizacii RLS dal'nego obnaruzheniya // Nauchnaya vizualizaciya, 2024 16.3: 87 - 96, DOI: 10.26583/sv.16.3.09
Modeling of range-doppler reflections from the Earth`s surface during radar sensing from an aircraft
L. B. Ryazantsev, e-mail: kernel386@mail.ru
O. A. Babich, e-mail: oleg9mufc5fan@mail.ru
D. Ju. Maklakov, e-mail: maklakov_dju@radar-mms.com
Zhukovsky and Gagarin Air Force Academy, Russia, Voronezh
NPP Radar MMC JSC, Russia, Saint-Petersburg
Keywords: unmanned aerial vehicle, autonomous navigation, Doppler speed meters, altitude estimation, Earth’s surface sensing.
Abstract
The article is devoted to the study of Range-Doppler portraits of the Earth’s surface when probing with coherent signals of a radar sensor placed on board an aircraft.
This research is motivated by the fact that in recent decadesfact that in recent decades, there has been a steady trend toward using Global Navigation Satellite System (GNSS) receivers to measure the motion parameters of unmanned aerial vehicles (UAVs) and for their navigation. Most UAVs cannot operate without such receivers. However, a number of requirements imposed on navigation devices do not always allow their use for autonomous navigation tasks and often preclude their installation on small-class UAVs.
The most effective devices for determining UAV flight altitude with high precision relative to the ground surface are Doppler radar speed and drift angle sensors (DISS). These sensors are typically designed based on continuous-wave frequency-modulated radar (FMCW radar) principles, which reduces hardware requirements and minimizes size and weight. However, to eliminate the influence of changes in the carrier's angular position, the sensors are designed with three or four beams, increasing their dimensions and complicating installation on small-sized platforms.
At the same time, rapid advancements in analog and digital microelectronics, the emergence of miniature microwave integrated circuits implementing software-defined radio (SDR) principles, and high-performance system-on-chip (SoC) solutions have enabled the development of high-resolution radars with compact size and low weight. These radars can determine the carrier's own coordinates and motion parameters for autonomous navigation by analyzing Doppler reflections from the underlying surface.
Thus, the objective of this work is to develop a model of range-Doppler reflections from the underlying surface to justify methods for estimating UAV velocity and flight altitude using an onboard compact radar system.
To verify the obtained results, experimental testing was conducted using a compact C-band radar prototype named "Kvazar", developed by JSC "NPP Radar MMS" (St. Petersburg, Russia).
Thus, the developed model enables the calculation of Doppler reflections from the underlying surface for various parameters, including: Radar carrier flight conditions (velocity and altitude), Surface illumination parameters (antenna beam orientation and pattern shape), Background characteristics (radar cross-section density) and Radar resolution and coherent signal integration time.
In the future, the proposed model will support the development of signal processing methods for compact coherent radar systems, facilitating accurate estimation of altitude, ground speed, and drift angles to enable autonomous navigation for small unmanned aerial vehicles (UAVs).
References
1. NRA24 measuring radar with a frequency of 24 GHz [Electronic resource] // http://en.nanoradar.cn/Article/detail/id/372.html (date of reference: 02/04/2024).
2. Kolchinsky V.E., Mandurovsky I.A., Konstantinovsky M.I. Autonomous Doppler devices and navigation systems for aircraft. Moscow: Sovetskoe Radio, 1975. 430 p.
3. Kupryashkin I.F., Likhachev V.P., Ryazantsev L.B. Small-sized multifunctional radars with continuous frequency-modulated radiation. Moscow: Radiotechnika, 2020. 288 p.
4. Ivannikov K.S., Kupryashkin I.F., Ryazantsev L.B. An algorithm for forming a terrain profile using a small-sized radar with a synthesized high-resolution antenna aperture // Digital signal processing, 2019. No. 1. pp.31-36.
5. Dudnik P.I., Ilchuk A.R., Tatarsky B.G. Multifunctional radar systems. Moscow: Dropha, 2007. 283 p.
6. Verba V.S., Neronskiy L.B., Osipov I.G., Turuk V.E. Space-based Earth observation radar systems. Moscow: Radiotekhika, 2010. 680 p.
7. George B. Thomas, Jr. "Thomas' Early Transcendental Equations." Pearson Education, 2014. 1206 pages.
8. Road R., Milauskas G., Whipple R. Geometry for pleasure and solving complex problems. MacDougal Littell, 1991. pp. 717-718.
