Abstract
Frequency hopping signal has the advantages of strong anti-jamming ability and low probability of interception. It can effectively improve communication quality and security. Therefore, frequency hopping technology is widely used in the field of information countermeasures, and has become one of the main anti-jamming technologies adopted by various countries. As a non-partner, how to quickly obtain the main parameters of frequency hopping signal in order to implement effective and timely interference is particularly important. In this paper, a blind estimation algorithm based on short-time Fourier transform (STFT) is proposed. STFT is a time-frequency analysis method with low complexity. The performance of this method depends largely on the length of the Fourier transform window. The algorithm in this paper roughly estimates the period of frequency hopping signal according to the Frequency domain characteristics of input signal, and uses this information to determine the length of local window. The obtained time-frequency distribution is purified by setting a reasonable threshold, and the purified time-frequency distribution is used to extract information and estimate the main parameters of frequency hopping signal. The results show that this method can roughly estimate the length of Fourier transform window in very low SNR environment, and fine estimation method has higher accuracy in estimating the Frequency hopping period of frequency hopping signal.
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Zhang, Y., Jia, X., Yin, C.: Time-frequency analysis of frequency hopping signal based on partial reconstruction. In: International Conference on Signal Processing, Communications and Computing (ICSPCC), pp. 1–5 (2017)
Liu, S., Zhang, Y.D., Shan, T., Tao, R.: Structure-aware bayesian compressive sensing for frequency-hopping spectrum estimation with missing observations. IEEE Trans. Signal Process. 66(8), 2153–2166 (2018)
Chaudhury, K.N., Unser, M.: Construction of hilbert transform pairs of wavelet bases and gabor-like transforms. IEEE Trans. Signal Process. 57(9), 3411–3425 (2009)
Zhao, L., Wang, L., Bi, G., Zhang, L., Zhang, H.: Robust frequency-hopping spectrum estimation based on sparse bayesian method. IEEE Trans. Wireless Commun. 14(2), 781–793 (2015)
Ma, Y., Yan, Y.: Blind detection and parameter estimation of single frequency-hopping signal in complex electromagnetic environment. In: Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC), pp. 370–374 (2016)
Lei, Y., Zhong, Z., Wu, Y.: A new hop duration blind estimation algorithm for frequency-hopping signals. In: IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, pp. 695–699 (2008)
Boashash, B., Sucic, V.: Resolution measure criteria for the objective assessment of the performance of quadratic time-frequency distributions. IEEE Trans. Signal Process. 51(5), 1253–1263 (2003)
Boashash, B., Azemi, G., O’Toole, J.M.: Time-frequency processing of nonstationary signals: advanced TFD design to aid diagnosis with highlights from medical applications. IEEE Signal Process. Mag. 30(6), 108–119 (2013)
Zhao, Y., Zou, Z., Wu, L., Li, Y.: Frequency detection algorithm for frequency diversity signal based on STFT. In: Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), pp. 790–793 (2015)
Tan, J.L., Sha’ameri, A.: Adaptive smooth-windowed wigner-ville distribution for digital communication signal. In: Telecommunication Technologies & Malaysia Conference on Photonics NCTT-MCP National Conference on. IEEE, pp. 254–259 (2009)
Kanaa, A., Sha’ameri, A.Z.: A robust parameter estimation of FHSS signals using time–frequency analysis in a non-cooperative environment. Phys. Commun. 26, 9–20 (2018)
Li. N., Dong, S., Yang, D., Hao, Z.: The research on frequency-hopping signals analysis methods based on adaptive optimal kernel time-frequency representation. In: International Conference on Measuring Technology and Mechatronics Automation, pp. 544–547 (2009)
Barbarossa, S., Scaglione, A.: Parameter estimation of spread spectrum frequency-hopping signals using time-frequency distributions. In: First IEEE Signal Processing, Workshop on Signal Processing Advances in Wireless Communications, pp. 213–216 (1997)
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This work is supported by the National Natural Science Foundation of China (No. 41861134010 and No. 91438205).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Li, Z., Wang, X., Li, M., Han, S. (2019). An Adaptive Window Time-Frequency Analysis Method Based on Short-Time Fourier Transform. In: Han, S., Ye, L., Meng, W. (eds) Artificial Intelligence for Communications and Networks. AICON 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-030-22971-9_8
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DOI: https://doi.org/10.1007/978-3-030-22971-9_8
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