Progress In Electromagnetics Research M
ISSN: 1937-8726
Home | Search | Notification | Authors | Submission | PIERS Home | EM Academy
Home > Vol. 69 > pp. 87-96


By C.-T. Li, Y.-Z. Jiang, F.-J. Liu, and T.-T. Jiang

Full Article PDF (492 KB)

In order to effectively improve the communication quality in the extremely-low frequency (ELF) communication, an integrated model of the analog circuit combined with the multi-channel array algorithm is constructed, and a highly sensitive magnetic sensor is designed. An array algorithm based on generalized singular value decomposition is proposed to find the optimal filter coefficient, and then to achieve the purpose of suppressing interference in ELF communication. In the manufacture of magnetic antenna, the method of partitioned windings divided by acrylic effectively reduces the distributed capacitance of the magnetic antenna, and the rational design of the amplification filter circuit lays the foundation for the interference suppression in the next step. Specific process of the proposed algorithm is deduced. The corresponding evaluation indices are given, and the correlation among evaluation indice is expounded. The simulated and experimental results are discussed respectively. The experimental setups are designed and presented. The results show that no matter which the simulated signal or the experimental data is, the proposed algorithm can effectively suppress the interference, and the output signal to interference ratio is increased by 30 dB.

C.-T. Li, Y.-Z. Jiang, F.-J. Liu, and T.-T. Jiang, "GSVD-Based Optimal Filtering with Analog Circuits Preprocessing for Interference Suppression in ELF Communication," Progress In Electromagnetics Research M, Vol. 69, 87-96, 2018.

1. Ying, W., et al., "A blind detector for Rayleigh flat-fading channels with non-Gaussian interference via the particle learning algorithm," AEU — International Journal of Electronics and Communications, Vol. 67, No. 12, 1068-1071, Dec. 2013.

2. Lukoschus, D. G., "Optimization theory for induction-coil magnetometers at high frequencies," IEEE Trans. Geosci. Electron., Vol. 17, No. 3, 56-63, Jul. 1979.

3. Grosz and E. Paperno, "Analytical optimization of low-frequency search coil magnetometers," IEEE Sensors J., Vol. 12, No. 8, 2719-2723, Aug. 2012.

4. Shang, X.-L., L. Wang, and J. Lin, "Low noise wideband inductive magnetic sensor," Journal of Central South University (Science and Technology), Vol. 46, No. 9, 3295-3301, Sep. 2015.

5. Zhu, W., Q. Di, and L. Liu, "Development of search coil magnetometer based on magnetic flux negative feedback structure," Chinese J. Geophys., Vol. 56, No. 11, 3683-3689, Nov. 2013.

6. Ahmed, M. and L.-N. Tho, "Channel estimation and self-interference cancelation in full-duplex communication systems," IEEE Transcations on Vehicular Technology, Vol. 66, No. 1, 321-334, Jan. 2017.

7. Zhao, Y., et al., "Research on empirical mode decomposition of signals submerged in a heavy noise," Journal of Vibration and Shock, Vol. 28, No. 3, 149-151, Mar. 2009.

8. Niu, J. P., et al., "Weak NQR signal detection based on generalized matched filter," Procedia Engineering, Vol. 7, 377-382, Aug. 2009.

9. Van Veen, B. and K. Buckley, "Beamforming: A versatile approach to spatial filtering," IEEE ASSP Mag., Vol. 5, No. 2, 4-24, Apr. 1988.

10. Gannot, S., et al., "A consolidated perspective on multimicrophone speech enhancement and source separation," IEEE/ACM Trans. on Audio, Speech, and Language Processing, Vol. 25, No. 4, 4-24, Apr. 2017.

11. Souden, M., J. Benesty, and S. Affes, "A study of the LCMV and MVDR noise reduction filters," IEEE Trans. on Signal Processing, Vol. 58, No. 9, 4925-4935, Sep. 2010.

12. Doclo, S., E. De Clippel, and M. Moonen, "Combined acoustic echo and noise reduction using GSVD-based optimal filtering," Proc. IEEE Int. Conf. Acoust. Speech Signal Processing (ICASSP 2000), Vol. 2, 1061-1064, 2000.

© Copyright 2010 EMW Publishing. All Rights Reserved