Brillouin optical time-domain analyzer (BOTDA) is used to monitor the temperature and strain along a fiber. So far, neural network and machine learning methods have been successfully applied for temperature extraction. But for different frequency scanning steps, different networks should be designed and trained. Here, a BOTDA assisted by kernel extreme learning machine (K-ELM) with high generalization is proposed and experimentally demonstrated. By utilizing K-ELM, the raw Brillouin gain spectra measured from BOTDA system are classified into different temperature classes. The performance of K-ELM is investigated both in simulation and experiment under different cases of signal-to-noise ratios, pump pulse widths, and frequency scanning steps. Compared with curve fitting methods, the K-ELM algorithm has better measurement accuracy of 0.3 and it realizes great improvement of the processing speed over 120 times. The ultrafast processing speed, high accuracy and universality make K-ELM become a highly competitive candidate for the high-speed BOTDA sensing system.

The fiber-optic hydrophone (FOH) provides new possibilities for underwater acoustic detection and surveillance, particularly when integrated with autonomous underwater vehicles (AUVs). Its lightweight, high sensitivity, and ease of multiplexing are significant advantages. Miniaturizing the FOH and incorporating multiple units into a thin towed array suitable for AUVs, however, presents a challenge. In this study, we present the design and implementation of a 48-element thin FOH towed array, boasting an outer diameter of only 20 mm. The average pressure sensitivity of the FOH is approximately -140.2 dB re rad/μPa, with a fluctuation of less than ± 1 dB across a frequency range of 20 Hz – 4 kHz. This wide operating frequency range and high sensitivity align well with the demands of AUVs for underwater acoustic detection and surveillance. The average minimum detectable pressure of the array meets practical application requirements, even when compared to ambient sea noise. Noise characterization was assessed through towing tests in real-world environments. Results indicate that flow noise, dominant in the low-frequency band (< 200 Hz), is unlikely to be a limiting factor in practical applications, as the design frequencies of the array are located outside this range. The directional of arrival (DOA) estimation of the fixed and moving sources was successfully demonstrated. Bearings obtained using a conventional beamformer closely matched the true values, confirming the feasibility of the thin FOH towed array for practical DOA estimation. These findings are instrumental for the future implementation of thin FOH towed arrays on AUVs.

Signal detection and format recognition are critical and challenging tasks across civil and military sectors. However, they often encounter signal truncation issues during online signal processing, resulting in inaccurate predictions due to incomplete features. To address this issue, we herein propose the signal detection mask long short-term memory (SDM-LSTM) network module. The SDM-LSTM module facilitates seamless transmission of sequential signal features among blocks, even in scenarios facing signal truncation. Our approach involves organizing online-acquired signal samples into short blocks and conducting signal feature extraction and prediction for each block. This methodology ensures that each block retains the cumulative features from preceding blocks, thereby maintaining the continuity of signal features. Consequently, signal truncation no longer induces feature incompleteness. The proposed model, leveraging the SDM-LSTM module, offers predictions for each block, aligning with the sequence-to-sequence prediction paradigm. The simulations and experimental results validate the efficacy of our model in addressing signal truncation and facilitating online signal processing.

Detecting wireless communication signals in low signal-to-noise ratio (SNR) environments is challenging due to noise interference and frequency overlap. Traditional array processing techniques rely on direction-of-arrival (DOA) estimation and beamforming before detection and recognition, often resulting in high computational complexity and potential error accumulation. To address these challenges, this paper proposes a spatiotemporal spectrum neural network (STSNN), an end-to-end multitask learning framework that directly processes array signals, performing joint signal detection, modulation recognition, frequency band estimation, and DOA estimation in a unified model. STSNN first applies a two-dimensional Fourier transform (2D-FFT) to compute the spatiotemporal spectrum, then identifies a distinctive spatiotemporal center keypoint to enhance multitask learning efficiency. Extensive simulations and real-world experiments confirm its effectiveness, with a 16-element array achieving an F1-score of 0.989 at −15 dB SNR, significantly outperforming single-channel spectrogram-based methods (0.470). As a data-driven approach, STSNN remains robust to array element position errors, ensuring reliability in diverse deployment scenarios. By eliminating the need for explicit DOA estimation and beamforming, STSNN simplifies the processing pipeline, enhances detection robustness, and offers a scalable solution for low-SNR signal detection and estimation in radio frequency, sonar, and other array-based systems.