Stochastic resonance with reinforcement learning for underwater acoustic communication signal

Qiu, Yinwen; Yuan, Fei; Ji, Shuyao; Cheng, En

doi:10.1016/j.apacoust.2020.107688

Cited by 21 publications

(10 citation statements)

References 23 publications

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“…From Fig. 1, the half width of potential well x m = a b and the potential height ∆V = a 2 4b [31]. The critical conditions for the particle to skip the potential barrier are given by…”

Section: A Methodsmentioning

confidence: 99%

A New Index for Detecting Frequency of Unknown Underwater Weak Signals with Genetic Algorithm

Yu¹,

Li²

2022

IJACSA

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In this paper, a new index is proposed for detecting the frequency of unknown underwater signals based on the stochastic resonance theory. When the received weak signal is input into the stochastic resonance system, first, by frequency analysis, the frequency with the highest amplitude Am of the output signal spectrum is considered as the pre-detection frequency. Then a cosine signal with the pre-detection frequency and unit amplitude is constructed. Define the pre-signal-to-noiseratio as the logarithm of the squared amplitude Am over the mean of signal amplitudes in all other frequencies. The new index is defined as the product of the pre-signal-to-noise-ratio and the correlation coefficient between the received unknown signal and the constructed cosine signal. The new index is featured by taking into account the signal characteristics in both time and frequency domain, and it will yield better signal frequency detection performance. In addition, to improve the time efficiency of the frequency detection, a method to bound the searching range, keyed to the genetic algorithm, of the stochastic resonance system parameters is proposed. The method can be used to detect the frequency of both single frequency and frequency-hopping unknown signals. With the designed new index and system parameter bounding method, the simulations and experiments for the weak underwater unknown signals are conducted. Compared to the piecewise mean value index and weighted power spectral kurtosis index, the new index yields a higher detection probability at varied input signal-to-noise ratios and signal frequencies. With bounding system parameter searching ranges, the time efficiency is improved. The main purpose of this paper is to detect the frequency of unknown underwater weak signals by stochastic resonance system with genetic algorithm. The main contributions are summarized as follows. First, the detection probability of weak signals is improved by stochastic resonance system with the proposed signal detection index than some other indexes. Second, to improve the time efficiency of the signal frequency detection, a method to bound the searching range of system parameters is proposed.

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“…From Fig. 1, the half width of potential well x m = a b and the potential height ∆V = a 2 4b [31]. The critical conditions for the particle to skip the potential barrier are given by…”

Section: A Methodsmentioning

confidence: 99%

A New Index for Detecting Frequency of Unknown Underwater Weak Signals with Genetic Algorithm

Yu¹,

Li²

2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…Alberto [19] proposes a novel approach based on a computationally and energy-efficient deep convolutional denoising autoencoder to reduce the noise interference. Qiu [20] proposes a reinforcement learning system that requires sophisticated design and critical parameter choice to meet its oscillatory condition to keep the balance among signals, noise, and the nonlinear system. Li [21] proposes an approach based on relativistic conditional generative adversarial networks (RCGAN) to resolve the conditions of complex marine ambient noise and scarce data.…”

Section: Introductionmentioning

confidence: 99%

Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network

2024

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The presence of natural ambient noise interferes with the system for locating and identifying underwater targets. This paper suggests that a Dual-Path Transformation Network(DPTN) reduces ambient noise in underwater acoustic signals. First, the input acoustic signals' higher-order non-linear features are extracted using a multi-scale convolutional encoder neural network. Second, sub-vectors with the same length are created according to the time dimension from the higher-order non-linear features. The subvectors are stitched together to form a three-dimensional tensor. Third, a neural network transformer based on the feed-forward network is constructed. Further, to capture long-term series features and separate the target signal from the noisy signals, the three-dimensional tensor is used as the input of the transformerbased masking network. Finally, overlap-add and transpose are used to obtain discernible target signals. The experimental results verify the effectiveness of the proposed underwater acoustic signal denoising algorithm and demonstrate that the proposed DPRN method can obtain higher output signal-to-noise ratio (SNR) and the scale-invariant signal-to-noise ratio (SI-SNR) compared with the other classical algorithms.

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“…Alberto [22] proposes a novel, energy-efficient deep convolutional denoising auto-encoder approach to reduce noise interference. Qi [23] proposes a reinforcement learning system that requires sophisticated design and critical parameter choice to meet its oscillatory condition to keep the balance among signal, noise, and the nonlinear system. Koh [24] proposes an approach based on U-net to resolve the conditions of complex marine ambient noise and scarce data.…”

Section: Introductionmentioning

confidence: 99%

A novel noise reduction technique for underwater acoustic signals based on dual‐path recurrent neural network

2022

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A dual‐path recurrent neural network model is proposed to achieve noise reduction of underwater acoustic signals, which consists of three steps: feature extraction, mask separation, and signal recovery. For feature extraction, we use a multi‐scale convolutional neural network to extract higher‐order non‐linear features of the input signal and chunk the obtained non‐linear features into subvectors with fixed lengths according to the temporal dimension. In the mask separation, two recurrent neural networks based on constructing a dual‐path network, a bidirectional network for extracting intra‐features, and a directional network for extracting inter‐features is build. Finally, overlapping and permutation neural networks are used to recover the denoised acoustic signal. By comparing different denoising methods, it can be seen that this method is effective in underwater acoustic signals. By evaluating the two tasks of the ShipsEar dataset, this model can improve the signal‐to‐noise ratio by 12.02 and 9.48 dB, respectively.

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Stochastic resonance with reinforcement learning for underwater acoustic communication signal

Cited by 21 publications

References 23 publications

A New Index for Detecting Frequency of Unknown Underwater Weak Signals with Genetic Algorithm

A New Index for Detecting Frequency of Unknown Underwater Weak Signals with Genetic Algorithm

Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network

A novel noise reduction technique for underwater acoustic signals based on dual‐path recurrent neural network

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