Detection of underwater acoustic transient signals under Alpha stable distribution noise

CHEN Wen; ZOU Nan; ZHANG Guangpu; LI Yanhe

doi:10.11999/JEIT250500

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CHEN Wen, ZOU Nan, ZHANG Guangpu, LI Yanhe. Detection of underwater acoustic transient signals under Alpha stable distribution noise[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250500

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CHEN Wen, ZOU Nan, ZHANG Guangpu, LI Yanhe. Detection of underwater acoustic transient signals under Alpha stable distribution noise[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250500

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Detection of underwater acoustic transient signals under Alpha stable distribution noise

doi: 10.11999/JEIT250500 cstr: 32379.14.JEIT250500

CHEN Wen^{1, 2, 3},
ZOU Nan^{1, 2, 3
,
,},
ZHANG Guangpu^{1, 2, 3},
LI Yanhe^{1, 2, 3}

1.
National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University), Ministry of Industry and Information Technology; Harbin 150001, China
3.
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China

Accepted Date: 2025-11-12
Rev Recd Date: 2025-11-12

Available Online: 2025-11-17

Abstract

Abstract

Objective Transient signals are generated by state changes of underwater acoustic targets and are difficult to suppress or eliminate, thus becoming an important means for covert underwater target detection. However, practical marine environmental noise exhibits non-Gaussian characteristics, such as impulsive spikes.That severely degrade or even invalidate traditional Gaussian-based detection methods, particularly energy detection widely used in engineering applications. While existing studies employ nonlinear transformations or fractional lower-order statistics to address non-Gaussian noise, there are limitations such as signal distortion and high computational complexity. To overcome these challenges, the Alpha-stable distribution is adopted to replace traditional Gaussian modeling, and a Data Preprocessing denoising- Short-Time Cross-Correntropy Detection (DP-STCCD) method is proposed to achieve passive detection and Time of Arrival (ToA) estimation for unknown deterministic transient signals in non-Gaussian noise environments. Methods The proposed method comprises two stages: data preprocessing denoising and short-time cross-correntropy detection. In the data preprocessing stage, an outlier detection technique based on the Interquartile Range (IQR) is applied. Upper and lower thresholds are calculated to effectively remove impulsive spikes while preserving local signal features. Then multi-stage filtering is employed to further suppress noise: median filtering reconstructs the signal with minimal detail loss, modified mean filtering eliminates residual spikes by discarding extreme values in local windows. In the detection stage, the denoised signal is segmented into frames. Short-time cross-correntropy based on a Gaussian kernel is computed between adjacent frames to construct detection statistics. A first-order recursive filter estimates background noise to set detection thresholds. Joint amplitude-width decision logic generates detection results. ToA estimation is achieved by identifying peaks in the short-time cross-correntropy. This method eliminates dependence on prior noise knowledge and enhances robustness in non-Gaussian environments through data cleaning and information-theoretic feature extraction. Results and Discussions Simulations under standard symmetric Alpha-stable distributed noise validate the algorithm’s performance. The data preprocessing denoising algorithm effectively eliminates impulsive spikes while retaining critical time-domain signal characteristics (Fig.3). After denoising, the detection performance of the energy detector is partially restored. And the peak-to-average ratio of short-time cross-correntropy features improves by 10 dB (Fig. 4, Fig. 5). Experimental results demonstrate that DP-STCCD significantly outperforms Data Preprocessing denoising-Energy Detection(DP-ED) in both detection probability and ToA estimation accuracy under identical conditions. At the characteristic index $ \mathrm{\alpha } $=1.5 and Generalized SNR (GSNR) of -11 dB, DP-STCCD achieves a 30.2% higher detection probability and an 18.4% improvement in ToA estimation precision compared to DP-ED (Fig. 6, Fig. 9(a)). These results validate the effectiveness and robustness of the proposed method in complex noise environments. Conclusions A joint detection method DP-STCCD integrating data preprocessing denoising and short-time cross-correntropy features is proposed to address underwater transient signal detection under Alpha-stable distributed noise. Preprocessing techniques, including IQR-based outlier detection and multi-stage filtering, effectively suppress impulsive interference while preserving key signal characteristics. The short-time cross-correntropy feature enhances detection sensitivity and ToA estimation accuracy. Results indicate that the proposed method outperforms traditional energy detectors under low GSNR conditions and maintains superior stability across varying characteristic indices. This study provides a novel approach for covert underwater target detection in non-Gaussian noise environments. Future work will focus on optimizing the algorithm for practical marine noise interference to enhance its engineering applicability.
- Non-Gaussian noise,
- Alpha-stable distribution,
- Correntropy,
- Transient signal detection

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References(25)

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