Skip to main content
Log in

Mixing Matrix Estimation Algorithm for Time-Varying Radar Signals in a Dynamic System Under UBSS Model

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

This paper presents a novel mixing matrix estimation method based on a frame cluster analysis for application to the dynamic system of radar signals under an underdetermined blind source separation. The received signals are first processed using an adaptive denoising method. They are then divided into different appropriate length frames. Next, the frame type is determined and each frame is processed in accordance with its type. Short-time Fourier transform is used to transform the mixed signals from time domain to time–frequency domain. A transformation matrix is introduced to resolve the issue of the traditional clustering algorithm not being applicable within the complex field. After introducing the transformation matrix, an innovative single-source point detection algorithm and an estimation algorithm for the number of source signals are proposed. Finally, the mixing matrix is estimated via a fuzzy c-means clustering algorithm based on the characteristics of complex numbers. The simulation results show that the proposed algorithm improves the estimation accuracy of the mixing matrix considerably. Further, it has evident advantages for blind estimation of the mixing matrix for time-varying radar signals in the dynamic system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

source signals

Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

source signals

Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data Availability

The datasets generated or analyzed during this current study are available from the corresponding author on reasonable request.

References

  1. L. Bai, H. Chen, Underdetermined blind source separation of dynamic sources and mixing matrix. J. Univ. Electron. Sci. Technol. China 41, 348–354 (2012)

    Google Scholar 

  2. S. Bulek, N. Erdol, Block adaptive ICA with a time varying mixing matrix, in 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop (Marco Island, 2009), pp. 90–95

  3. Ö. Canli, H. Doğan, Performance evaluation of a speech protected active noise control system, in 2019 International Artificial Intelligence and Data Processing Symposium (IDAP) (Malatya, Turkey, 2019), pp. 1–4

  4. Y. Chen, Y. Li, J. Zhou, Mixing matrix estimation in underdetermined blind source separation based on single source points detection, in 2018 IEEE 18th International Conference on Communication Technology (ICCT) (Chongqing, 2018), pp. 1077–1081

  5. T. Dong, Y. Lei, J. Yang, An algorithm for underdetermined mixing matrix estimation. Neurocomputing 104, 26–34 (2013)

    Article  Google Scholar 

  6. M. Enescu, V. Koivunen, Tracking time-varying mixing system in blind separation, in Proceedings of the 2000 IEEE Sensor Array and Multichannel Signal Processing Workshop. SAM 2000 (Cat. No.00EX410) (Cambridge, MA, USA, 2000), pp. 291–295

  7. W.H. Fu, Z. Hu, D. Li, A sorting algorithm for multiple frequency-hopping signals in complex electromagnetic environments. Circuits, Syst., Signal Process. 8, 1–23 (2019)

    Article  Google Scholar 

  8. W.H. Fu, X.B. Zhou, B. Nong et al., Blind estimation of underdetermined mixing matrix based on density measurement. Wireless Pers. Commun. 104, 1283–1300 (2019)

    Article  Google Scholar 

  9. P. Georgiev, F. Theis, A. Cichocki, Sparse component analysis and blind source separation of underdetermined mixtures. IEEE Trans. Neural Netw. 16(4), 992–996 (2005)

    Article  Google Scholar 

  10. Q. Guo, G. Ruan, P. Nan, Underdetermined mixing matrix estimation algorithm based on single source points. Circuits, Syst., Signal Process. 36, 4453–4467 (2017)

    Article  Google Scholar 

  11. A. Koutvas, E. Dermatas, G. Kokkinakis, Blind speech separation of moving speakers in real reverberant environments, in 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 00CH37100), vol. 2 (Istanbul, Turkey, 2000), pp. II1133–II1136

  12. Y. Li, X. Geng, X. Guo, Q. Sun, F. Ye, T. Jiang, Mixing matrix estimation of frequency hopping signals based on single source points detection, in 2019 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium) (Atlanta, GA, USA, 2019), pp. 13–14

  13. Y.B. Li, W. Nie, Y. Fang, Y. Lin, A mixing matrix estimation algorithm for underdetermined blind source separation. Circuits, Syst., Signal Process. 35, 3367–3379 (2016)

    Article  MathSciNet  Google Scholar 

  14. B.Y. Li, J.H. Tian, Overcomplete ICA algorithm of speech signal extraction in underdetermined mixtures, in 2011 International Conference on Electric Information and Control Engineering (Wuhan, 2011), pp. 1520–1522