Detection of GSM standard signal in the background of interference
E.V. Rogozhnikov, e-mail: evgenii.v.rogozhnikov@tusur.ru
K.V. Savenko, e-mail: kirill.savenko@tusur.ru
E. Dmitriyev, e-mail: edgar.dmitriev@tusur.ru
N.A. Ivannikov, e-mail: ivannikoy0201@mail.ru
R.A. Porvatkin, e-mail: porvatkin2001@gmail.com
I.V. Gridin, e-mail: vanyagridin2001@bk.ru
D.Z. Zhapova, e-mail: dragondarima2001@mail.ru
The Tomsk State University of Control Systems and Radioelectronics (TUSUR), Russia, Tomsk
Keywords: correlation, correlation receiver, GSM, PRACH, adaptive threshold, DSP, FPGA.
Abstract
The detection of PRACH (Physical Random Access Channel) signals in GSM networks under noisy conditions presents a significant challenge in modern digital communication systems. This study investigates three distinct correlation-based receiver architectures: the classical correlation receiver, the sign-based correlation receiver, and the frequency-domain cross-correlation receiver. The research was conducted through MATLAB simulations, evaluating each receiver's probability of correct and false detection rates across SNR levels ranging from -5 dB to 5 dB.
The classical correlation receiver, which computes the direct multiplication of input and reference signals, demonstrated the highest detection accuracy, achieving over 95% correct detection at SNRs above -2 dB. However, its implementation requires substantial hardware resources, including multiple DSP blocks and memory, making it computationally complex. The sign-based receiver, which simplifies processing by using only the sign bits of the signal, significantly reduces hardware complexity but suffers from a 2 dB degradation in performance compared to the classical approach. The frequency-domain receiver, leveraging Fast Fourier Transform (FFT) operations, offers near-optimal detection performance (within 1 dB of the classical method) but introduces additional latency and memory requirements due to buffering and FFT processing.
The study highlights the trade-offs between detection accuracy, computational complexity, and resource efficiency. For applications such as UAV – assisted search-and-rescue operations –where reliable detection of mobile devices in forested areas is critical – the classical correlation receiver emerges as the most suitable choice. Despite its higher resource demands, its superior performance justifies its use, especially given the short preamble length (54 samples) of PRACH signals in GSM.
References
1. Development of a hardware-software complex for searching for a lost person with a mobile communication device in a forest area [Online]. Available: https://engineers.tusur.ru/projects/2cb392b9-ab42-445f-a6fd-ac77acc0c721.
2. Digital cellular telecommunications system (Phase 2+); Multiplexing and multiple access on the radio path (GSM 05.02) [Online]. Available: https://www.etsi.org/deliver/etsi_gts/05/0502/05.00.00_60/gsmts_0502v050000p.pdf
3.Digital cellular telecommunications system (Phase 2+); Physical layer on the radio path; General description (GSM 05.01 version 5.3.0) [Online]. Available: https://www.etsi.org/deliver/etsi_gts/05/0501/05.03.00_60/gsmts_0501v050300p.pdf
4. Digital cellular telecommunications system (Phase 2+); Modulation (GSM 05.04 version 7.0.0) [Online]. Available: https://www.etsi.org/deliver/etsi_en/300900_300999/300959/07.00.00_40/en_300959v070000o.pdf
5.How the radio interface works in GSM networks [Online]. Available: https://habr.com/ru/articles/268127/ (accessed: 11.12.2023).
6. Zyuko, A. G., et al. Theory of Signal Transmission. Moscow: Radio i Svyaz, 355 p. (1986).
7. Korostelev, A. A., N. F. Klyuev, and Yu. A. Melnik. Theoretical Foundations of Radar / Ed. by V. E. Dulevich. Moscow: Sov. Radio (1978).
Neural network Wiener filtering of images in the domain of discrete wavelet transform
K.A. Alimagadov, e-mail: alimagadovk@yandex.ru
S.V. Umnyashkin, e-mail: vrinf@miee.ru
National Research University of Electronic Technology (MIET), Russia, Moscow
Keywords: noise suppression, image processing, discrete wavelet transform, neural networks, Wiener filter.
