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A Radar Electromagnetic Environment Sensing Method Based on Cyclic Spectral Algorithm

  • Jurong HuEmail author
  • Yu Zhang
  • Xujie Li
  • Xiaoyong Ni
  • Evans Baidoo
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 517)

Abstract

In this paper, a radar electromagnetic environment sensing method based on the cyclic spectral algorithm is discussed, which can be used to acquire the spectrum information of radar signals and distinguish them. This paper uses the second-order cyclostationary detection algorithm based on the spectral correlation function (SCF) to obtain the cyclic spectral. The estimation of SCF is and the estimation precision by calculating deviation and variance of SCF are displayed. In the simulation, a scenario of radar electromagnetic environment is presented by transmitting Linear Frequency Modulation signals (LFM) and Amplitude Modulation signals (AM). Simulation results indicate that the cyclic spectral algorithm can not only sense the spectrum information of signals but also judge the type of signal. Therefore, the bandwidth of the interference information can be detected. The simulation results show that this method is highly preferred for radar electromagnetic environment sensing even under low signal-to-noise ratio (SNR) circumstance.

Keywords

Radar electromagnetic environment Second-order cyclostationary Detection SCF MIMO radar 

References

  1. 1.
    Yu, S., Wang, X.: Joint spectrum sensing in distributed MIMO systems. In: Vehicular Technology Conference, pp. 1–4. IEEE (2011)Google Scholar
  2. 2.
    Huang, G., Tugnait, J.K.: On cyclostationarity based spectrum sensing under uncertain gaussian noise. IEEE Trans. Signal Process. 61(8), 2042–2054 (2013)CrossRefGoogle Scholar
  3. 3.
    Damavandi, M.A., Nader-Esfahani, S.: Compressive wideband spectrum sensing in cognitive radio systems based on cyclostationary feature detection. In: International Conference on Next Generation Mobile Applications, Services and Technologies, pp. 282–287. IEEE (2016)Google Scholar
  4. 4.
    Zhang, T., Yu, G., Sun, C.: Performance of cyclostationary features based spectrum sensing method in a multiple antenna cognitive radio system. In: Wireless Communications and Networking Conference, WCNC, pp. 1–5. IEEE (2009)Google Scholar
  5. 5.
    Yawada, P.S., Wei, A.J.: Cyclostationary detection based on non-cooperative spectrum sensing in cognitive radio network. In: IEEE, International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, pp. 184–187. IEEE (2016)Google Scholar
  6. 6.
    Kandeepan, S., Baldini, G., Piesiewicz, R.: Experimentally detecting IEEE 802.11n Wi-Fi based on cyclostationarity features for ultra-wide band cognitive radios. In: IEEE, International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 2315–2319. IEEE (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Jurong Hu
    • 1
    Email author
  • Yu Zhang
    • 1
  • Xujie Li
    • 1
  • Xiaoyong Ni
    • 2
  • Evans Baidoo
    • 1
  1. 1.The School of Computer and InformationHohai UniversityNan JingChina
  2. 2.University of Electronic Science and Technology of ChinaCheng DuChina

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