Adaptive whitening of ambient ocean noise with narrowband signal preservation

Hollmann, Luke J.; Stevenson, Robert Louis

doi:10.1121/1.4953020

Cited by 3 publications

(4 citation statements)

References 22 publications

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“…However, at low SNR, where the signal is almost buried in the noise, background equalization techniques usually work poorly. The ALE method [22][23][24][25][26][27] is an essentially adaptive filter that enhances the signal by self-adjusting the errors between the desired signal and the output signal, but the method converges slowly under low SNR. Moreover, when there are multiple line spectra in the frequency spectrum, the enhancement range of the weak line spectrum is smaller than that of the strong line spectrum.…”

Section: Discussionmentioning

confidence: 99%

“…However, their performance is poor in the case of a low input SNR. Under a low input SNR, adaptive line enhancement (ALE) is usually used to preprocess received passive-sonar signals [22]. The basic idea of adaptive line-spectrum enhancement is to extract the periodic line spectrum from the broadband noise by using the difference in correlation length between the narrowband line spectrum and the broadband noise.…”

Section: Introductionmentioning

confidence: 99%

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A Single-Hydrophone Coherent-Processing Method for Line-Spectrum Enhancement

Zhao

Xiong

et al. 2023

Remote Sensing

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Improving the line-spectrum detection capability of a single hydrophone is of great significance for the passive detection of small underwater platforms. In this paper, we propose a single-hydrophone cross-power spectrum (SHCS) method based on time-domain coherence. This method uses the coherence of the line spectrum and the non-coherence of the continuous spectrum noise to obtain coherent gain and improve the signal-to-noise ratio (SNR) of the line spectrum. The effects of the input SNR, number of averaging operations, and overlap ratio on the performance of the SHCS method under a background of Gaussian white noise are simulated and analyzed. The results show that when the overlap ratio is 0 and the number of averaging operations reaches saturation, the SHCS method can achieve the best performance and about 15 dB coherence gain is obtained. The performance of the SHCS method was verified by sea experiments. Under the extremely low input SNR, in which the line spectrum was almost completely submerged in the marine environmental noise, the SHCS method can obtain about 10 dB coherence gain. Under the conventional input SNR, in which the line spectrum could be observed, the SHCS method can obtain about 13 dB coherence gain. The results of processing the radiated noise from an actual cargo ship also demonstrate the effectiveness of the SHCS method.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Single-Hydrophone Coherent-Processing Method for Line-Spectrum Enhancement

Zhao

Xiong

et al. 2023

Remote Sensing

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show abstract

“…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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“…Improving the SNR can help improve the detection performance of passive sonars. To enhance the narrowband discrete components, passive sonars usually employ an adaptive line enhancer (ALE) as a pre‐processing step [9–11]. As an important application of the adaptive filter technique, ALEs have been used in many fields such as speech enhancement [12, 13] and biomedical signal processing [14].…”

Section: Introductionmentioning

confidence: 99%