Abstract
The article is devoted to improvement of noise suppression by Wiener filtering in the domain of discrete wavelet transform. The proposed filtration algorithm estimates image’s wavelet power spectrum using convolutional neural network (CNN) in the basis of biorthogonal filters 9/7 to form coefficients of Wiener filter.
The key point of processing is the adaption of local filtering technique based on fuzzy classification applied to coefficients of image’s discrete wavelet transform (DWT). These coefficients are separated into two classes corresponding to either contour or background areas in the image. Estimation of wavelet power spectrum is performed independently for each class (subset) of wavelet-coefficients. The obtained wavelet power spectra are used to calculate the matrix of Wiener filter in the domain of DWT. Gabor filters and Gaussian filters are used as kernels of convolutional layers performing fuzzy classification and wavelet power spectrum estimation in the proposed neural network to reduce the number of trainable parameters.
Suppression of wavelet coefficients in high-frequency subbands of DWT during Wiener filtering causes visual artifacts in the processed image. To diminish these artifacts an additional post processing is applied basing on the U-Net-like CNN proposed.
The neural network was trained on the subset of ImageNet and tested on Berkeley segmentation data set 500. Estimation of filtering quality was based on analysis of peak signal to noise ratio and structure similarity index values. It was found that the developed approach provides higher quality of noise suppression than other considered algorithms based on Wiener filtering. In addition, we compared our noise suppression algorithm with DCT2net and demonstrated that the considered networks achieve close values of the quality metrics on the test data. The neural network proposed in the article have an order of magnitude smaller number of trainable parameters than DCT2net (1491 vs. 28 561).
References
1. . Alimagadov K.A., Umnyashkin S.V. Augmentaciya dannyh na osnove vejvlet-fil'tracii pri obuchenii nejronnyh setej (Data augmentation based on wavelet filtration during neural network training) // Proceedings of 33-rd International conf. «GraphiCon». M., 2023. pp. 437–442.
2. Huang J.-J., Dragotti P.L. WINNet: Wavelet-inspired invertible network for image denoising // IEEE Trans. on Image Process., vol. 31, pp. 4377–4392, 2022.
3. Herbreteau S., Kervrann C. DCT2net: An interpretable shallow CNN for image denoising // IEEE Trans. Image Process., vol. 31, pp. 4292–4305, 2022.
4. Tu Z., Talebi H., Zhang H., Yang F., Milanfar P., Bovik A., Li Y. MAXIM: Multi-axis MLP for image processing // IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)., pp. 5769–5780, 2022.
5. Haykin S. Neural networks and learning machines. - 3rd ed. // New York: Pearson. 2009. 936 p.
6. Rahaman N., Baratin A., Arpit D., Draxler F., Lin M., Hamprecht F., Bengio Y., Courville A. On the spectral bias of neural networks // In International Conference on Machine Learning. PMLR. pp. 5301–5310, 2019.
7. Gal R., Cohen D., Bermano A., Cohen-Or D. SWAGAN: A style based wavelet-driven generative model // ACM Transactions on Graphics (TOG)., pp. 1–11, 2021.
8. Alimagadov K.A., Umnyashkin S.V. Podavlenie belogo shuma na izobrazheniyah na osnove vinerovskoj fil'tracii v oblasti diskretnogo vejvlet-preobrazovaniya s primeneniem nejrosetevyh tekhnologij (White noise suppression based on Wiener filtering using neural networks technologies in the domain of discrete wavelet transform) // Izvestiya vysshih uchebnyh zavedenij. Elektronika. M., 2022. vol. 27. no. 6. pp. 807–818.
9. Guo T. et al. A review of wavelet analysis and its applications: Challenges and opportunities // IEEE Access., no. 10, pp. 58869–58903, 2022.
10. Umnyashkin S.V. Osnovy teorii cifrovoj obrabotki signalov. (Fundamentals of the theory of digital signal processing) // Ì.: Tekhnosfera, 2025. 550 p.
11. Gonzalez R.C., Woods R.E. Digital image processing. - 4th ed. // New York: Pearson Education. 2018. 1168 p.
12. Feichtinger H.G., Strohmer T. Gabor analysis and algorithms: Theory and applications. // Springer Science & Business Media. 2012. 496 p.
13. Ronneberger O., Fischer P., Brox T. U-Net: Convolutional networks for biomedical image segmentation // Medical Image Computing and Computer-Assisted Intervention. Springer International Publishing. Munich, 2015. pp. 234-241.
