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“Digital
Signal Processing” No. 3-2023 |
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Peculiarities of the Arktika-M spacecraft target information quality monitoring mathematical models and programs
V.V. Eremeev, N.A. Egoshkin, A.E. Moskvitin, A.V. Solovyev
e-mail: foton@rsreu.ru
The Ryazan State Radio Engineering University (RSREU), Russia, Ryazan
Keywords: quality monitoring, telemetric data, Earth remote sensing, spaceborne images high-level processing, software functioning monitoring.
Abstract
The paper discusses the features of periodic quality control of information coming from the Arktika-M spacecraft. Monitoring is performed by statistical service information analysis and visual images inspection. Based on the monitoring results, the calibration of the software parameters are refined in order to improve the quality of target information processing.
To monitor the correct operation of the software, methods of visual assessment of output images, mathematical calculations, analysis of the completeness and integrity of data, as well as analysis of information about the operating parameters of the satellite and on-board imaging equipment are used.
The approaches proposed in the work for monitoring the quality of target information make it possible to effectively supervise the correct functioning of ground-based means of processing satellite images, as well as to constantly maintain at the proper level and improve the quality of output information products.
References
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2. Egoshkin N.A., Eremeev V.V., Moskvitin A.E., Ushenkin V.A. Obrabotka Informacii ot sovremennyh kosmicheskih sistem radiolokacionnogo nabludeniya Zemli – Moscow: FIZMATHLIT, 2019.
3. Egoshkin N.A., Eremeev V.V., Moskvitin A.E. Koordinatnaya privyazka izobrazheniy ot geostacionarnyh sputnikov po konturnym tochkam diska Zemli // Vestnik GRGTU. 2007. Issue 22. pp. 10-16.
4. Voronin A.A., Egoshkin N.A., Eremeev V.V., Moskatinyov I.V. Geometricheskaya obrabotka dannyh kosmicheskih sistem globalnogo nabludeniya Zemli // Vestnik GRGTU. 2009. Issue 27. pp. 12-17.
5. Egoshkin N.A., Moskvitin A.E. Povyshenie tochnosti korrekcii izobrazheniy na osnove fiultracii izmereniy uglovogo polozheniya skaniruyuschego zerkala // Vestnik GRGTU. 2010. Issue 33. pp. 7-11.
6. Egoshkin N.A. Dinamicheskie modeli geometricheskoi obrabotki izobrazheniy v sistemah distancionnogo zondirovaniya Zemli // Cifrovaya obrabotka signalov. 2017. Issue 1. Pp 3-7.
Algorithm and software of high-speed neural network cloud segmentation of Resurs-P satellite’s panchromatic images
A.E. Moskvitin, V.A. Ushenkiv, S.A. Larukov
e-mail: foton@rsreu.ru
Ryazan State Radio Engineering University, Russia, Ryazan
Keywords: panchromatic image, cloud segmentation, remote sensing, artificial neural network.
Abstract
The paper considers the problem of cloud segmentation in panchromatic images obtained by the Geoton highly detailed sensor of the Resurs-P Earth remote sensing satellite. The "Lanky U-Net" artificial neural network architecture, which allows achieving the same quality of cloud segmentation in panchromatic images with 5.5 times less amount of calculations than that of the "U-Net" architecture with a 2-fold reduced number of channels, is proposed. Studies have been conducted to select the level of radiometric processing of the artificial network input data. It is proposed to supply the artificial neural network input in three channels: an image fully normalized taking into account the zenith angle of the Sun; an image partially normalized by converting into energy brightness on the sensor pupil; the original image in brightness codes. The overall segmentation accuracy of 0.972, the false positive rate of 0.029, the false negative rate of 0.025 and the processing speed on the NVIDIA GeForce RTX 2080 Ti graphics card of 7.4 Mpix./s were achieved. The examples of cloud segmentation results obtained with use of developed software are given.
References
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2. Kuznecov A.E., Poshehonov V.I. Strukturno-parametricheskij sintez komponentov malogo kosmicheskogo apparata kartograficheskogo naznachenija (Structural and parametric synthesis of cartographic small spacecraft components) // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta (Vestnik of Ryazan State Radio Engineering University). 2019. Vol. 69. P. 185–192.
