Single channel convolutive blind source separation for LFM radar signals

Xu, Pengfei; Jia, Yinjie; Guo, Xinnian

doi:10.2478/jee-2022-0052

Abstract

We propose a single channel blind source separation algorithm for convolutively mixed linear frequency modulation (LFM) signals based on smoothed Wigner-Ville distribution (SWVD) time-frequency analysis, Canny edge detection, and Hough transform detection. First, the SWVD time-frequency analysis diagram is obtained as an image based on the LFM time-frequency characteristics. Second, Canny edge detection is performed on the image. Then, Hough transform is used to detect the characteristic parameters of the linear signal. Finally, the source signal is recovered. The simulation results show that the algorithm is effective for single channel detection and extraction of convolutively mixed LFM signals.

References

[1] H. Zhaoshui, X. Shengli, and F. Yuli, “Sparse representation and blind source separation of ill-posed mixtures”, Science in China Series F: Information Sciences, vol. 49, no. 5, pp. 639–652, https://doi.org/10.1007/S11432-006-2020-8, 2006.10.1007/s11432-006-2020-8
Search in Google Scholar Back to article
[2] A. Ozerov, P. Philippe, R. Gribonval, and F. Bimbot, “Choice and adaptation of statistical models for single channel singing voice separation”, Traitement du Signal, vol. 24, no. 3, pp. 211–224, 2007.
Search in Google Scholar Back to article
[3] R. Tao, N. Zhang, and Y. Wang, “Analysing and compensating the effects of range and Doppler frequency migrations in linear frequency modulation pulse compression radar”, IET Radar Sonar and Navigation, vol. 5, no. 1, pp. 12–22, https://doi.org/10.1049/IET-RSN.2009.0265, 2011.10.1049/iet-rsn.2009.0265
Search in Google Scholar Back to article
[4] B. Reynders and S. Pollin, “Chirp spread spectrum as a modulation technique for long range communication”, Symposium on Communications and Vehicular Technologies, https://doi.org/10.1109/SCVT.2016.7797659, 2016.10.1109/SCVT.2016.7797659
Search in Google Scholar Back to article
[5] G. Ben, X. Zheng, Y. Wang, N. Zhang, and X. Zhang, “A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal”, Applied Sciences, vol. 11, no. 2, pp. 673, https://doi.org/10.3390/APP11020673, 2021.10.3390/app11020673
Search in Google Scholar Back to article
[6] W. Gongjian, Research on dynamic gesture recognition algorithm based on smoothed pseudo Wigner-Ville distribution, PhD thessis, Beijing University of Posts and Telecommunications, Beijing, China, 2016.
Search in Google Scholar Back to article
[7] J. Canny, J, “A Computational Approach to Edge Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, https://doi.org/10.1109/TPAMI.1986.4767851, 1986.10.1109/TPAMI.1986.4767851
Search in Google Scholar Back to article
[8] P. V. C. Hough, “Method and means for recognizing complex patterns”, US Patent 3,069,645, 1960.
Search in Google Scholar Back to article
[9] L. Durak and O. Arikan, “Short-time Fourier transform: two fundamental properties and an optimal implementation”, IEEE Transactions on Signal Processing, vol. 51, no. 5, pp. 1231–1242, https://doi.org/10.1109/TSP.2003.810293, 2003.10.1109/TSP.2003.810293
Search in Google Scholar Back to article
[10] L. Xiumei and Y. Guoqing, “Performance comparisons among time-frequency representations”, Computer Simulation, vol. 32, no. 03, pp. 220–224, 2015.
Search in Google Scholar Back to article
[11] S. Peiqing, Research on methods and implement of radar signal digital channlized reconnaissance based on STFT, PhD thessis. University of Electronic Science and Technology of China, Chengdu, China.
Search in Google Scholar Back to article
[12] B. Gao, W. L. Woo, S. S. Dlay, “Unsupervised Single-Channel Separation of Nonstationary Signals Using Gammatone Filter-bank and Itakura - Saito Nonnegative Matrix Two-Dimensional Factorizations”, IEEE Transactions on Circuits and Systems I-Regular Papers, vol. 60, no. 3, pp. 662–675, https://doi.org/10.1109/TCSI.2012.2215735, 2013.10.1109/TCSI.2012.2215735
Search in Google Scholar Back to article
[13] E. P. Wigner, “On the quantum correction for thermodynamic equilibrium”, Physical Review, vol. 40, no. 5, pp. 749–759, https://doi.org/10.1103/PHYSREV.40.749, 1932.10.1103/PhysRev.40.749
Search in Google Scholar Back to article
[14] P. Xu, Y. Jia, Z. Wang, and M. Jiang, “Underdetermined Blind Source Separation for Sparse Signals based on the Law of Large Numbers and Minimum Intersection Angle Rule”, Circuits Systems and Signal Processing, vol. 39, no. 5, pp. 2442-2458, https://doi.org/10.1007/S00034-019-01263-2, 2020.10.1007/s00034-019-01263-2
Search in Google Scholar Back to article
[15] Y. Jia and P. Xu, “Convolutive Blind Source Separation for Communication Signals Based on the Sliding Z-Transform”, IEEE Access, no. 8, pp. 41213-41219, https://doi.org/10.1109/ACCESS.2020.2976700, 2020.10.1109/ACCESS.2020.2976700
Search in Google Scholar Back to article

Single channel convolutive blind source separation for LFM radar signals

Abstract

Paradigm

My account