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Mutual interference suppression of the sinusoidal frequency modulated pulse using SHAPE algorithm

SHAPE 알고리즘을 이용한 사인파 주파수 변조 펄스의 상호간섭 억제

  • 김근환 (세종대학교 해양시스템융합공학과) ;
  • 이동화 (대구대학교 AI학부)
  • Received : 2022.08.02
  • Accepted : 2022.09.30
  • Published : 2022.10.30

Abstract

The SHAPE algorithm has the advantage of being able to shape the pulse spectrum as desired and design it not to distort other characteristics, so it was used in the active sonar pulse design. In this paper, we propose a pulse design using the SHAPE algorithm for a multi-static sonar system to reduce the cross-correlation between frequency-adjacent pulses and prevent the performance degradation of the pulses themselves. The boundary function of the SHAPE algorithm is set to be limited to the pulse bandwidth. As a result of applying the proposed design method to the sinusoidal frequency modulated pulse, the peak cross-correlation level (PCCL), which means the degree of cross-correlation, was reduced by 44.23 dB. Although the PCCL decreased by several tens of dB, no significant change in the ambiguity function was observed, and the integrated sidelobe level (ISL), which means the average value of the side lobe, increased by 11.64 dB.

SHAPE 알고리즘은 펄스의 스펙트럼 형태를 원하는 대로 성형하면서, 이 외의 특성에는 왜곡을 발생시키지 않도록 설계할 수 있다는 장점이 있어 기존의 능동소나 펄스 설계에 활용되었다. 본 논문에서는 다중상태 소나 시스템을 위한 펄스를 설계할 때, 주파수 대역에서 인접한 펄스 간의 상호상관도를 감소시키면서도 펄스 자체의 성능 저하를 방지하기 위해 SHAPE 알고리즘을 적용한 펄스 신호 설계 기법을 제안한다. 이를 위해서 SHAPE 알고리즘의 경계함수를 펄스 대역폭으로 제한하도록 설정하였다. 제안하는 설계 기법을 사인화 주파수 변조 펄스 신호에 적용한 결과 상호상관도를 의미하는 peak cross-correlation level (PCCL)이 44.23 dB 감소하였다. PCCL이 수십 dB 감소하였음에도 모호성 함수의 변화가 크게 관찰되지 않았으며, 부엽의 평균값을 의미하는 integrated sidelobe level (ISL)이 11.64 dB 증가하였다.

Keywords

Acknowledgement

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임 (No. 2019R1F1A1058434)

References

  1. Collins, Timothy. (1996), Active sonar pulse design. Diss. University of Birmingham.
  2. D. A. Hague and J. R. Buck (2015), The generalized sinusoidal frequency modulated waveform for continuous active sonar, OCEANS 2015-Genova. IEEE.
  3. Gianelli, Christopher, Luzhou Xu, and Jian Li. (2015), Active sonar systems in the presence of strong direct blast, OCEANS 2015-Genova. IEEE.
  4. J. B. Soli (2017), High resolution continuous active sonar, Ph.D. dissertation, Dept. Elect. Comput. Eng., Duke Univ., Durham, NC, USA.
  5. Kim, Geunhwan, et al. (2021), A Study on Pulse Train Waveforms for High Duty Cycle Sonar Systems: Optimization Scheme and Relationship Between Orthogonality and Bandwidth. IEEE Access 9, 119800-119817. https://doi.org/10.1109/ACCESS.2021.3107907
  6. Kim, Hyeon-su, et al. (2021), A method for setting coherent processing interval of continuous active sonar based on correlation of GSFM pulse, The Journal of the Acoustical Society of Korea 40.5 401-407. https://doi.org/10.7776/ASK.2021.40.5.401
  7. Lourey, S. (2017), Adaptive filtering for enhanced detection of continuous active sonar signals. Proceedings of the Underwater Acoustics Conference & Exhibition.
  8. Nielsen, Richard O. (1991), Sonar signal processing. Artech House, Inc.
  9. P. C. Hines (2013), Experimental comparison of continuous active and pulsed active sonars in littoral waters, in 1st Int. Conf. Exhib. Underwater Acoust., 7.
  10. R. V. Vossen, S. Beerens, and E. Spek (2011), Anti-submarine warfare with continuously active sonar, Sea Technol. 52.11, 33.
  11. Rowe, William, Petre Stoica, and Jian Li. (2014), Spectrally constrained waveform design, IEEE Signal Processing Magazine 31.3, 157-162.
  12. Van Trees, Harry L. (2014), Optimum array processing: Part IV of detection, estimation, and modulation theory. John Wiley & Sons, 2004.Processing Magazine 31.3.
  13. Xu, Luzhou, Jian Li, and Akshay Jain. (2015), Impact of strong direct blast on active sonar systems, IEEE Transactions on Aerospace and Electronic Systems 51.2 894-909. https://doi.org/10.1109/TAES.2014.140442