3. Kuznecov A.E. Sistemy i tehnologii obrabotki ajerokosmicheskoj informacii // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta (Vestnik of Ryazan State Radio Engineering University). 2012. Vol. 39-2. P. 7–14.
4. Vetrov A.A., Kuznecov A.E. Segmentacija oblachnyh ob’ektov na panhromaticheskih izobrazhenijah zemnoj poverhnosti (Cloud objects segmentation in Earth surface panchromatic images) // Cifrovaja obrabotka signalov (Digital Signal Processing). 2011. Vol 3. P. 32–36.
5. Eremeev V.V., Kochergin A.M., Kuznetcov A.E. Automatic detection of clouds in multispectral images subjected to interchannel parallax // IEEE International Geoscience and Remote Sensing Symposium. 2015. P. 4928–4930.
6. Li Z., Shen H., Cheng Q., Liu Y., You S., He Z. Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors // ISPRS J. of Photogram. and Rem. Sen. 2019. Vol. 150. P. 197–212.
7. Jiao L., Huo L., Hu C., Tang P. Refined UNet: UNet-Based Refinement Network for Cloud and Shadow Precise Segmentation // Remote Sensing. 2020. Vol. 12(12).
8. Mohajerani S., Saeedi P. Cloud-Net: An End-to-End Cloud Detection Algorithm for Landsat 8 Imagery // IEEE International Geoscience and Remote Sensing Symposium. 2019. P. 1029–1032.
9. Mohajerani S., Saeedi P. Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery via Filtered Jaccard Loss Function and Parametric Augmentation // IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021. Vol. 14. P. 4254–4266.
10. Eremeev V., Kuznetcov A., Kochergin A., Makarenkov A. Clouds segmentation on panchromatic high spatial resolution remote sensing images using convolutional neural networks // Proceedings of the SPIE. 2019. Vol. 11155.
11. Ronneberger O., Fischer P. and Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation // Medical Image Computing and Computer-Assisted Intervention. 2015. P. 234–241.
12. Ioffe S., Szegedy C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift // Proceedings of the 32nd International Conference on International Conference on Machine Learning. 2015. Vol. 37. P. 448–456.
Adaptive neural network compression of multispectral satellite images of the Earth's surface
V.A. Ushenkiv, e-mail: foton@rsreu.ru
The Ryazan State Radio Engineering University (RSREU), Russia, Ryazan
Keywords: multispectral image, adaptive compression, remote sensing, artificial neural network.
Abstract
The paper considers the problem of compressing multispectral images on board of remote sensing satellites in order to reduce the amount of information transmitted over the radio line. A simple architecture of artificial neural networks family, that allows adaptive compression of images with controlled distortions, is proposed. The question of training such family of networks is considered. The results of experimental studies are presented in comparison with known deterministic and neural network compression algorithms. In this paper, it was possible to obtain a two-stage neural network implementation of adaptive lossy image compression, which retains the advantages of known artificial neural networks over deterministic algorithms at high compression rates, but has significantly lower requirements for the speed and memory capacity of computing devices, which potentially allows it to be used on board of remote sensing satellites.
References
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10. Ioffe S., Szegedy C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift // Proceedings of the 32nd International Conference on International Conference on Machine Learning. 2015. Vol. 37. P. 448–456.
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Detection of polyps on colonoscopic images using algorithms based on the YOLO neural network architecture
V.V. Khryashchev, e-mail: v.khryashchev@uniyar.ac.ru
A.L. Priorov, e-mail: pri@uniyar.ac.ru
A.A. Lebedev, e-mail: lebedevdes@gmail.com
N.A. Kotov, e-mail: n.kotov2@uniyar.ac.ru
P.G. Demidov Yaroslavl State University (YarSU), Russia, Yaroslavl
Keywords: endoscopy, colonoscopic images, intestinal polyps, neural network detection algorithms, deep learning, YOLO neural network architecture.
Abstract
The use of modern neural network algorithms for the detection (detection) of colon polyps on digital images obtained during coloscopic examination is investigated.
The well-known Kvasir-SEG digital image database was used to train and test deep machine learning algorithms. It contains images of 1072 polyps and offers both bounding rectangular frames and binary masks for segmentation. The resolution of the images in the specified dataset varies from 332x487 to 1920x1072 pixels.
