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This paper investigates a 1.7 mm diameter ultra-weak fiber Bragg grating (UWFBG) hydrophone towed array cable for acoustic direction finding. The mechanism of the underwater acoustic waves received by this integrated-coating sensitizing optical cable is deduced, and it is shown that the amplitude of its response varies with the direction of the sound wave. An anechoic pool experiment is carried out to test the performance of such a hydrophone array. The test array is a selection of six sensing fibers, each of which is coiled into 9 cm diameter fiber ring suspended in the water to receive acoustic signals. An average sensitivity of −141.2 dB re rad/μPa at frequencies from 2.5 kHz to 6.3 kHz was achieved, validating the detection of the azimuth of underwater acoustic waves. The ultra-thin towing cable system, with free structure, high sensitivity, and underwater target-detection capability has demonstrated great potential for future unmanned underwater vehicle (UUV) applications. [ABSTRACT FROM AUTHOR]

This paper investigates a 1.7 mm diameter ultra-weak fiber Bragg grating (UWFBG) hydrophone towed array cable for acoustic direction finding. The mechanism of the underwater acoustic waves received by this integrated-coating sensitizing optical cable is deduced, and it is shown that the amplitude of its response varies with the direction of the sound wave. An anechoic pool experiment is carried out to test the performance of such a hydrophone array. The test array is a selection of six sensing fibers, each of which is coiled into 9 cm diameter fiber ring suspended in the water to receive acoustic signals. An average sensitivity of −141.2 dB re rad/μPa at frequencies from 2.5 kHz to 6.3 kHz was achieved, validating the detection of the azimuth of underwater acoustic waves. The ultra-thin towing cable system, with free structure, high sensitivity, and underwater target-detection capability has demonstrated great potential for future unmanned underwater vehicle (UUV) applications.

• In this paper, a new feature extraction and recognition method for perimeter intrusion vibration signals is proposed, using msCNN to extract multi-scale structural features of the signal, and combining features of different scales at the end of each convolutional block. • Before entering into the next convolutional block, the attention mechanism is used to recalibrate the combined features to enhance the expression ability of the features. • Finally, the time dependence is analyzed by LSTM, so that the characteristics of the signal are richer and more comprehensive, so as to obtain a higher recognition rate. To improve the recognition rate of ultra-weak fiber Bragg grating (UWFBG) arrays in perimeter security monitoring, this paper proposes a msCNN-LSTM combinatorial model recognition method, which combines an improved multi-scale convolutional neural network (msCNN) and a long short-term memory neural network (LSTM) to identify intrusion behaviors more accurately by synchronously extracting the multi-scale structural features and time-dependent relationships of vibration signals. Using msCNN to cross-learn multi-layer features, the hidden information between features at different scales can be obtained. The attention mechanism was applied to recalibrate the combined features extracted from the shallower layers, which enhanced the expression ability of features. Finally, the time dependence is analyzed by LSTM. Experimental results show that the scheme can effectively distinguish five typical intrusion behaviors, and the average recognition rate can reach 97.84%. [ABSTRACT FROM AUTHOR]

ABSTRACT We proposed an improved Φ-OTDR system based on ultra-weak fiber Bragg grating arrays to realize quantitative strain measurement using narrow pulse, which can extend the detectable fiber length. Experimental results showed that quantitative strain measurement at the end of a 4km long sensing fiber could be realized with 50ns pulses. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:2892-2894, 2016 [ABSTRACT FROM AUTHOR]