Research of underwater bottom object and reverberation in feature space

Li, Xiukun; Xiong, Zhi

doi:10.1007/s11804-013-1190-1

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…The broadband signal is time-varying. A single fish target echo has a regular time-frequency distribution, while the overlapping echo exhibits significant changes in its instantaneous frequency due to phase jump and random changes in amplitude [25,26]. Therefore, detecting single fish echoes can be based on the peak time delay estimation and instantaneous frequency characteristics of the broadband signals.…”

Section: Detection Principlementioning

confidence: 99%

Individual Fish Echo Detection Method Based on Peak Delay Estimation and Instantaneous Frequency Characterization

Yang,

Cheng,

et al. 2023

Fishes

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In fisheries science research and farmed fish monitoring, acquiring individual fish echoes is the basis for the growth assessment, tracking, and target strength estimation of farmed fish. However, individual fish echo detection methods based on narrowband signal features cannot be applied well to high-density aquaculture scenarios. The broadband signaling system can improve the distance resolution of the detected target and can help to improve the performance of individual fish echo detection. In this study, for the broadband signal system and the characteristics of the underwater fish acoustic echoes, an individual fish echo detection method is proposed using the matched filter output envelope peak interval and instantaneous frequency characteristics of the echo as evaluation indices, and the simulation and experiments of the method are carried out in an anechoic water tank. The results show that the broadband signal system and the corresponding detection method perform better in detecting single target echoes than the narrowband signal system. Compared with the broadband single echo detection method that only relies on the peak interval of the matched filter envelope, the joint detection method that incorporates the instantaneous frequency characteristics of the echo signal has a better rejection capability for overlapping echoes, which can reduce the probability of misjudging the overlapping echoes. The combined detection methods may provide a better detection performance for individual fish echoes.

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Section: Detection Principlementioning

confidence: 99%

Individual Fish Echo Detection Method Based on Peak Delay Estimation and Instantaneous Frequency Characterization

Yang,

Cheng,

et al. 2023

Fishes

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show abstract

“…Cohen's time-frequency distribution [15,16] reduces cross-term interferences to a certain extent by adding kernel functions, but its applicability to different signals has significant differences. Based on the joint time-frequency domain processing method, Li et al [17] established a joint feature space by studying the target echo and reverberation characteristics and separated the target echo and reverberation in the joint feature space. Kay et al [18] proposed a pre-whitener based on an autoregressive model, which makes objects easier to detect.…”

Section: Introductionmentioning

confidence: 99%

Underwater Reverberation Suppression via Attention and Cepstrum Analysis-Guided Network

Hao

Wang

et al. 2023

JMSE

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Active sonar systems are one of the most commonly used acoustic devices for underwater equipment. They use observed signals, which mainly include target echo signals and reverberation, to detect, track, and locate underwater targets. Reverberation is the primary background interference for active sonar systems, especially in shallow sea environments. It is coupled with the target echo signal in both the time and frequency domain, which significantly complicates the extraction and analysis of the target echo signal. To combat the effect of reverberation, an attention and cepstrum analysis-guided network (ACANet) is proposed. The baseline system of the ACANet consists of a one-dimensional (1D) convolutional module and a reconstruction module. These are used to perform nonlinear mapping and to reconstruct clean spectrograms, respectively. Then, since most underwater targets contain multiple highlights, a cepstrum analysis module and a multi-head self-attention module are deployed before the baseline system to improve the reverberation suppression performance for multi-highlight targets. The systematic evaluation demonstrates that the proposed algorithm effectively suppresses the reverberation in observed signals and greatly preserves the highlight structure. Compared with NMF methods, the proposed ACANet no longer requires the target echo signal to be low-rank. Thus, it can better suppress the reverberation in multi-highlight observed signals. Furthermore, it demonstrates superior performance over NMF methods in the task of reverberation suppression for single-highlight observed signals. It creates favorable conditions for underwater platforms, such as unmanned underwater vehicles (UUVs), to carry out underwater target detection and tracking tasks.

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“…To address these challenges, researchers have developed reverberation suppression and signal separation technologies. However, as seabed reverberation and transmitted signals are inseparable in the Fourier domain, processing methods in the wavelet domain [4] and fractional Fourier domain [5][6][7] have become increasingly significant. To enhance data quality, singular value decomposition (SVD) [8] and mode decomposition (MD) [9][10][11][12][13][14] techniques can be implemented at different stages of processing.…”

Section: Introductionmentioning

confidence: 99%

Application of a Randomized Algorithm for Extracting a Shallow Low-Rank Structure in Low-Frequency Reverberation

Pang

Gao

2023

Remote Sensing

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The detection performance of active sonar is often hindered by the presence of seabed reverberation in shallow water. Separating the reverberations from the target echo and noise in the received signal is a crucial challenge in the field of underwater acoustic signal processing. To address this issue, an improved Go-SOR decomposition method is proposed based on the subspace-orbit-randomized singular value decomposition (SOR-SVD). This method successfully extracts the low-rank structure with a certain striation pattern. The results demonstrate that the proposed algorithm outperforms both the original Go algorithm and the current state-of-the-art (SOTA) algorithm in terms of the definition index of the low-rank structure and computational efficiency. Based on the monostatic reverberation theory of the normal mode, it is established that the low-rank structure is consistent with the low-frequency reverberation interference striation. This study examines the interference characteristics of the low-rank structure in the experimental sea area and suggests that the interferences of the fifth and seventh modes mainly control the low-rank structure. The findings of this study can be applied to seafloor exploration, reverberation waveguide invariant (RWI) extraction, and data-driven reverberation suppression methods.

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Research of underwater bottom object and reverberation in feature space

Cited by 7 publications

References 10 publications

Individual Fish Echo Detection Method Based on Peak Delay Estimation and Instantaneous Frequency Characterization

Individual Fish Echo Detection Method Based on Peak Delay Estimation and Instantaneous Frequency Characterization

Underwater Reverberation Suppression via Attention and Cepstrum Analysis-Guided Network

Application of a Randomized Algorithm for Extracting a Shallow Low-Rank Structure in Low-Frequency Reverberation

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