YOLOv6, YOLOR, YOLOv7, YOLOv7X, and YOLOv8 networks, previously trained on the basis of MS COCO images, were used as neural network architectures. Since the required volume of images in the Kvasir-SEG database used is relatively small, data augmentation (reproduction, enrichment) was used to increase it to the required size. It was performed taking into account the specifics of obtaining endoscopic images in real clinical practice.
It is important to note that currently there is a noticeable lag in the size of the available databases of endoscopic images from the requirements of modern neural network algorithms and deep machine learning methods, which slows down the development of this important field for practical medicine.
As a result of applying the studied neural network detection algorithms to a standard set of endoscopic images from the specified database, the highest values of the metrics AP@[0.25..0.75] - equal to 98.4; and AP@0.50 – equal to 98.6; for a neural network detector based on the YOLOv8 network were obtained.
The obtained results can be used in the development of a video stream analysis system in an endoscopic system operating in real time during colonoscopic examinations.
References
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3. Lee J. Resection of diminutive and small colorectal polyps: what is the optimal technique? Clinical endoscopy. 2016. vol. 49 (4). p. 355.
4. Lebedev A., Khryashchev V., Stefanidi A., Stepanova O., Kashin S., Kuvaev R. Convolutional neural network for early detection of gastric cancer by endoscopic video analysis. Proc. SPIE 11433. Twelfth International Conference on Machine Vision (ICMV 2019).
5. Batukhtin D.M., Peganova E.V., Mitrakova N.N. Analysis of narrow-spectral endo-scopic images on the inner surface of the esophagus // Bulletin of the Volga State Technological University. Series: radio engineering and infocommunication systems. 2014. No. 4 (23). pp. 45-57.
6. Kovalenko D.A., Gnatyuk V.S. Association of scenes in endoscopic videos // GraphiCon 2017: Processing and analysis of biomedical images. Perm. 2017. pp. 269-274.
7. Matyja M., Pasternak A., Szura M., Wysocki M., Pedziwiatr M., Rembiasz K. How to improve the adenoma detection rate in colorectal cancer screening? Clinical factors and techno-logical advancements. Archives of medical science: AMS. 2019. vol. 15 (2). p. 424.
8. Lebedev A.A., Khryashchev V.V., Srednyakova A.S., Kazina E.M. Development of an algorithm for detecting polyps on endoscopic images using convolutional neural networks // Digital signal processing. 2021. No. 2. pp. 55-60.
9. Lebedev A.A. Research of neural network algorithms for detecting objects on video images in medical systems of applied television: dissertation for the degree of candidate of tech-nical sciences. Yaroslavl. 2022.
10. Karkanis S.A., Iakovidis D.K., Maroulis D.E., Karras D.A., Tzivras M. Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans. on Infor-mation Technology in Biomedicine. 2003. vol. 7 (3). pp. 141-152.
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Complex neural network
V. P. Kuznetsov, e-mail: kuznetsovaitu@yandex.ru
The Ryazan State Radio Engineering University (RSREU), Russia, Ryazan
Keywords: neural network, weight coefficient, activation function, error signal, learning algorithm, target function, gradient, partial derivative.
Abstract
The conception of complex neural networks construction on example of a too-layer feed-forward network is proposed. The network contains the complex weights and the complex activation functions as a complex variable function.
The main problem of complex neural networks construction consists in selection of activation function from complex variable function multitude. This problem is solved on the basis of the complex variable functions which keeps a phase of input complex signal and transforms his absolute value into single circle. The activation functions in the form of «algebraic sigmoid» [3] and «SoftSign» [4] have such properties for input complex signal.
Complex neural network is meant for complex signals processing, which use widely for telecommunication systems analysis. Such neural networks may be used for identification, pre-distortion, prediction, control. The results of experimental research of complex neural network for identification of nonlinear power amplifier are presented.
