• Title/Summary/Keyword: LOFAR(low frequency analysis and recording)

Search Result 4, Processing Time 0.023 seconds

Lofargram fusion methods based on local anisotropy (국부 비등방성에 기반한 LOFAR그램 융합 방법)

  • Kim, Juho;Ahn, Jae-Kyun;Cho, Chomgun;Lee, Chul Mok;Hwang, Soobok
    • The Journal of the Acoustical Society of Korea
    • /
    • v.38 no.1
    • /
    • pp.128-138
    • /
    • 2019
  • In this paper, we present fusion methods for two different lofargrams. Since the conventional method synthesizes the lofargrams using frequency spectrum, it has limited performance in fusion of tonal signals which have two-dimensional information of the time-frequency domain. Proposed algorithm uses a two-dimensional directional bilateral filter for preprocessing and fuses two lofargrams based on comparison of local anisotropy of the lofargrams. After noise is suppressed and tonals are sharpened, the local anisotropy can be used as a criterion to divide tonals and noise. The experiment results using simulated data and real data showed that the proposed algorithms result in similar or lower noise level of the fused lofargram than conventional algorithms and decrease tonal omission in fusion process.

LOFAR/DEMON grams compression method for passive sonars (수동소나를 위한 LOFAR/DEMON 그램 압축 기법)

  • Ahn, Jae-Kyun;Cho, Hyeon-Deok;Shin, Donghoon;Kwon, Taekik;Kim, Gwang-Tae
    • The Journal of the Acoustical Society of Korea
    • /
    • v.39 no.1
    • /
    • pp.38-46
    • /
    • 2020
  • LOw Frequency Analysis Recording (LOFAR) and Demodulation of Envelop Modulation On Noise (DEMON) grams are bearing-time-frequency plots of underwater acoustic signals, to visualize features for passive sonar. Those grams are characterized by tonal components, for which conventional data coding methods are not suitable. In this work, a novel LOFAR/DEMON gram compression algorithm based on binary map and prediction methods is proposed. We first generate a binary map, from which prediction for each frequency bin is determined, and then divide a frame into several macro blocks. For each macro block, we apply intra and inter prediction modes and compute residuals. Then, we perform the prediction of available bins in the binary map and quantize residuals for entropy coding. By transmitting the binary map and prediction modes, the decoder can reconstructs grams using the same process. Simulation results show that the proposed algorithm provides significantly better compression performance on LOFAR and DEMON grams than conventional data coding methods.

Research on Synthesis of Radiation Noise from Moving Target (이동하는 표적의 방사소음 합성기법 연구)

  • 배재휘
    • Journal of the Korea Institute of Military Science and Technology
    • /
    • v.3 no.1
    • /
    • pp.58-65
    • /
    • 2000
  • A target signal simulation method for passive sonar systems is introduced. The method uses multirate signal processing techniques to simulate moving target signals in the multi-path sound propagation environment by introducing Lloyd's mirror and Doppler effect. Time and frequency variation of target signal due to the target maneuvering is also considered to provide realistic ship signatures in the LOFAR gram so that the simulated target is used for sonar operator training. Synthesized target characteristics is analyzed and compared with real target signal in terms of interference pattern and frequency variation in the LOFAR gram.

  • PDF

Separation of passive sonar target signals using frequency domain independent component analysis (주파수영역 독립성분분석을 이용한 수동소나 표적신호 분리)

  • Lee, Hojae;Seo, Iksu;Bae, Keunsung
    • The Journal of the Acoustical Society of Korea
    • /
    • v.35 no.2
    • /
    • pp.110-117
    • /
    • 2016
  • Passive sonar systems detect and classify the target by analyzing the radiated noises from vessels. If multiple noise sources exist within the sonar detection range, it gets difficult to classify each noise source because mixture of noise sources are observed. To overcome this problem, a beamforming technique is used to separate noise sources spatially though it has various limitations. In this paper, we propose a new method that uses a FDICA (Frequency Domain Independent Component Analysis) to separate noise sources from the mixture. For experiments, each noise source signal was synthesized by considering the features such as machinery tonal components and propeller tonal components. And the results of before and after separation were compared by using LOFAR (Low Frequency Analysis and Recording), DEMON (Detection Envelope Modulation On Noise) analysis.