한국음향학회지 (The Journal of the Acoustical Society of Korea)

  • 제38권2호
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  • Pages.177-183
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  • 2019
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  • 1225-4428(pISSN)
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  • 2287-3775(eISSN)
한국음향학회 (The Acoustical Society of Korea)
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수동 선배열 소나의 저주파 간섭 신호에 대한 독립성분분석 알고리즘 비교

Comparison of independent component analysis algorithms for low-frequency interference of passive line array sonars

  • 투고 : 2018.11.06
  • 심사 : 2019.03.26
  • 발행 : 2019.03.31
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 수동 선배열 소나의 저주파 영역에서 수신된 표적 신호로부터 간섭신호를 분리해 내기 위해 독립성분분석 알고리즘을 적용하는 방안을 제안하고 기존 알고리즘들의 성능을 비교해 보았다. 저주파 대역 신호의 경우 비교적 넓은 방위로부터 수신되기 때문에 인접 빔 신호를 관측신호로 활용하여 독립성분분석을 수행할 수 있다. 신호분리에 사용한 독립성분분석 알고리즘은 FastICA(Fast Independent Component Analysis), NNMF (Non-negative Matrix Factorization), JADE (Joint Approximation Diagonalization of Eigen-matrices)이다. 실측 선배열 수동소나신호를 이용하여 독립성분분석을 수행한 결과 제안한 방법으로 간섭신호분리가 가능함을 확인하였으며, JADE 알고리즘의 신호 분리 성능이 가장 우수한 것으로 나타났다.

In this paper, we proposed an application method of ICA (Independent Component Analysis) to passive line array sonar to separate interferences from target signals in low frequency band and compared performance of three conventional ICA algorithms. Since the low frequency signals are received through larger bearing angles than other frequency bands, neighboring beam signals can be used to perform ICA as measurement signals of the ICA. We use three ICA algorithms such as Fast ICA, NNMF (Non-negative Matrix Factorization) and JADE (Joint Approximation Diagonalization of Eigen-matrices). Through experiments on real data obtained from passive line array sonar, it is verified that the interference can be separable from target signals by the suggested method and the JADE algorithm shows the best separation performance among the three algorithms.

키워드

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Fig. 1. Experiment environment.

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Fig. 2. Received signals in frequency-azimuth domain.

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Fig. 3. Short-Time Fourier Transform (STFT) of five beamformed signals corresponding to the Fig. 2. (a) 5th beam, (b) 6th beam, (c) 7th beam, (d) 8th beam, (e) 9th beam.

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Fig. 4. Power spectrum of middle beam (7th beam) signal at 10 s.

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Fig. 5. STFT of separated signals by FastICA algorithm (a) interference (b) signal.

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Fig. 6. Power spectrums of separated signals by Fast-ICA algorithm (a) interference (b) signal.

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Fig. 7. STFT of separated signals by NNMF algorithm (a) interference (b) signal.

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Fig. 8. Power spectrums of separated signals by NNMF algorithm (a) interference (b) signal.

GOHHBH_2019_v38n2_177_f0009.png 이미지

Fig. 9. STFT of separated signals by JADE algorithm (a) interference (b) signal.

GOHHBH_2019_v38n2_177_f0010.png 이미지

Fig. 10. Power spectrums of separated signals by JADE algorithm (a) interference (b) signal.

Table 1. Comparison of SIRs (signal to interference ratio) computed by three algorithms according to number of signals [dB].

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Table 2. Comparison of computation times of three algorithms [s].

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