References
1. Proakis D. Digital communications. M.: Radio and communications, 2000.
2. Osowski S. Neural networks for information processing. M.: Finances and statistics, 2004.
3. Haykin S. Neural networks: a comprehensive foundation, second edition: Prentice Hall, Inc., 1999.
4. Nikolenko S., Kadurin A., Arhangelskay E. Deep learning. SPb.: Piter, 2018.
5. Churakov E.P. Prediction of econometric time series. M.: Finances and statistics, 2008.
Two-stage algorithm for parametric optimization of weighted space-time rank filtering of images
A.A. Smirnov, e-mail: al.an.smirnov@yandex.ru
A.V. Smirnov, e-mail: ant.vas.smirnov@yandex.ru
The Ryazan State Radio Engineering University (RSREU), Russia, Ryazan
Keywords: parametric optimization, two-stage optimization method, dynamic programming method, quasi-Newtonian methods, space-time image filtering.
Abstract
The problem of weighted space-time filtering of images using rank statistics is considered as a nonlinear conditional optimization problem (NCP), for the solution of which it is proposed to use an optimization algorithm consisting of two stages – global and local. At the first stage, using one of the dynamic programming methods, a rough approximation of the optimal vector of weight coefficients (global extremum region) is found. And at the second stage, the components of the optimal vector of weighting coefficients are determined with a given accuracy by one of the effective local methods of the quasi-Newton type.
The effectiveness of using the proposed two-stage optimization method for weighted space-time image filtering based on the use of average rank order statistics (median filtering) on a set of 25 test grayscale images with different density distributions of the brightness of elements has been studied. The dimensions of the test images were 360x240 pixels. Image filtering was carried out under the influence of “white” impulse noise with probabilities of 0.1, 015, 0.2, 0.25, 0.3. The filter suppression coefficient, defined as the ratio of the standard deviation of the error at the filter input to the standard deviation of the error at the filter output, was used as an efficiency criterion..
It is shown that the use of the proposed two-stage algorithm for parametric optimization of weighted space-time image filtering provides high efficiency of impulse noise suppression over the entire range of probabilities.
References
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Low complexity iterative hybrid precoding for millimeter-wave massive MIMO systems
M. Assaf, e-mail: md.moh1600@gmail.com
O.G. Ponomarev, e-mail: ponomarev@phys.tsu.ru
Tomsk State University (TSU), Russia, Tomsk
Keywords: communication systems, analog/ digital precoding, Millimeter wave communication, massive MIMO, hybrid beamforming, OFDM.
Abstract
One of the important technologies for achieving high capacity performance in the 5G mobile cellular networks is the utilization of the millimeter (mm) wave spectrum, which is commonly considered to be between 30 and 300 GHz. The article deals with hybrid precoding for massive multiple-input multiple-output (MIMO) technology that operates in mm wave band. It's known that in traditional MIMO, the optimal performance can be achieved when the number of RF chains is exactly equal to the number of antenna elements. However, this cannot be achieved directly with massive MIMO due to the high manufacturing cost and energy consumption of the RF circuits. In the article, the performance of hybrid precoding methods over flat-fading channels is investigated.
The main objective of any hybrid precoding method is to maximize the spectral efficiency of the MIMO system, which implies that the RF and baseband precoding and combining matrices should be designed jointly. Unfortunately, optimizing all four matrices simultaneously is quite difficult. Therefore, various strategies are proposed for achieving a suboptimal solution while minimizing computational overhead. The common problem with the existed precoding approaches is that they are either complex and require several mathematical operations to improve the spectral efficiency, or that they require additional information about the radio channel, which is undesirable, or that they employ matrix decomposition, which is also not a simple operation and does not provide high performance in the cases where the number of RF chains is higher than the number of data streams. Our hybrid precoder/combiner is designed to use the additional RF chains to reduce the distance between the unconstrained precoding matrix and the products of the hybrid RF and baseband precoding/combining matrices, resulting in an approach to maximum spectral efficiency. Our proposed method does not require full channel knowledge or complex decomposition techniques, which reduces the complexity and amount of feedback information.
The obtained results investigated that the spectral efficiency gap between the optimal fully-digital design and the existed schemes is reduced when the number of streams is less than the number of RF chains.
References
1. Z. Xiao et al., “A survey on millimeter-wave beamforming enabled UAV communications and networking,” IEEE Commun. Surveys Tuts., vol. 24, no. 1, pp. 557–610, 5th Quart., 2022.