14. Russakovsky O. et al. Imagenet large scale visual recognition challenge // International journal of computer vision., no. 115, pp. 211-252, 2015.
15. PyTorch // GitHub URL: https://github.com/pytorch/pytorch
16. Kingma D.P., Ba J.L. Adam: a method for stochastic optimization // arXiv URL: https://arxiv.org/abs/1412.6980v1
17. Berkeley segmentation data set and benchmarks 500 (BSDS500) // Berkeley Computer Vision Group URL: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html
18. Wang Z., Bovik A.C., Sheikh H.R., Simoncelli E.P. Image quality assessment: from error visibility to structural similarity // IEEE Transactions on Image Processing., no. 13, pp. 600–612, 2004.
19. Alimagadov K.A., Umnyashkin S.V. Application of Wiener filter to suppress white noise in images: wavelet vs Fourier basis // 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). St. Petersburg, Moscow, 2021. pp. 2059-2063.
Empirical mode decomposition: thematic review (part 1)
V.V. Egorov, e-mail: egorovrimr@mail.ru
D.M. Klionskiy, e-mail: klio2003@list.ru
Saint Petersburg State University of Aerospace Instrumentation
Saint Petersburg Electrotechnical University "LETI"
Keywords: empirical mode decomposition (EMD), Hilbert-Huang technology, parabolic envelope interpolation, ensemble EMD, multidimensional modification of EMD, sifting process, multicomponent signal.
Abstract
Empirical mode decomposition (EMD) and the more general Hilbert-Huang technology were introduced in 1998 and were used for analyzing natural data. Since then EMD has been applied to processing and analyzing various types of signals including vibrational, acoustic, geophysical, biomedical signals.
In the present thematic review, we provide and analyze the main known modifications of the EMD method: classical EMD, EMD with parabolic envelope interpolation, EMD based on wavelet packet decomposition, ensemble EMD and the multidimensional modification of EMD. The main ideas and application features are described in the paper. We also provide connection between EMD and ordinary differential equations. Some features of the practical application of EMD modifications are specified along with the applications of the Hilbert-Huang technology.
Classical EMD and its modifications can be applied as part of the Hilbert-Huang technology. EMD is applied to processing one-dimensional and multi-dimensional data including processing of biomedical, geophysical, vibrational, and other types of signals. EMD and its modifications are highly adaptive since the basic functions are extracted from the analyzed one-dimensional or multi-dimensional signal and a priori information is not required.
In many cases EMD and its modifications offer advantages in signal processing and signal analysis due to adaptive properties because the basis for signal decomposition is formed from the original signal. EMD allows us to perform preprocessing of stationary and non-stationary multicomponent signals and time-frequency analysis with high resolution. Due to extraction of basis functions from the original signal we can interpret decomposition results in terms of a particular knowledge domain including biomedicine, geophysics, vibration processing, acoustics, etc.
The main known modifications of EMD such as classical EMD, EMD with parabolic envelope interpolation, EMD based on wavelet packet decomposition, ensemble EMD, and multidimensional EMD allow us to choose the necessary technique in a particular task by means of taking into account the advantages and disadvantages of EMD described in the paper.
References
1. N. E. Huang, et al. The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis. Proc. Royal Soc. London. Vol. 454, 1998,
p. 903-995.
2. S. L. Marpl Tsifrovoi spectralniy analiz (Digital spectral analysis and its applications). M.: Mir, 1990, 584 p.
3. N. E. Huang, S. S. P. Shen Hilbert-Huang transform and its applications. World Scientific, 2005. 350 p.
4. P. Flandrin, G. Rilling, P. Gonsalves Empirical mode decomposition as a filter bank. IEEE Signal Process. Lett. Vol. 11, 2004, pp. 112-114.
5. D. M. Klionskiy, N. I. Oreshko, V. V. Geppener Dekompositsia na empiricheskiye modi pri analize drobnogo brounovskogo dvizhenia (Empirical mode decomposition for the analysis of fractional Brownian motion). Tsifrovaya obrabotka signalov, ¹ 3, 2008, pp. 37-45.
6. D. M. Klionsky, N. I. Oreshko, V. V. Geppener Applications of empirical mode decomposition for processing nonstationary signals. Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications. Vol. 18, No. 3, 2008, pp. 390-399.
