Sparsity‐based adaptive line enhancer for passive sonars

Yu, Haohui; Chi, Cheng; Qiu, Liqing; Liang, Guolong

doi:10.1049/iet-rsn.2019.0080

Cited by 11 publications

(5 citation statements)

References 24 publications

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“…It has been found that if the input SNR is lower than a certain threshold, the ALEs cannot perform well. In recent years, some sparsity-based ALEs have been proposed in order to improve the output SNRs [4,5]. However, those sparsitybased ALEs also have the limitations of the input SNR.…”

Section: Alementioning

confidence: 99%

“…The narrowband components in the noise radiating from underwater or surfaces vehicles, which are also referred to as tonals or lines, are important for passive sonar detection [1][2][3][4][5][6][7]. These tonals originate from the propellers and machines of the aforementioned vehicles and have higher power and stability than the wideband components (continuous spectrum) in the radiated noise, which are mainly caused by cavitation [1,3,6].…”

Section: Introductionmentioning

confidence: 99%

“…These tonals originate from the propellers and machines of the aforementioned vehicles and have higher power and stability than the wideband components (continuous spectrum) in the radiated noise, which are mainly caused by cavitation [1,3,6]. In the present study, it is considered that in order to improve the detection performances of tonals in passive sonar systems, a method for enhancing the tonals is necessary [2,4,5].…”

Section: Introductionmentioning

confidence: 99%

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Deep‐learning‐based line enhancer for passive sonar systems

Chi

et al. 2021

IET Radar Sonar & Navi

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Detection of acoustic tonals from surfaces and underwater vehicles is important for passive sonar systems. Enhancements of the tonals are usually necessary in passive sonar prior to detections. Conventionally, passive sonars employ adaptive line enhancers (ALE) in order to realise enhancements of the tonals. However, ALEs have requirements on their input signalto-noise ratios (SNR). When the SNR inputs are too low, the ALEs cannot perform well. Therefore, for the purpose of overcoming the limitations of the SNR inputs to ALEs, this study proposes to enhance tonals using unsupervised deep-learning techniques. The proposed deep-learning-based line enhancer (DLE) is based on an autoencoder neural network. The simulation results show that when the input SNR is −28 dB, the proposed DLE still achieves an SNR gain of 15 dB. However, the reference ALE fails. The experimental results also demonstrate the superiority of the proposed DLE.

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Section: Alementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep‐learning‐based line enhancer for passive sonar systems

Chi

et al. 2021

IET Radar Sonar & Navi

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“…In this way, increasing the signal-to-noise ratio (SNR) is expected as a tenet to detection performance for applications. To enhance the narrowband discrete components, passive sonars usually employ an adaptive line enhancer (ALE) as a pre-processing step [9][10][11][12]. However, performance of the conventional ALE is limited, and advanced signal processing techniques dedicated to better denoising performance are of highly practical importance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Intrawell Matched Stochastic Resonance with a Potential Constraint Aided Line Enhancer for Passive Sonars

Dong

Shen

et al. 2020

Sensors

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Remote passive sonar detection and classification are challenging problems that require the user to extract signatures under low signal-to-noise (SNR) ratio conditions. Adaptive line enhancers (ALEs) have been widely utilized in passive sonars for enhancing narrowband discrete components, but the performance is limited. In this paper, we propose an adaptive intrawell matched stochastic resonance (AIMSR) method, aiming to break through the limitation of the conventional ALE by nonlinear filtering effects. To make it practically applicable, we addressed two problems: (1) the parameterized implementation of stochastic resonance (SR) under the low sampling rate condition and (2) the feasibility of realization in an embedded system with low computational complexity. For the first problem, the framework of intrawell matched stochastic resonance with potential constraint is implemented with three distinct merits: (a) it can ease the insufficient time-scale matching constraint so as to weaken the uncertain affect on potential parameter tuning; (b) the inaccurate noise intensity estimation can be eased; (c) it can release the limitation on system response which allows a higher input frequency in breaking through the large sampling rate limitation. For the second problem, we assumed a particular case to ease the potential parameter a o p t = 1 . As a result, the computation complexity is greatly reduced, and the extremely large parameter limitation is relaxed simultaneously. Simulation analyses are conducted with a discrete line signature and harmonic related line signature that reflect the superior filtering performance with limited sampling rate conditions; without loss of generality of detection, we considered two circumstances corresponding to H 1 (periodic signal with noise) and H 0 (pure noise) hypotheses, respectively, which indicates the detection performance fairly well. Application verification was experimentally conducted in a reservoir with an autonomous underwater vehicle (AUV) to validate the feasibility and efficiency of the proposed method. The results indicate that the proposed method surpasses the conventional ALE method in lower frequency contexts, where there is about 10 dB improvement for the fundamental frequency in the sense of power spectrum density (PSD).

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“…In order to effectively extract or enhance the narrowband signals in complex environments, the conventional ALE is always used with a combination of other methods. For example, the incorporation of an l 1 -norm sparse penalty into frequency domain adaption has been proposed in [14] to reduce the misadjustment of conventional ALE. Coherent integration and high-order cumulants have been combined with a multilevel switching adaptive line enhancer to obtain better performance [15].…”

Section: Introductionmentioning

confidence: 99%

Passive Detection of Ship-Radiated Acoustic Signal Using Coherent Integration of Cross-Power Spectrum with Doppler and Time Delay Compensations

Guo

Piao

Guo

et al. 2020

Sensors

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Passive sonar is widely used for target detection, identification and classification based on the target radiated acoustic signal. Under the influence of Doppler, generated by relative motion between the moving target and the sonar array, the received ship-radiated acoustic signals are non-stationary and time-varying, which has a negative effect on target detection and other fields. In order to reduce the influence of Doppler and improve the performance of target detection, a coherent integration method based on cross-power spectrum is proposed in this paper. It can be concluded that the frequency shift and phase change in the cross-power spectrum obtained by each pair of data segments can be corrected with the compensations of time scale (Doppler) factor and time delay. Moreover, the time scale factor and time delay can be estimated from the amplitude and phase of the original cross-power spectrum, respectively. Therefore, coherent integration can be implemented with the compensated cross-power spectra. Simulation and experimental data processing results show that the proposed method can provide sufficient processing gains and effectively extract the discrete spectra for the detection of moving targets.

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Sparsity‐based adaptive line enhancer for passive sonars

Cited by 11 publications

References 24 publications

Deep‐learning‐based line enhancer for passive sonar systems

Deep‐learning‐based line enhancer for passive sonar systems

Adaptive Intrawell Matched Stochastic Resonance with a Potential Constraint Aided Line Enhancer for Passive Sonars

Passive Detection of Ship-Radiated Acoustic Signal Using Coherent Integration of Cross-Power Spectrum with Doppler and Time Delay Compensations

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