2. S. A. Busari, K. M. S. Huq, S. Mumtaz, L. Dai, and J. Rodriguez, “Millimeter-wave massive MIMO communication for future wireless systems: A survey,” IEEE Commun. Surveys Tuts., vol. 20, no. 2, pp. 836–869, 2nd Quart., 2017.
3. A. F. Molisch et al., “Hybrid beamforming for massive MIMO: A survey,” IEEE Commun. Mag., vol. 55, no. 9, pp. 134–141, Sep. 2017
4. O. E. Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi and R. W. Heath, "Spatially Sparse Precoding in Millimeter Wave MIMO Systems," in IEEE Transactions on Wireless Communications, vol. 13, no. 3, pp. 1499-1513, March 2014
5. L. Dai, X. Gao, J. Quan, S. Han and C. -L. I, "Near-optimal hybrid analog and digital precoding for downlink mmWave massive MIMO systems," 2015 IEEE International Conference on Communications (ICC), London, UK, 2015, pp. 1334-1339.
6. C. -E. Chen, "An Iterative Hybrid Transceiver Design Algorithm for Millimeter Wave MIMO Systems," in IEEE Wireless Communications Letters, vol. 4, no. 3, pp. 285-288, June 2015,
7. X. Liu et al., "Hybrid Precoding for Massive mmWave MIMO Systems," in IEEE Access, vol. 7, pp. 33577-33586, 2019.
8. D. Zhang, P. Pan, R. You and H. Wang, "SVD-Based Low-Complexity Hybrid Precoding for Millimeter-Wave MIMO Systems," in IEEE Communications Letters, vol. 22, no. 10, pp. 2176-2179, Oct. 2018,
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10. S. Wang, M. He, J. Wang, R. Ran, H. Ji and V. C. M. Leung, "A Family of Hybrid Precoding Schemes for Millimeter-Wave Massive MIMO Systems," in IEEE Systems Journal, vol. 16, no. 3, pp. 4881-4891, Sept. 2022
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12. H. Lou, M. Ghosh, P. Xia, and R. Olesen, “A comparison of implicit and explicit channel feedback methods for MU-MIMO WLAN systems,” in Proc. IEEE Pers. Indoor Mobile Radio Commun. (PIMRC), Sep. 2013, pp. 419–424.
Features of operation of the receiving path in
a multichannel radar station with temporary
automatic gain control
A.V. Sheremet, e-mail: shandrei@yandex.ru
I.S. Azarov, e-mail: azarov@bsuir.by
Belarusian State University of Informatics and Radioelectronics (Minsk, Republic of Belarus)
Keywords: multichannel radar station, automatic gain control, feature of modern radar, detection characteristics of an aerial target.
Abstract
One of the directions of development of modern radar stations is the detection and tracking of unmanned aerial vehicles (UAVs) at low and extremely low altitudes. A feature of their detection is a complex interference situation:
in the analyzed airspace there are different types of objects at the same time (airplanes, helicopters, hang gliders, paraglide, UAVs, etc.);
in one resolution element there may be a reflected signal from several aerial objects, as a special case of a swarm of UAVs;
various kinds of interference are constantly present (industrial radiation, reflections from meteorological formations, from the underlying surface and local objects, etc.), which form a natural background;
the presence of organized radio interference (active and passive).
The need for sustained detection of the latter over the entire distance is significantly complicated in view of their mass character, diversity and relatively low cost, in view of which they have recently become very widespread in various spheres of society.
The power of the radar signal reflected from «distributed» local objects has a significant impact on the detection characteristics of an aerial target. In the conditions of the use of technologies to reduce their radar visibility or the use of small-sized unmanned aerial vehicles, the total differential effective scattering area becomes commensurate, and in some cases predominant over the level of the signal reflected from the target.
At the heart of mass-produced radars, a system time control with a known control law is mainly used. The widespread introduction of multipath radars has led to the fact that the resulting viewing area is formed based on the results of processing several beams, each of which has a separate receiving channel and its own geometric features. When reflections from «distributed» local objects such as rain, sea surface, etc. prevail in the channel, detection becomes problematic.
