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Electromagnetic situation generation algorithm based on information geometry

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Abstract

In complex electromagnetic environment, the different location of interference sources will affect their normal function. In order to seek and locate the source of the interference, it is necessary to generate the electromagnetic situation that can be clearly shown and easily evaluated. The existing methods have the problems of slow calculation speed, low accuracy and incapably working under all kinds of electromagnetic information. This paper proposes an electromagnetic situation generation algorithm for distributed sensor network based on the information geometry theory, which can greatly improve the function by only adding a small amount of calculation cost. The Gaussian mixture model (GMM) is used to set up the statistical model of signals from each sensor. By using an information distance derived from the Kullback–Leibler divergence based on GMM to evaluate the changes of the situation, the fluctuation of electromagnetic situation can be better revealed. In order to accelerate the situation generation, the method based on the parameters transfer of adjacent nodes is proposed. The simulation results show that the electromagnetic situation generated by the method proposed in this paper is more sensitive to the interference and its position, compared with the traditional method. The experiment is carried out in a microwave anechoic chamber to test the proposed method and the results show that the method proposed in this study has fast response ability and high accuracy to the change of electromagnetic situation.

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References

  1. Jiang, B., Jiang, Q., Qi, S., & Guo, J. (2017). Visualization of integrated electromagnetic situation in battle. In Proceedings of the international conference on algorithms, methodology, models and applications in emerging technologies (ICAMMAET) (pp. 1–5), Chennai, India.

  2. Tang, C., Zhang, L., Zhang, Y., & Song, H. (2018). Factor graph-assisted distributed cooperative positioning algorithm in the GNSS system. Sensors, 18, 3748.

    Article  Google Scholar 

  3. Zhang, L., Tang, C., Zhang, Y., & Song, H. (2019). Inertial-navigation-aided single-satellite highly dynamic positioning algorithm. Sensors, 19, 4196.

    Article  Google Scholar 

  4. He, Y., Zhai, D., Zhang, R., Du, X., & Guizani, M. (2019). An anti-interference scheme for uav data links in air-ground integrated vehicular networks. Sensors, 19, 4742.

    Article  Google Scholar 

  5. Eliardsson, P., & Gabrielsson, B., et al. (2015). Electromagnetic environment mapping for the assessment of critical wireless services in ISM bands. In Proceedings of the IEEE international symposium on electromagnetic compatibility (EMC) (pp. 198–202), Dresden, Germany.

  6. Zvezdina, M. Y., & Shokova, Y. A., et al. (2017). Visualization characteristics of electromagnetic environment near communications system reflector antenna. In Proceedings of the radiation and scattering of electromagnetic waves (RSEMW) (pp. 73–76), Divnomorskoe, Russia.

  7. Lou, S., Duan, B., et al. (2019). Analysis of finite antenna arrays using the characteristic modes of isolated radiating elements. IEEE Transactions on Antennas and Propagation, 67(3), 1582–1589.

    Article  Google Scholar 

  8. Sipal, D., Abegaonkar, M. P., & Koul, S. K. (2017). Easily extendable compact planar UWB MIMO antenna array. IEEE Antennas and Wireless Propagation Letters, 16, 2328–2331.

    Article  Google Scholar 

  9. Zhao, Y., Luo, X., Lin, X., et al. (2019). Visual analytics for electromagnetic situation awareness in radio monitoring and managemen. IEEE Transactions on Visualization and Computer Graphics, 26(1), 590–600.

    Article  Google Scholar 

  10. Liu, Z., & Shi, D., et al. (2015). A new ray tracing acceleration technique in the simulation system of electromagnetic situation. In Proceedings of the 7th Asia-Pacific conference on environmental electromagnetics (CEEM) (pp. 329–333), Hangzhou, China.

  11. Arenas, J. A., Gil, U., & Plets, D. (2012). Statistical analysis of field strength location variability for UHF multimedia broadband services. IEEE Antennas and Wireless Propagation Letters, 11, 34–36.

    Article  Google Scholar 

  12. Yao, L., Shen, T., et al. (2017). Use of a reference point method to calibrate the field uniformity when testing with transient electromagnetic fields. IEEE Transactions on Electromagnetic Compatibility, 59(2), 352–359.

    Article  Google Scholar 

  13. Jun, D., Xiuying, P., & Hui, H. (2016). The method of building equivalent electromagnetic environment. In Proceedings of the Asia-Pacific international symposium on electromagnetic compatibility (APEMC) (pp. 1135–1138), Shenzhen, China.

  14. Zhang, D., Chen, E., et al. (2019). Prediction of electromagnetic compatibility for dynamic datalink of UAV. IEEE Transactions on Electromagnetic Compatibility, 61(5), 1474–1482.

    Article  Google Scholar 

  15. Zhang, Y., & Zhang, X., et al. (2019). Research on vehicle radiation immunity test method based on actual electromagnetic environment. In Proceedings of the joint international symposium on electromagnetic compatibility, Sapporo and Asia-Pacific international symposium on electromagnetic compatibility (EMC Sapporo/APEMC) (pp. 468–471), Sapporo, Japan.

