The radar tracking based on multiple model approach
DOI: 10.34759/trd-2023-129-19
Аuthors
*, **,
*e-mail: sychev@mai.ru
**e-mail: posipov94@gmail.com
Abstract
As of today, the onrush development of the unmanned aviation and of its application scope are observed. Besides the application in economic activity, the scope of the unmanned aviation functions by special services and in military sphere is constantly growing. The small-sized and nearly invisible unmanned aerial vehicles present are of special peril. The problem of low-observable targets detecting, tracking and intercepting for the socially significant objects protecting occurs. The article proposes a method for integration of the unmanned aviation detection, tracking and intercepting managing means, as well as synchronization of the control for these tasks solving. The article presents the description of the open information transfer protocol used in a wireless two-way exchange channel for the interception means control. Classes of possible interception objects and the structure of the complex for the interception process organization are determined. The article proposes scenarios of interception options, and presents their time characteristics as well as describes the options for radar stations that ensure detection of small-sized and low-observable objects with low values of the effective scattering area. The article describes the currently up-to-date task of identifying features of the aerial objects observed by radar for recognition and decision-making with the allocation of classes of artificial and natural origin as well. The article defines methods of useful data extracting from the reflected signals employing a convolutional neural network, and considers two options of neural network structuring, in which the input data is represented as a graphical representation of the spectrum of the reflected signal (in grayscale) and in the form of arrays of numbers.
Keywords:
sub-Nyquist receiver, undersampling receiver, undersampling, wideband receiver, digital receiver, time-frequency parameters, software-defined radio, software-defined receiver, 10 Gigabit Ethernet, pulse descriptor wordReferences
- Yaakov Bar-Shalom, X.-Rong Li, Thiagalingam Kirubarajan. Estimation with Applications to Tracking and Navigation, John Wiley&Sons, Inc., New York, 2001, 592 p.
- Blackman S.S., Popoli R. Design and Analysis of ModernTracking Systems. Norwood, MA, Artech House, 1999, 1230 p.
- Konovalov A.A. Osnovy traektornoi obrabotki radiolokatsionnoi informatsii (The Basis of Radar Tracking Systems), Saint Petersburg, Izd-vo SPbGETU «LETI», 2014.
- Kosachev I.M., Chugai K.N., Rybakov K.A. Trudy MAI, 2019, no. 105. URL: https://trudymai.ru/eng/published.php?ID=104262
- Kosachev I.M., Chugai K.N., Rybakov K.A. Trudy MAI, 2019, no. 106. URL: https://trudymai.ru/eng/published.php?ID=105725
- Volkov V.A., Kudryavtseva I.A. Trudy MAI, 2016, no. 89. URL: https://trudymai.ru/eng/published.php?ID=73405
- Genovese F. The Interacting Multiple Model Algorithm for Accurate State Estimation of Maneuvering Targets, Johns Hopkins Apl Technical Digest, 2001, vol. 22, no. 4, pp. 614-623.
- Rameshbabu K., Swarnadurga J. et al. Target Tracking System Using Kalman Filter, International Journal of Advanced Engineering Research and Studies, 2012, vol.2, pp. 90-94.
- Liu Y.C., Zuo X.G. A Maneuvering Target Tracking Algorithm Based on the Interacting Multiple Models, TELKOMNIKA Indonesian Journal of Electrical Engineering, 2013, vol. 11 (7), pp. 3997-4003. DOI:10.11591/telkomnika.v11i7.2851
- Dai H., Dai S., Cong Y., Wu G. Performance Comparison of EKF/UKF/CKF for the Tracking of Ballistic Target, TELKOMNIKA Indonesian Journal of Electrical Engineering, 2012, vol. 10 (7), pp. 1692-1699. DOI:10.11591/telkomnika.v10i7.1564
- Li X., Jilkov V. Survey of maneuvering target tracking. Part I: dynamic models, IEEE Transactions on Aerospace and Electronic Systems, 2003, vol. 39 (4), pp. 1333–1364.
DOI:10.1109/TAES.2003.1261132 - Mitchell A.E., Smith G.E., Bell K.L., Rangaswamy M. Single target tracking with distributed cognitive radar, 2017 IEEE Radar Conference (RadarConf), Seattle, WA, 2017, pp. 0285-0288. DOI:10.1109/RADAR.2017.7944213
- Bar-Shalom Y., Daum F., Huang J. The Probabilistic Data Association Filter-Estimation In the Presence of Measurement Origin Uncertainty, IEEE control systems, 2009, vol. 29, pp. 82–100. DOI:10.1109/MCS.2009.934469
- Kao Y.C., Jan S.S. Validation of Interacting Multiple Model Estimator Implementation for Radar Tracking System, In Proceedings of IGNSS Symposium 2011, Sydney, Australia, 15–17 November 2011.
- Kao Y.C., Jan S.S. Interacting Multiple Model and Probabilistic Data Association Filter on Radar Tracking for ATM System, In Proceedings of ION GNSS 2012, Nashville, TN, USA, 17–21 September 2012.
- Zhang S., Li J., Wu L. A novel multiple maneuvering targets tracking algorithm with data association and track management, International Journal of Control, Automation and Systems, 2013, vol. 5, pp. 947-956. DOI:10.1007/s12555-012-0177-z
- Bakulev P.A., Sychev M.I., Nguen Chong Lyu. Radiotekhnika, 2004, no. 1, pp. 26-32.
- Sychev M.I. Elektrosvyaz’, 2022, no. 3, pp. 35-43.
- Sychev M.I. Trudy MAI, 2016, no. 90. URL: https://trudymai.ru/eng/published.php?ID=74830
- Sychev M.I. Izvestiya vysshikh uchebnykh zavedenii. Aviatsionnaya tekhnika, 2017, no. 2, pp. 28-35.
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