This is due to the fact that reflections significantly overload the receiving path of the radar station. A feature of modern radar stations is their multi-channel nature. One of the main ways to reduce the power level of reflected signals is the use of individual circuits with gain control of the receiving path in each channel according to a known law. At the same time, there are no recommendations on the procedure for preliminary evaluation of the parameters of the gain control law of the receiving path, taking into account the geometry of the location of the beams, the tactical and technical characteristics of the radar station and the specifics of its application. In the course of the work, such an approach is proposed and its correctness is shown.
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Some aspects of applying the adaptive technique of empirical mode decomposition of non-stationary signals
V.V. Egorov, e-mail: rimr500@mail.ru
D.M. Klionskiy, e-mail: kli02003@list.ru
State University of Aerospace Instrumentation, Russia, Saint Petersburg
Saint Petersburg Electrotechnical University “LETI”, Russia, Saint Petersburg
Keywords: empirical mode decomposition, modified empirical mode decomposition, informative components, information criteria, classification of components.
Abstract
The present paper discusses the issues of adaptive empirical mode decomposition. We represent the modified empirical mode decomposition method that allows us to eliminate decomposition redundancy. The properties of empirical modes are studied. We represent the results of comparing the empirical mode decomposition, Fourier analysis, and wavelet analysis. Some experimental data are shown that deal with studying harmonic signals. The classification of empirical modes is introduced and we suggest the technique of selecting informative components based on information criteria.
The techniques expounded in the paper, namely empirical mode decomposition and some aspects of the wavelet analysis are mainly applied to processing non-stationary signals. Non-stationary signals are encountered in such fields as, for example, speech technologies, hydroacoustics, processing vibrational signals, and geophysical and biomedical research. Further improvement of the accuracy and reliability of such analysis requires us to employ certain approaches that are adaptive to the signal under study. Empirical mode decomposition, its modifications, and the wavelet transform meet these requirements and therefore can be widely used for dealing with various types of non-stationary signals.
The paper consists of several sections and below you can find the brief contents of each one:
1. We consider the class of functions called empirical modes (or intrinsic mode functions) and also some theoretical and practical aspects of the empirical mode decomposition technique;
2. Decomposition stages are represented and the comparison is made between Fourier analysis, empirical mode decomposition, and the wavelet-based analysis;
3. We suggest the modification of the empirical mode decomposition algorithm (classical empirical mode decomposition), which allows us to avoid decomposition redundancy and extract informative components. These components make certain contributions to the original signal and they differ from each other according to multicomponent and multiband structure of the original signal in the time and frequency domains;
4. General classification of empirical modes is suggested based upon physical interpretation of empirical modes and their presence in the original signal. The interpretations of empirical modes are different and the advantage of the suggested approaches consist in using the empirical modes for explaining and clarifying some particular features in the original signal;
5. We have developed the technique for discovering informative components in the original decomposition and the suggested classification of empirical modes.
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Optimal estimate of the satellite altimeter echo–signal delay from correlated observation samples under rough sea state
A.E. Zhestetrev, e-mail: zhesterev_ae@rirt.ru
V.P. Ipatov, e-mail: ival1941@yandex.ru
Russian Institute of Radionavigation and Time (RIRT), St. Petersburg, Russia
Keywords: satellite altimeter, Cramer-Rao bound, LFM-signal, probing signal, MSK.
Abstract
An optimal algorithm for estimating the delay of the satellite altimeter echo signal based on dependent discrete observations in a rough sea condition is synthesized and expressions are obtained for the potential accuracy of the estimate. It is shown that in the presence of sea surface waves, the MSK signal yields in the estimate accuracy to LFM-signal noticeably less than it gains under the calm sea state.
References
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Development of a method and algorithms for estimating wind shear and turbulence in the meteorological radar complex of the aerodrome zone
Sinitsyn I.A., e-mail: isinitsyn@yandex.ru
Galaeva K.I., e-mail: ks.galaeva@mail.ru
JSC “Scientific and Production Enterprise “Istok” named after A.I. Shokin”
Moscow State Technical University of Civil Aviation (MSTU GA), Russia, Moscow
Keywords: meteorological radar, airfield zone, method and algorithms, specific velocity of turbulent energy dissipation, horizontal wind shear, vertical wind shear, hazard gradation, standard deviation of estimates of wind shear and turbulence.