7. D. M. Klionskiy, I. V. Neunivakin, N. I. Oreshko, V. V. Geppener Dekompositsia na empiricheskije modi i yee primenenie dlya identificatsii informativnikh component I prognosirovaniya znacheniy signalov s ispolzovaniem neironnih setey (Empirical mode decomposition and its application for identifying informative components and signal prediction using neural networks). Neurokomputery: razrabotka, primenenie, ¹6, 2010, pp. 43-50.
8. D. M. Klionskiy, N. I. Oreshko, V. V. Geppener Dekompositsiya na empiricheskiye modi s parabolicheskoy interpolyatsiyei ogibauchih v zadachah ochistki signalov ot shuma (Empirical mode decomposition with parabolic envelope interpolation in signal denoising tasks). Tsifrovaya obrabotka signalov, ¹ 2, 2011, pp. 51-60.
9. V. V. Egorov, D. M. Klionskiy Nekotoriye voprosi primeneniya adaptivnogo metoda decompositsii nestatsionarnih signalov na empiricheskiye modi (Some aspects of applying the adaptive technique of empirical mode decomposition for non-stationary signals). Tsifrovaya obrabotka signalov, ¹ 3, 2023, pp. 52-58.
10. D. Klionskiy, M. Kupriyanov, D. Kaplun Signal denoising based on empirical mode decomposition. Journal of Vibroengineering. Vol. 19, No. 7, 2017, pp. 5560-5570.
11. N. I. Oreshko, D. M. Klionskiy, V. V. Geppener, A. V. Ekalo Dekompositsiya na empiricheskiye modi v tsifrovoi obrabotke signalov (Empirical mode decomposition in digital signal processing). Izd. SPbGETU "LETI", 2013, 164 p.
12. D. M. Klionskiy, V. V. Geppener Technologia Hilberta-Huanga I yee primenenie v tsifrovoy obrabotke signalov (Hilbert-Huand technology and its application in digital signal processing). Izd. SPbGETU "LETI", 2019, 150 p.
13. N. E. Huang, Z. Shen, S. R. Long A new view of nonlinear water waves: the Hilbert spectrum. Annual Review of Fluid Mechanics, 1999, pp. 417-457.
14. Y. Chen, M. Q. Feng A technique to improve the empirical mode decomposition in the Hilbert-Huang transform. Earthquake Engineering and Engineering Vibration. Vol. 2. No. 1, 2003, pp. 75-85.
15. Z. K. Peng, P. W. Tse, F. L. Chu An improved Hilbert–Huang transform and its application in vibration signal analysis. Journal of Sound and Vibration. Vol. 286, No. 1-2, 2005, pp. 187-205.
16. R. C. Sharpley, V. Vatchev Analysis of the intrinsic mode functions. Industrial Mathematics Institute, Department of Mathematics, Institute of South California. Vol. 12, 2004. 31 p.
17. P. Goncalves, P. Abry, G. Rilling, P. Flandrin Fractal dimension estimation: empirical mode decomposition versus wavelets. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '07), 2007, pp. 1153-1156.
18. A. I. Solonina, D. M. Klionskiy, T. V. Merkucheva, S. N. Perov Tsifrovaya obrabotka signalov i MATLAB (Digital signal processing and MATLAB). Izd. BHV-Peterburg, 2013, 512 p.
19. A. I. Solonina Tsifrovaya obrabotka signalov v zerkale MATLAB (Digital signal processing with MATLAB). Izd. BHV-Peterburg, 2018, 560 p.
20. V. Uidrou, S. Stirnz Adaptivnaya obrabotka signlov (Adaptive signal processing). Izd. Radio I svyaz, 1989, 440 p.
21. K. F. Kouen, P. M. Grant Adaptivnie filtra (adaptive filters). Izd. Mir, 1988, 392 p.
22. V. I. Djigan adaptivnaya filtratsiya signalov: Teoria I algoritmi (Digital signal processing: theory and algorithms). Izd. Technosphera, 2013, 528 p.
23. N. K. Smolentsev Wavelet analiz v MATLAB (Wavelet analysis in MATLAB). 3-e izd. Izd. DMK Press, 2010, 448 p.
24. D. B. Percival, A. T. Walden Wavelet methods for time series analysis. Cambridge University Press, 2000, 594 p.
25. G. Tsolis, T. D. Xenos Signal denoising using empirical mode decomposition and higher order statistics. International Journal of Signal Processing, Image Processing and Pattern Recognition. Vol. 4, No. 2, 2011, pp. 91-106.
