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Frequency dependence of reflections on radar landmarks

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Category: Content №5 2024

Last Updated on 29 October 2024

Published on 30 November -0001

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Authors:

I.V.Vassiliyev, orcid.org/0000-0002-6216-0443,  Special Design and Technology Bureau “Granit”, Almaty, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

B.B.Imansakipova*, orcid.org/0000-0003-0658-2112, Satbayev University, Almaty, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Sh.K.Aitkazinova, orcid.org/0000-0002-0964-3008, Satbayev University, Almaty, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

K.Z.Issabayev, orcid.org/0000-0001-5183-3668, Military Engineering Institute of Radio Electronics and Communications, Almaty, the Republic of Kazakhstan, e-mail:  This email address is being protected from spambots. You need JavaScript enabled to view it.

M.K.Olzhabayev, orcid.org/0000-0002-2741-6562, Military Engineering Institute of Radio Electronics and Communications, Almaty, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

D.G.Kanapiyanova, orcid.org/0000-0003-2819-3791, Satbayev University, Almaty, the Republic of Kazakhstan, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: imansakipovakazntu@gmail. com

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2024, (5): 130 - 135

https://doi.org/10.33271/nvngu/2024-5/130

Abstract:

Purpose. Reducing the dispersion of radar reflections from local objects, with multi-frequency sensing, to solve the problem of orientation by radar reflections from objects.

Methodology. Reflections from local objects in the entire frequency range of the radar station (RS) at the same radio engineering position were measured by three independent radars of the same type on different days and at different antenna elevations. The deviation of the radar stations in the position did not exceed 500 meters. Coordinates (azimuth, range) of reflections of several separate local objects were allocated for each radar. The average values of reflections from local objects and their dispersion in the frequency range were calculated. Using various algorithms, individual frequencies were sampled and the reflected signals were averaged at these frequencies. The decrease in the dispersion of the reflected signal from the number of frequencies at which reflections were measured and from the algorithm for selecting these frequencies was investigated.

Findings. Averaging the values of reflections from local objects for several frequencies leads to a decrease in dispersion and, as a result, to a more accurate correspondence of the reflected signal level to the geometric size of the local object. The variance decreases most rapidly for a small number of frequencies selected for averaging when selecting frequencies located in an interval of at least 1% relative to each other.

Originality. To solve the problem of orientation based on radar reflections from local objects, it is necessary to identify the landmarks selected on a digital terrain model. Due to the fact that local objects (hills) are a collection of many reflectors falling into the allowed volume of the radar, with different levels of reflections and random phases, there may not be radar reflection from a local object at a certain frequency, or it may be very small. In order to unambiguously identify all landmarks, measurements must be carried out at several frequencies. The work has established how many frequencies measurements should be performed at and on what principle these frequencies should be selected.

Practical value. The advent of digital terrain models made it possible to solve the problem of terrain orientation by comparing radar reflections from local objects with reflection models based on digital terrain maps. Radar reflection models use mathematical expectations of reflection values, unlike real reflections, which have random deviations in signal levels depending on the operating frequency. Reducing the variance of these deviations increases the accuracy of identifying characteristic local objects (landmarks) used to orient the radar in the absence of data from satellite navigation systems.

Keywords: dispersion, radar, frequency range, landmark, digital terrain model

References.

1. Langley, R. B., Teunissen, P. J., & Montenbruck, O. (2017). Introduction to GNSS. In Teunissen, P. J., Montenbruck, O. (Eds.). Springer Handbook of Global Navigation Satellite Systems. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-42928-1_1.

2. Humphreys, T. (2017). Interference. In Teunissen, P. J., Montenbruck, O. (Eds.). Springer Handbook of Global Navigation Satellite Systems. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-42928-1_16.

3. Lo, S. C., Peterson, B. B., Hardy, T., & Enge, P. K. (2010). Improving Loran Coverage with Low Power Transmitters. Journal of Navigation, 63(1), 23-38. https://doi.org/10.1017/S0373463309990245.

4. LaLonde, T., Shortridge, A., & Messina, J. (2010). The Influence of Land Cover on Shuttle Radar Topography Mission (SRTM) Elevations in Low-relief Areas. Transactions in GIS, 14, 461-479. https://doi.org/10.1111/j.1467-9671.2010.01217.x.

5. Mukul, M., Srivastava, V., Jade, S., & Mukul, M. (2017). Uncertainties in the Shuttle Radar Topography Mission (SRTM) Heights: Insights from the Indian Himalaya and Peninsula. Scientific Reports, 7, 41672. https://doi.org/10.1038/srep41672.

6. Galati, G., & Pavan, G. (2018). Generation of Land-Clutter Maps for Cognitive Radar Technology. In Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (Eds.) Trends and Advances in Information Systems and Technologies. WorldCIST’18 2018. Advances in Intelligent Systems and Computing, 746. Springer, Cham. https://doi.org/10.1007/978-3-319-77712-2_141.