Abstract
This article presents the developed method and algorithms for estimating wind shear and turbulence used in the ground-based meteorological radar complex of the aerodrome zone MRLC AZ. Wind shear refers to horizontal wind shear by 600 m and vertical wind shear by 30 m, turbulence refers to the parameter of the eddy dissipation rate of turbulent energy EDR, these terms comply with modern requirements of ICAO, WMO, RF Roshydromet. The method for estimating wind shear and turbulence consists of 3 stages, during which the measured values of the radial velocity, the width of the radial velocity spectrum become the initial data for calculating wind shear and turbulence, followed by classification by hazard degree. Based on the method for estimating wind characteristics, algorithms have been developed for estimating horizontal and vertical wind shear, parameters of the specific eddy dissipation rate of turbulent energy, which is a more correct representation of atmospheric turbulence. The article presents an assessment of the efficiency of the obtained algorithms by the method of statistical tests, it is shown that the obtained values of the standard deviation of estimates of EDR, wind shears meet modern existing requirements.
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Optimal fractional delay FIR filters
Andreev A.G., e-mail: aandreyev@sfu-kras.ru
Senchenko A.A., e-mail: asenchenko@sfu-kras.ru
Siberian Federal University (SFU), Russia, Krasnoyarsk
Keywords: fractional delay filters, FIR, optimal coefficients, antenna arrays, signal modelling, signal delay.
Abstract
Modelling a receiver with antenna array requires an algorithm to calculate delayed versions of impinging signals. This delay is determined by antenna array configuration, direction of signal arrival and usually is not a multiple of sampling frequency period. Although deterministic signals with known structure can be synthesized with required delay for each antenna element, this approach is not suitable for arbitrary (random) signals. Therefore, an algorithm is required that can produce a delayed version of a signal with minimum frequency distortion in a wideband. Other applications where delayed versions of signals are essential includes communications, audio and music technology, speech coding and synthesis, signal compression.
In signal processing modelling several approaches to delay a signal can be adopted. First, increasing of sampling frequency allows to control signal delay in smaller steps. Main downside of this approach is its high computational load. Second technique is based on Farrow filters. Drawbacks are nonlinear distortion and interpolation error increase with frequency. Finally, signal delay can be implemented based of finite impulse response (FIR) filters. The problem is to design a filter that has a flat amplitude response and linear phase response with required slope.
To obtain filter coefficients least squares criterion is used. Sum of squares of differences of desired and actual frequency responses at discrete points in a required frequency band is used as a loss function. Based on this criterion, equations for filter coefficients calculation are presented. Article also contains a numerical example of filter design and an analysis of the frequency response deviation dependence on filter order. Frequency response deviation is defined as a maximum difference of actual and desired filter response (amplitude response and group delay).
It is shown that solving linear system of equations using LU matrix decomposition with complete pivoting gives smaller frequency response deviation than direct matrix inversion.
References
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Trajectory signal restoring in conditions of periodic shading by autoregressive analysis method
Dvoryankov D.A.1, e-mail: dvoryankov.d.a@mail.ru
Androsov V.V.2,
Volchenkov V.A.1, e-mail: volchenkov.v.a@tor.rsreu.ru
Vityazev S.V.1, e-mail: vityazev.s.v@tor.rsreu.ru
1The Ryazan State Radio Engineering University (RSREU), Russia, Ryazan
2Ryazan State Instrument-making Enterprise
Keywords: radar systems, interpolation, AR model, radar image, filling gaps in radar signal.
Abstract
The autoregressive analysis algorithm is described in relation to the signal shape restoration, which was distorted due to the helicopter rotor blades influence. Alternative restoring methods are also provided in this paper. The AR analysis method is compared with other recovery methods by modeling the reflected radio signals processing. The ability of AR analysis to recover both simple and more complex distorted signals is shown. The dependences of the recovery quality on the speed and bearing angle of the radar are given, and the computational costs are estimated. The computational costs and algorithm ability to work in real time are also evaluated.
References
1. Dvoryankov D.A., Androsov V.V., Vityazev S.V., Vityazev V.V. Radar system imaging in conditions of periodic shading. GraphiCon 2022
32nd International Conference on Computer Graphics and Vision, pp. 774-783.
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