Method for high-precision topographic interferogram processing with use of low-detail stereophotogrammetric information obtained from optical remote sensing systems
Ushenkin V.A., e-mail: foton@rsreu.ru
The Ryazan State Radio Engineering University (RSREU), Russia, Ryazan
Keywords: synthetic aperture radar, radar image, interferogram, interferometric processing, digital terrain model, digital surface model, digital elevation model.
Abstract
The paper proposes a new method for high-precision processing of radar topographic interferograms with use of low-detail stereophotogrammetric information obtained from optical remote sensing systems. In comparison with traditional interferometry technologies, additional processing stages are introduced, which are fusion of reference low-detail stereophotogrammetric digital elevation models in order to reduce the number of artifacts and calculation of the phase corresponding to the fusion result, additional median filtering of the interferogram, rejection of areas with radar signal overlapping, identified using the reference stereophotogrammetric data with refinement based on interferogram phase analysis, rejection of shadow areas, identified using the reference stereophotogrammetric data with refinement based on amplitude analysis of the interferometric radar image pair, refinement of the interferometric base and correction of atmospheric phase distortions using the reference stereophotogrammetric data. The results of the proposed method obtained during processing of data from foreign radar satellites are presented.
References
1. Gusev S.I., Egoshkin N.A., Eremeev V.V., Kuznecov A.E., Moskvitin A.Je., Ushenkin V.A. Cifrovaja obrabotka dannyh radiolokacionnogo zondirovanija Zemli iz kosmosa (Digital processing of data obtained by spaceborne radar remote sensing of the Earth) / edited by V.V. Eremeev, V.A. Ushenkin. Moscow: KURS, 2021. 248 p.
2. Kuznecov A.E., Poshekhonov V.I. Informacionnaja tehnologija stereofotogrammetricheskoj obrabotki videodannyh ot mnogomatrichnyh skanirujushhih ustrojstv (Informational technology of multiline scanner videodata stereophotogrammetric processing) // Cifrovaja obrabotka signalov (Digital signal processing). 2010. Vol. 3. P. 44–49.
3. Cuartero A., Felicisimo A.M., Ariza F.J. Accuracy of DEM generation from Terra-ASTER stereo data // The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2004. Vol. 35. Part B2. P. 559–563.
4. Tadono T., Nagai H., Ishida H., Oda F., Naito S., Minakawa K., Iwamoto H. Generation of the 30 m-mesh global digital surface model by Alos PRISM // The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016. Vol. 41. Part B4. P. 157–162.
5. Egoshkin N.A., Ushenkin V.A. Kompleksirovanie cifrovyh modelej rel'efa s cel'ju povyshenija tochnosti opornoj informacii o vysote ob#ektov zemnoj poverhnosti (DEM fusion to improve the accuracy of reference Earth surface elevation data) // Cifrovaja obrabotka signalov (Digital signal processing). 2017. Vol. 1. P. 13–17.
6. Egoshkin N.A., Ushenkin V.A. Sovmeshhenie vysokodetal'nyh izobrazhenij s ispol'zovaniem opornoj cifrovoj modeli rel'efa pri interferometricheskoj obrabotke radiolokacionnoj informacii (High-detail image matching with use of reference digital terrain model in interferometric processing of radar data) // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta (Vestnik of Ryazan Radio Engineering University). 2015. Vol. 51. P. 72–79.
7. Egoshkin N.A. Obrabotka bortovoj navigacionnoj informacii dlja vysokotochnoj operativnoj korrekcii kosmicheskih izobrazhenij (Processing satellite navigation information for high-speed operational correction of remote sensing images) // Cifrovaja obrabotka signalov (Digital signal processing). 2017. Vol. 4. P. 23–29.
8. Egoshkin N.A., Ushenkin V.A. Utochnenie prostranstvennoj bazy pri kosmicheskoj bistaticheskoj radiolokacionnoj s#emke Zemli po signalu interferogrammy (Bistatic SAR baseline estimation by interferogram analysis) // Cifrovaja obrabotka signalov (Digital signal processing). 2016. Vol. 3. P. 42–48.
