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

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

Funds: The National Key Laboratory Fund for Underwater Acoustic Technology (KY10500220062), Fund of Key Laboratory of Sonar Technology(2023-JCJQ-LB-32/09)

Received Date: 2025-06-03
Accepted Date: 2025-11-12
Rev Recd Date: 2025-11-09

Available Online: 2025-11-17

Abstract

Abstract

Objective Transient signals are generated during changes in the state of underwater acoustic targets and are difficult to suppress or remove. Therefore, they serve as an important basis for covert detection of underwater targets. Practical marine noise exhibits non-Gaussian behavior with impulsive components, which degrade or disable conventional Gaussian-based detectors, including energy detection commonly used in engineering systems. Existing approaches apply nonlinear processing or fractional lower-order statistics to mitigate non-Gaussian noise, yet they face drawbacks such as signal distortion and increased computational cost. To address these issues, an Alpha-stable noise model is adopted. A Data-Preprocessing denoising Short-Time Cross-Correntropy Detection (DP-STCCD) method is proposed to enable passive detection and Time-of-Arrival (ToA) estimation for unknown deterministic transient signals in non-Gaussian underwater environments. Methods The method consists of two stages: data-preprocessing denoising and short-time cross-correntropy detection. In the preprocessing stage, an outlier detection approach based on the InterQuartile Range (IQR) is used. Upper and lower thresholds are calculated to remove impulsive spikes while retaining local signal structure. Multi-stage filtering is then applied to further reduce noise. Median filtering reconstructs the signal with limited detail loss, and modified mean filtering suppresses remaining spikes by discarding extreme values within local windows. In the detection stage, the denoised signal is divided into short frames. Short-time cross-correntropy with a Gaussian kernel is calculated between adjacent frames to form the detection statistic. A first-order recursive filter estimates background noise and determines adaptive thresholds. Detection outputs are generated using joint amplitude–width decision rules. ToA estimation is performed by locating peaks in the short-time cross-correntropy. The method does not require prior noise information and improves robustness in non-Gaussian environments through data cleaning and information-theoretic feature extraction. Results and Discussions Simulations under symmetric Alpha-stable noise verify the effectiveness of the method. The preprocessing stage removes impulsive spikes while preserving key temporal features (Fig. 3). After denoising, the performance of energy detection shows partial recovery, and the peak-to-average ratio of short-time cross-correntropy features increases by 10 dB (Fig. 4, Fig. 5). Experimental results show that DP-STCCD provides higher detection probability and improved ToA estimation accuracy compared with Data Preprocessing denoising-Energy Detection(DP-ED). Under conditions with characteristic index α=1.5 and a Generalized Signal-to-Noise Ratio (GSNR) of −11 dB, DP-STCCD yields a 30.2% improvement in detection probability and an 18.4% increase in ToA estimation precision relative to the comparative method (Fig. 6, Fig. 9(a)). These findings confirm the robustness and detection capability of the proposed approach in non-Gaussian underwater noise environments. Conclusions A joint detection method, DP-STCCD, combining data-preprocessing denoising and short-time cross-correntropy features is proposed for transient signal detection under Alpha-stable noise. Preprocessing approaches based on IQR outlier detection and multi-stage filtering suppress impulsive interference while preserving key time-domain characteristics. Short-time cross-correntropy improves detection sensitivity and ToA estimation accuracy. The results show that the proposed method provides better performance than traditional energy detection under low GSNR and maintains stable behavior across different characteristic indices. The method offers a feasible solution for covert underwater target detection in non-Gaussian environments. Future work will optimize the algorithm for real marine noise and improve its engineering applicability.
- Non-Gaussian noise,
- Alpha-stable distribution,
- Correntropy,
- Transient signal detection

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