  16. Kammerer, A. J., Haack, T., & Norris, R., et al. (2018). Predicting RF propagation with numerical models. In Proceedings of IEEE international symposium on antennas and propagation & USNC/URSI national radio science meeting (pp. 1927–1928), Boston, MA, USA.

  17. Kim, K., & Oh, S. (2016). Geometric optics-based propagation prediction model in urban street canyon environments. IEEE Antennas and Wireless Propagation Letters, 15, 1128–1131.

    Article  Google Scholar 

  18. Chen, Y., Wang, X., Morelande, M., & Moran, B. (2013). Information geometry of target tracking sensor networks. Information Fusion, 14(3), 311–326.

    Article  Google Scholar 

  19. Amari, S. (2016). Information geometry and its applications. Tokyo: Springer.

    Book  Google Scholar 

  20. Zhao, Q., Zhou, G., Zhang, L., Cichocki, A., & Amari, S. (2015). Bayesian robust tensor factorization for incomplete multiway data. IEEE Transactions on Neural Networks and Learning Systems, 27(4), 736–748.

    Article  Google Scholar 

  21. Lyu, F., Zhang, Y., & Cao, W. (2016). Medical image retrieval using high dimensional information geometry. In Proceedings of the 5th international conference on audio, language and image processing (ICALIP) (pp. 395-398), Shanghai, China.

  22. Ye, L., Yang, Q., Chen, Q., & Deng, W. (2019). Multidimensional joint domain localized matrix constant false alarm rate detector based on information geometry method with applications to high frequency surface wave radar. IEEE Access, 7, 28080–28088.

    Article  Google Scholar 

  23. Hua, X., Zhou, Z., & Zeng, Y., et al. (2019). Matrix information geometry for passive sonar signal detection in a non-stationary environment. In Proceedings of the 4th IEEE international conference on signal and image processing (ICSIP) ( pp. 391-394), Wuxi, China.

  24. Zhao, Y., Wang, X., Kong, W., & Shen, L. (2017). Information fusion analysis of multi-UAV system based on information geometry. In Proceedings of the 36th Chinese control conference (CCC) (pp. 8491–8496), Dalian, China.

  25. Vitànyi, P. M. B. (2017). Exact expression for information distance. IEEE Transactions on Information Theory, 63(8), 4725–4728.

    Article  Google Scholar 

  26. Chen, J., & Pratt, T. G. (2015). Packet-based energy efficiency of MIMO systems with interference avoidance over frequency-selective fading channels. IEEE Transactions on Wireless Communications, 15(4), 2698–2712.

    Article  Google Scholar 

  27. Gebru, I. D., Alameda-Pineda, X., Forbes, F., & Horaud, R. (2016). EM algorithms for weighted-data clustering with application to audio-visual scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(12), 2402–2415.

    Article  Google Scholar 

  28. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1–22.

    Google Scholar 

  29. Weili, G., Yewsoon, O., Yingjiang, Z., et al. (2018). Fisher information matrix of unipolar activation function-based multilayer perceptrons. IEEE Transactions on Cybernetics, 49(8), 3088–3098.

    Google Scholar 

  30. Arnaudon, M., Barbaresco, F., & Yang, L. (2013). Riemannian medians and means with applications to radar signal processing. IEEE Journal of Selected Topics in Signal Processing, 7(4), 595–604.

    Article  Google Scholar 

  31. Zhang, L. L., Tang, C. K., Chen, P. L., & Zhang, Y. (2020). Gaussian parameterized information aided distributed cooperative underwater positioning algorithm. IEEE Access, 8(2), 64634–64645.

    Article  Google Scholar 

  32. Guo, S., Wang, Y., & Liu, R. (2015). Multi-dimensional and complicated electromagnetic interference hardware-in-theloop simulation method. Journal of Systems Engineering and Electronics, 26(6), 1142–1148.

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61801394 and Grant 61803310, and in part by the Fundamental Research for the Central Universities under Grant 3102019HHZY030013 and Grant G2019KY05206, and in part by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2020JQ-202 and in part by Common fund for equipment development under Grant 61405180101.

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Authors and Affiliations

  1. The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, 050050, China

    Zhe Song & Peilin Chen

  2. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710072, China

    Zhe Song, Yi Zhang & Chengkai Tang

  3. The Shaanxi Key Laboratory of Integrated and Intelligent Navigation, Xi’an, 7100700, China

    Hongli Lv

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  1. Zhe Song
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  2. Yi Zhang
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  3. Hongli Lv
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  4. Peilin Chen
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  5. Chengkai Tang
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Correspondence to Chengkai Tang.

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Song, Z., Zhang, Y., Lv, H. et al. Electromagnetic situation generation algorithm based on information geometry. Telecommun Syst 77, 171–187 (2021). https://doi.org/10.1007/s11235-020-00731-4

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