9. Goldstein R.M., Werner C.L. Radar interferogram filtering for geophysical applications // Geophysical Research Letters. 1998. Vol. 25 (21). P. 4035–4038.
10. Baran I., Stewart M.P., Kampes B.M., Perski Z., Lilly P. A modification to the Goldstein radar interferogram filter // IEEE Trans. Geoscience and Remote Sensing. 2003. Vol. 41(9). P. 2114–2118.
11. Egoshkin N.A., Ushenkin V.A. Interferometricheskaja obrabotka radiolokacionnoj informacii na osnove kombinacii metodov razvertyvanija fazy (Interferometric processing of radar data based on phase unwrapping methods’ combitation) // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta (Vestnik of Ryazan Radio Engineering University). 2015. Vol. 54 2. P. 21–31.
12. Egoshkin N.A., Eremeev V.V., Moskvitin A.Je., Ushenkin V.A. Formirovanie cifrovyh modelej rel'efa na osnove vysokotochnogo razvertyvanija fazy interferogramm ot sistem radiolokacionnoj s#emki Zemli (Digital elevation models generation based on high-precision phase unwrapping of SAR interferograms) // Radiotehnika (Radioengineering). 2016. Vol. 11. P. 120–127.
13. Ushenkin V.A. Algoritmy vysokotochnoj obrabotki interferometricheskoj informacii ot sistem distancionnogo zondirovanija Zemli na osnove 3D-analiza nabljudaemoj sceny (Algotithms of high-accuracy processing of interferometric data obtained by the Earth remote sensing systems based on 3D-analysis of observed scene): dissertation … cand. tech. sci.: 05.13.01. Ryazan, 2017. 165 p.
14. Egoshkin N.A., Eremeev V.V., Moskvitin A.Je., Ushenkin V.A. Obrabotka informacii ot sovremennyh kosmicheskih sistem radiolokacionnogo nabljudenija Zemli (Processing of data obtained by modern spaceborne radar systems of the Earth remote sensing). Moscow: FIZMATLIT, 2019. 320 p.
15. Egoshkin N.A. Metody vysokotochnoj geometricheskoj obrabotki informacii ot sovremennyh sistem kosmicheskogo zondirovanija Zemli (Methods of high-accuracy geometric processing of data obtained by modern spaceborne systems of the Earth remote sensing): dissertation … doct. tech. sci.: 05.13.01. Ryazan, 2019. 323 p.
Analysis of recursive rejection filters
D.I.Popov, e-mail: adop@mail.ru
The Ryazan State Radio Engineering University (RSREU), Russia, Ryazan
Keywords: analysis, amplitude-frequency response, clutter suppression coefficient, restructuring the structure, transient mode, rejection filter, recursion.
Abstract
An improved recursive rejection filter (RF) is proposed, which makes it possible to accelerate the transient process at the filter output by reconstructing its structure. At the same time, the established values of the decorrelated remnants of the rejection of the non-recursive part of the filter are sent to the RF output and to the feedbacks, which practically eliminates the "ringing" in the feedbacks caused by the average value and fluctuations of the clutter samples.
The structural scheme of the improved RF is given, in which the restructuring of the RF structure is implemented by switching recursive connections after the establishment of processes in the non-recursive part of the RF. The introduction of clutter rejection residues into recursive connections and to the RF output, corresponding to the established mode of the non-recursive part of the RF, significantly reduces the transition time when compensating for clutter counts.
A comparative analysis of the dynamic amplitude-frequency characteristics of the RF of a fixed and tunable structure and the effectiveness of the rejection of passive interference by these filters in the transient mode was carried out. It is shown that the restructuring of the RF structure by switching recursive connections significantly accelerates the processes of establishing a filter at the output, leading to significant (up to tens of decibels) gains in the efficiency of clutter rejection in the transient mode of operation compared to the RF of a fixed structure.
Solving the problem of accelerating the transition process of recursive RF allows us to really use the broad possibilities of forming the required characteristics of RF data and their flexible management, which, in the conditions of parametric a priori uncertainty, implies the corresponding adaptation of the RF.
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. Radiojelektronika. 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. (in Russian).
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. Rabiner L., Gould B. Teorija i primenenie cifrovoj obrabotki signalov (Theory and application of digital signal processing). – M.: Mir, 1978. – 848 p. (in Russian).