7. Melebari, A., Abdul Gaffar, M. Y., & Strydom, J. J. (2015). Analysis of high resolution land clutter using an X-band radar. 2015 IEEE Radar Conference, Johannesburg, South Africa, (pp. 139-144). https://doi.org/10.1109/RadarConf.2015.7411869.

8. Wang, X., Wang, H., Yan, S., Li, L., & Meng, C. (2012). Simulation for surveillance radar ground clutter at low grazing angle. 2012 International Conference on Image Analysis and Signal Processing, Huangzhou, China, (pp. 1-4). https://doi.org/10.1109/IASP.2012.6424999.

9. Qin, F., Wan, Y., Liang, X., & Zhou, S. (2019). Clutter Modeling for FOD Surveillance Radar at Low Grazing Angle. IEEE International Conference on Signal, Information and Data Processing (ICSIDP), Chongqing, China, (pp. 1-4). https://doi.org/10.1109/ICSIDP47821.2019.9173431.

10. Darzikolaei, M. A., Ebrahimzade, A., & Gholami, E. (2015). Classification of radar clutters with Artificial Neural Network. 2 ^nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), Tehran, Iran, (pp. 577-581). https://doi.org/10.1109/KBEI.2015.7436109.

11. Maria S. Greco, & Watts, S. (2014). Chapter 11 – Radar Clutter Modeling and Analysis. In Nicholas D. Sidiropoulos, Fulvio Gini, Rama Chellappa, Sergios Theodoridis (Eds.). Academic Press Library in Signal Processing, 2, 513-594. https://doi.org/10.1016/B978-0-12-396500-4.00011-9.

12. Kim, T.-H., Jeon, H.-W., Park, S.-H., Park, J.-T., Jung, C.-H., Park, J.-H., & Bae, J. (2021). Development of Ground Clutter Reflectivity Calculation Methods and Simulated Ground Clutter Signal Generation Models Using Airborne Radars. Journal of Electromagnetic Engineering and Science, 32(6), 541-548. https://doi.org/10.5515/KJKIEES.2021.32.6.541.

13. Wu, Q., & Zhang, W. (2014). Modeling and simulation of airborne radar clutter in a littoral complex environment. 2014 IEEE International Conference on Communiction Problem-solving, Beijing, China, (pp. 496-499). https://doi.org/10.1109/ICCPS.2014.7062331.

14. Zhang, L., Xue, A., Zhao, X., Xu, S., & Mao, K. (2021). Sea-Land Clutter Classification Based on Graph Spectrum Features. Remote Sensing, 13, 4588. https://doi.org/10.3390/rs13224588.

15. Baturina, E. B., & Vasiliev, I. V. (2011). Method for assessing the energy potential of a radar station, (Patent No. 25343 RK: MPK8 G 01S 7/40/) the Kyrgyz Republic.

16. Li, H., Wang, J., Fan, Y., & Han, J. (2018). High-Fidelity Inhomogeneous Ground Clutter Simulation of Airborne Phased Array PD Radar Aided by Digital Elevation Model and Digital Land Classification Data. Sensors, 18, 2925. https://doi.org/10.3390/s18092925.

17. Kurekin, A., Radford, D., Lever, K., Marshall, D., & Shark, L.-K. (2011). New method for generating site-specific clutter map for land-based radar by using multimodal remote-sensing images and digital terrain data. IET Radar, Sonar & Navigation, 5(3), 374-388, https://doi.org/10.1049/iet-rsn.2010.0036.

18. Wang, A., Zhang, W., & Cao, J. (2012). Terrain clutter modeling for airborne radar system using digital elevation model. The 2012 International Workshop on Microwave and Millimeter Wave Circuits and System Technology, Chengdu, China, (pp. 1-4). https://doi.org/10.1109/MMWCST.2012.6238182.

19. Shasha, L. (2013). The study of radar ground clutter simulation based on DEM. 2013 IEEE International Conference on Information and Automation (ICIA), Yinchuan, China, (pp. 258-262). https://doi.org/10.1109/ICInfA.2013.6720306.

20. Kim Donghoon, Park, A.J., Suh, U.-S., Goo, D., Kim Dongwan, Yoon, B., Fa, W.-S., & Kim, S. (2022). Accurate Clutter Synthesis for Heterogeneous Textures and Dynamic Radar Environments. IEEE Transactions on Aerospace and Electronic Systems, 58(4), 3427-3445. https://doi.org/10.1109/TAES.2022.3151585.

21. Mendakulov, Zh. K., Morosi, S., Martinelli, A., & Isabaev, K. Zh. (2021). Investigation of the possibility of reducing errors in determining the coordinates of objects indoors by multi- frequency method. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (1), 137-144, https://doi.org/10.33271/nvngu/2021-1/137.

22. Sdvyzhkova, О., Golovko, Y., Dubytska, M., & Klymenko, D. (2016). Studying a crack initiation in terms of elastic oscillations in stress strain rock mass. Mining of Mineral Deposits, 10(2), 72-77.

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