• The Journal of the Acoustical Society of Korea
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• v.31 no.6
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• pp.384-390
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• 2012

Passive sonars employ DEMON(Detection of Envelope Modulation on Noise) processing to extract propeller information from the radiated noise of underwater targets. However, the conventional DEMON processing suffers from the interference of tonal signals because it extracts propeller signals and some types of tonal signals as well. If there are some tonals in the frequency band for DEMON processing, the conventional DEMON processing may additionally extract frequency informations originated from the interaction between different tonals. In this paper, we propose a modified DEMON processing, which can eliminate the interference of the tonals. The proposed algorithm removes tonals in DEMON processing band before demodulation processing, hence results the robustness to the interference of the tonals. Some numerical simulations demonstrate the improved performance of the proposed algorithm against the conventional algorithm.

• The Journal of the Acoustical Society of Korea
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• v.32 no.2
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• pp.138-146
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• 2013

Passive sonars employ DEMON (Detection of Envelope Modulation on Noise) processing to extract propeller information from the radiated noise of underwater targets. Conventional DEMON processing improves SNR(Signal to Noise Ratio) characteristic by Welch method. The conventional Welch method overlaps several different time domain DEMON outputs to reduce the variance. However, the conventional methods have high computational complexity to get high SNR with correlated acoustic signals. In this paper, we propose new DEMON processing method that divides acoustic signal into several frequency bands before DEMON processing and averages each DEMON outputs. Therefore, the proposed method gathers independent acoustic signal faster than conventional method with low computational complexity. We prove the performance of the proposed method with mathematical analysis and computer simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2476-2484
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• 2015

Narrow band signal estimation and broad band signal estimation can be used to detect the ship-radiated noise. The broad band signal estimation method to detect the ship-radiated noise is called DEMON (Detection of Envelop Modulation On Noise). This paper proposes a new DEMON algorithm applying compressed sensing algorithm to filter bank and autocorrelation. We show the proposed algorithm estimates the hidden period in the wide band signal better than the conventional DEMON algorithm and the recently proposed filter-bank based DEMON algorithm. Especially we show that the proposed algorithm needs shorter data length than the conventional DEMON algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.8
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• pp.538-543
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• 2014

Narrow band signal estimation and broad band signal estimation can be used to detect the ship-radiated noise. The broad band signal estimation method to detect the ship-radiated noise is called DEMON (Detection of Envelop Modulation On Noise). This paper proposes a new DEMON algorithm using filter bank and autocorrelation. We show the proposed algorithm estimates the hidden period in the wide band signal better than the conventional DEMON algorithm and the recently proposed filter-bank based DEMON algorithm.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.4
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• pp.505-513
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• 2017

Cavitation is a phenomenon caused by vapour cavities that is produced in rapid pressure changes. When the cavitation happened, the sound pressure level of a underwater radiated noise is increased rapidly. As a result, it can increase the probability of the identification or classification of a our warship's acoustic signature by an enemy ship. However, there is a problem that it is hard to precisely detect the occurrence of a cavitation noise. Therefore, this paper presents recent improvements in terms of the cavitation noise measurement by using continuous wavelet transform and DEMON(Detection of Envelope Modulation on Noise) signal processing. Then, we present that the suggested scheme is more suitable for detecting the cavitation than existing algorithms.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.3
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• pp.163-168
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• 2011

In this paper, we proposed non-linear gamma curve algorithm for gamma correction. The previous non-linear gamma curve algorithm is generated by the least square polynomial using the Gauss-Jordan inverse matrix. However, the previous algorithm has some weak points. When calculating coefficients using inverse matrix of higher degree, occurred truncation errors. Also, only if input sample points are existed regular interval on 10-bit scale, the least square polynomial is accurately works. To compensate weak-points, we calculated accurate coefficients of polynomial using eigenvalue and orthogonal value of mat11x from singular value decomposition (SVD) and QR decomposition of vandemond matrix. Also, we used input data part segmentation, then we performed polynomial curve fitting and merged curve fitting results. When compared the previous method and proposed method using the mean square error (MSE) and the standard deviation (STD), the proposed segmented polynomial curve fitting is highly accuracy that MSE under the least significant bit (LSB) error range is approximately $10^{-9}$ and STD is about $10^{-5}$.