14. Popov D.I. Analiz jeffektivnosti rezhektornyh fil'trov // Vestnik Rjazanskoj gosudarstvennoj radiotehnicheskoj akademii. – 2003. – Vyp. 11. – pp. 105–107. (in Russian).
15. Popov D.I. Analiz adaptivnoj obrabotki mnogochastotnyh signalov // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta. – 2018. – ¹ 63. – pp. 9–13. (in Russian).
16. Popov D.I. Adaptivnaja mezhperiodnaja obrabotka mnogochastotnyh signalov // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta. – 2018. – ¹ 64. – pp. 17–22. (in Russian).
Using of asymmetrical «eye diagram» in digital signals phase modulation
P.A.Polushin, N.N.Korneeva, N.A.Arhipov, N.I.Kramarenko, e-mail: polushin.p@mail.ru
Vladimir State University (VlGU), Russia, Vladimir
Keywords: phase modulation, types of differential modulation, «eyes diagram» of phase modulation.
Abstract
The article deals with synchronization of generators used in receivers for phased-modulated digital signal demodulation. In correlation processing of demodulation frequency and phase of generator must be correctly tuned up to frequency and phase of received signal. This phase may variate for some reason or other. For tuning some methods are used, such as multiplication of signals, Costas schemes and others. However, mistake in tuning of generator phase may appear. It brings failure in work of generator.
To eliminate it in transmitting differential methods of modulation are used. Transmitted information containes not in absolute values of phase but in phase variation between two neighbour symbols. So, casual phase variations doesn't influece to demodulation.
However, such method has some shortcomings, because in correlation processing use signals with noise. In some cases mistakes reproducation may take place. It reduce interference stadiness by 1-3 dB.
Generator synchronization method for phase shift demodulation without differential modulation is proposed in the article. Method bases on using nonsymmetrical «eye diagrams». In «classical» modulation methods points in «eye diagram» are disposed on complex plane symmetrically. Every point corresponds to logical value of transmitted signal. In the article to deform «eye diagram» is proposed. It causes nonsymmetry in points position. Received variant of signal is analysed in receiver and according it correct generator tuning is made.
«Eye diagram» deformation may be fulfilled by various ways. Two ways of deformation are examined (radial deformation and angle deformation) in modulation methods BPSK (binary phase shift keying) and QPSK (quadrature phase shift keying). One of «eye diagram» points is removed from initial position in radial direction in one of methods and in angle direction in other method. It ties with zero value of phase.
Two variants of method realization by block schemes are described. After analysis of «eye diagram» of received signals appeared phase mistake is examined. It determines correct variant of accordance of points and logical values.
Deformation cause small decrease of interference immunity. But this decrease is less than decrease in differential modulation. Some graphics of decreases are given in article.
References
1. Sklar B. Digital Communications. Fundamentals and Applications. // Prentice Hall P T R, Upper Saddle River, New Jersey 074589 , 1099 p.
2. Polushin P.A., Kiselyov A,Y., Shalina V.V. Issledovanie vliyaniya shumov priemnika na sistemu fazovoj avtopodstrojki s umnozheniem chastoty pri ispolzovanii fazomanipulirovannyh signalov (Investigation of phase shift tuning system with frequency multiplication using phase shift modulated signals) //Proektirovanie i tehnologija elektronnyh sredstv (Projecting and technology of electronic devices), 2022, no.2, pp.26-29.
3. Polushin P.A., Nikitin O.R. Modifikatsija schemy Kostasa dlja avopodstrojki fazy v sistemah s podavlennoj nesushej (Costas scheme modification for phase regulation in systems with suppressed carrier frequency) // Radiotehnicheskie i telekommunikatsionnye sistemy (Radioengineering and telecommunication systems), 2022, no.2, pp.58-66.
4. Patent of Russian Federation No. 208271 for useful model, Ustrojstvo fazovoj avtopodstrojki sistemy s fazovoj moduliatsiej (Phase shift tuning system with phase shift keying), Polushin P.A., Nikitin O.R., Prasolov A.V., Bobrus S Y., registrated 01.12.2021., bulletin no 34.
5. Lee-Fang Wei. Rotationally Invariant Convolutional Channel Coding with Expanded Signal Space // IEEE Journal on Selected Areas in Communications, vol. SAC-2, No.5, September, 1984.
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