  15. C. Li, L. Zhu, Z. Zhang, Blind source separation via geometric segmentation with canonical correlation analysis in satellite communications, in 2019 International Symposium on Networks, Computers and Communications (ISNCC) (Istanbul, Turkey, 2019), pp. 1–5

  16. H. Liu, L. Zou, J. Wu, L. Zhang, T. Zhao, Underdetermined blind source separation algorithm of 220kV substation noise based on SCA, in 2016 IEEE International Conference on High Voltage Engineering and Application (ICHVE) (Chengdu, 2016), pp. 1–4

  17. R. Mukai, H. Sawada, S. Araki, S. Makino, Robust real-time blind source separation for moving speakers in a room, in 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings (ICASSP '03) (Hong Kong, 2003), pp. V-469

  18. B. Nong, W.H. Fu, Mixing matrix estimation in UBSS based on homogeneous polynomials. IET Signal Process. 12, 1123–1130 (2018)

    Article  Google Scholar 

  19. S. Qin, J. Guo, C. Zhu, Sparse component analysis using time-frequency representations for operational modal analysis. Sensors 15(3), 6497–6519 (2015)

    Article  Google Scholar 

  20. C. K. A. Reddy, A. Ganguly, I. Panahi, ICA based single microphone blind speech separation technique using non-linear estimation of speech, in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (New Orleans, LA, 2017), pp. 5570–5574

  21. O. Rekik, A. Mokraoui, A. Ladaycia, K. Abed-Meraim, Semi-blind source separation based on multi-modulus criterion: application for pilot contamination mitigation in massive MIMO communications systems, in 2019 19th International Symposium on Communications and Information Technologies (ISCIT) (Ho Chi Minh City, Vietnam, 2019), pp. 31–35

  22. L. Vielva, D. Erdoğmuş, C. Pantaleón, I. Santamaría, J. Pereda, J. C. Príncipe, Underdetermined blind source separation in a time-varying environment, in 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (Orlando, FL, 2002), pp. III-3049–III-3052

  23. W. Wang, F. Huang, Novel algorithm for underdetermined blind separation based on sparse component analysis, in 2010 IEEE International Conference on Information and Automation (Harbin, 2010), pp. 1819–1823

  24. X. Wang, G. Huang, Z. Zhou, W. Tian, J. Yao, J. Gao, Radar emitter intrapulse signal blind sorting under modified wavelet denoising. J. Eng. 21, 8013–8017 (2019)

    Article  Google Scholar 

  25. J.H. Xiang, C. Li, Q. Guo, Mixing matrix estimation of MIMO radar based on adaptive hierarchical clustering algorithm for underdetermined blind source separation, in 2017 Progress in Electromagnetics Research Symposium—Fall (PIERS—FALL) (2017), pp. 2459–2465

  26. Y. Xie, K. Xie, Z. Wu, S. Xie, Underdetermined blind source separation of speech mixtures based on k-means clustering, in 2019 Chinese Control Conference (CCC) (Guangzhou, China, 2019), pp. 42–46

  27. Y. Xie, K. Xie, S. Xie, Underdetermined blind source separation for heart sound using higher-order statistics and sparse representation. IEEE Access 7, 87606–87616 (2019)

    Article  Google Scholar 

  28. F. Ye, J. Chen, L.P. Gao et al., A mixing matrix estimation algorithm for the time-delayed mixing model of the underdetermined blind source separation problem. Circuits, Syst., Signal Process. 38, 1889–1906 (2019)

    Article  MathSciNet  Google Scholar 

  29. Y. Zhang, K. Wu, G. Tan, J. Wu, An online adaptive algorithm for underdetermined blind source separation, in 2014 12th International Conference on Signal Processing (ICSP) (Hangzhou, 2014), pp. 467–472

  30. L. Zhang, Z. Ye, Y. Zhang, S. Li, J. Li, W. Jiang, Underdetermined mixing matrix estimation based on single source detection, in 2018 China International SAR Symposium (CISS) (Shanghai, 2018), pp. 1–4

Download references

Acknowledgments

We gratefully acknowledge the anonymous reviewers who read the drafts and provided many helpful suggestions. This work is supported by the Natural Science Foundation of Shanghai (19ZR1454000).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Weihong Fu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bai, X., Fu, W., Zhou, C. et al. Mixing Matrix Estimation Algorithm for Time-Varying Radar Signals in a Dynamic System Under UBSS Model. Circuits Syst Signal Process 40, 3075–3098 (2021). https://doi.org/10.1007/s00034-020-01614-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-020-01614-4

Keywords

Navigation