Although fiber-optic distributed acoustic sensing (DAS) has been widely applied for intrusion recognition in the perimeter security field, challenges still remain for high-accuracy recognition of new types of intrusion events. Drone, as a non-contact, stealthy intrusion event, has not been reported to achieve effective monitoring and recognition by employing the fiber-optic DAS technology. By introducing drone intrusion, the scope of the threatening intrusion in the perimeter security monitoring field is extended. This study proposes a multi-source threatening event recognition scheme targeting drone intrusion in the fiber optic DAS system. To achieve this objective, a dual-stage recognition method is proposed. Besides, the wavelet denoising method is applied to extract the effective signal from weak disturbances introduced by drone flight. The variational mode decomposition (VMD)-based hybrid feature vector is formed to remove the noise and further extract the effective signal characteristics. Next, we demonstrate a hybrid model framework based on convolutional neural network (CNN)+long short term memory (LSTM)+self-attention mechanism to achieve the effective recognition of multi-source threatening event targeting drone intrusion. Particularly, we thoroughly discuss the distinguishing ability between drone intrusion and non-threatening wind blowing, and the recognition ability for the simultaneous occurrence of both human-contact intrusion and non-contact drone intrusion. The experimental results show that, the proposed recognition scheme can effectively distinguish the drone intrusion from the non-threatening wind blowing event with a high accuracy of 100%. In addition, when drone intrusion, environmental disturbances and human-contact intrusion occur simultaneously, a high recognition accuracy of 96.25% is achieved with a fast response time of 0.733 s.

Common-mode noises (CMNs) are frequent in the fiber optic accelerometer, and their suppression is extremely important, particularly in the ultra-weak signal detection application, e.g., micro-seismic monitoring. This Letter proposes a 3-component (3C) low-reflectivity fiber Bragg gratings accelerometer for CMN self-suppression. When compared with the traditional CMN suppression method, the proposed 3C accelerometer is able to improve the CMN suppression effect by an average value larger than 4.5 dB in three axes, as well as double the effective signal amplitude due to the push–pull structure, which brings an enhanced signal-to-noise ratio. Besides, the proposed 3C accelerometer does not need an additional reference interferometer to achieve such a CMN suppression effect; hence, it largely reduces the volume and cost of the sensing system, which shows huge advantages, particularly in the large-scale quasi-distributed array. The proposed 3C accelerometer provides a promising candidate for the weak vector vibration detection.

Efficient suppression of common-mode noises (CMNs) is significant for the performance of interferometric fiber-optic sensor system. To suppress CMNs, a reference interferometer is normally required as noise canceller. In this paper, we firstly report a detailed correlation analysis between the outputs of the sensing and reference interferometers. By using a band-limited noise decomposing method, we show that the correlation coefficient of the two interferometer outputs is a strong function over their mean phase difference, which appears as the limiting factor of the CMN suppression effect. Based on the analysis, we propose a novel CMN suppression technique by locking the mean phase value of the sensing interferometer using a reference interferometer with a 3 × 2 coupler. Experimental results show that our technique can increase the correlation coefficient between the outputs of sensing and reference interferometers by 6% (to 0.93) and reduce its standard deviation by ∼70% compared to traditional method. The CMNs suppression factor is measured as −8.49 dB in the frequency range from 300 Hz to 1 kHz, comparing to ∼−6 dB achieved by traditional method of simple subtraction. Our results set a new benchmark on the noise level of interferometric fiber-optic sensors.

This study proposes a multiple intrusion event recognition scheme in a fiber optic distributed acoustic sensing (DAS) system. The variational mode decomposition (VMD) algorithm is selected for signal decomposition, and VMD-based short-time energy encoding is firstly proposed for feature extraction. Besides, short-time energy encoding is combined with VMD-based short-time zero-crossing rate and kurtosis to form the hybrid feature vector, and therefore sufficient feature information of the intrusion event signals can be well retrieved, which eventually improves the recognition performances. Experimental results demonstrate that the no-human wind disturbance event and three types of human intrusion events can be 100 % successfully recognized, and the overall recognition rate and recognition time of four types of intrusion events are 98.4 % and 0.207 s by employing the optimized recognition scheme, which has been improved over the previously reported event recognition schemes. Furthermore, we preliminarily demonstrate the multi-point and multi-event recognition based on the proposed fiber optic DAS system with optimized event recognition scheme, showing great potential for intelligent security monitoring applications.

In this work, we thoroughly evaluate different types of lateral-offset Mach-Zehnder interferometers (MZIs) to search for the optimized design. Three different structures (SNS, SMNMS, and SNNNS. S: single-mode fiber; N: no-core fiber; M: multi-mode fiber) are proposed and their performances are compared numerically. Besides, for the SNNNS sensor, the effect of different no-core fiber (NCF) diameters on the sensor performance is discussed. The numerical analyses demonstrate that the SNNNS sensor with NCF diameter of 61.5μm shows the best performance among all the sensors. The experimental results confirm that, when compared with the SNS sensor, the SNNNS sensor is insensitive to the lateral-offset distance. It ensures an excellent interference performance within a large offset range, which facilitates the sensor fabrication. The optimized design of the sensor device shows the potential integration applications in the ultra-low concentration biomolecule detection field.

Aliased noises elevate the ultimate noise level of a pulse-triggered time-division-multiplexing (TDM) interferometric fiber-optic sensor array. Here we for the first time present an analytical model of the aliasing factor based on noise power spectral density analysis of the interferometric fiber sensor array. In contrast to the previous reports, the aliasing factor is found not only dependent on the pulse width and the repetition frequency, but also on the rising time of the pulse when matched filters are applied to suppress the noise components out of the main lobe. The model also indicates the explicit scaling rule between the aliasing factor and the number of TDM channels. The model are experimentally validated by measuring the noise floors under different combinations of pulse parameters. Based on the model, an ultra-low noise floor of -114 dB ref rad/ $\sqrt{\mathbf {Hz}}$ (i.e. 2 $\boldsymbol{\mu}$ rad/ $\sqrt{\mathbf {Hz}}$ ) is achieved and up to 6 interferometric sensors can be multiplexed, which, to the best of knowledge, represents the lowest noise level to date for the TDM interferometric fiber-optic sensor array. Our results reveal the prominent influence of the pulse rising time on the aliased noises and provides a practical approach to suppress noise floor of TDM sensor array.