Among all optical sensing techniques, the distributed Sagnac loop (SI) sensor has the advantage of being simple to implement with low cost. Most of the proposed techniques for using SI exploit the frequency null method for event localization. However, such a technique suffers from the low spectrum signal power, complicating event localization under environmental noise. In this work, event localization using time-domain instead of frequency null signals is achieved using machine learning (ML), which is increasingly being exploited in many science fields, including sensing applications. First, a training dataset that includes 200 events is generated over a 50 km effective sensing fiber. These time-domain signals are considered as features for training the ML algorithm. Then, the random forest (RF) ML algorithm is used to develop a model for event location prediction. The results show the capability of ML in predicting the event’s location with 55 m mean absolute error (MAE). Further, the percentage of test realizations with prediction error > 200 m is 0.7%. The sensing signal bandwidth is investigated, showing better performance results for sensing signals of larger bandwidths. Finally, the proposed model is validated experimentally. The results showed good accuracy with MAE < 100 m.

This paper demonstrates an intruder detection system using a strain-based optical fiber Bragg grating (FBG), machine learning (ML), and adaptive thresholding to classify the intruder as no intruder, intruder, or wind at low levels of signal-to-noise ratio. We demonstrate the intruder detection system using a portion of a real fence manufactured and installed around one of the engineering college’s gardens at King Saud University. The experimental results show that adaptive thresholding can help improve the performance of machine learning classifiers, such as linear discriminant analysis (LDA) or logistic regression algorithms in identifying an intruder’s existence at low optical signal-to-noise ratio (OSNR) scenarios. The proposed method can achieve an average accuracy of 99.17% when the OSNR level is <0.5 dB.

In optical sensing applications such as pipeline monitoring and intrusion detection systems, accurate localization of the event is crucial for timely and effective response. This paper experimentally demonstrates multievent localization for long perimeter monitoring using a Sagnac interferometer loop sensor and machine learning techniques. The proposed method considers the multievent localization problem as a multilabel multiclassification problem by dividing the optical fiber into 250 segments. A deep neural network (DNN) model is used to predict the likelihood of event occurrence in each segment and accurately locate the events. The sensing loop comprises 106.245 km of single-mode fiber, equivalent to ∼50 km of effective sensing distance. The training dataset is constructed in simulation using VPItransmissionMaker, and the proposed machine learning model’s complexity is reduced by using discrete cosine transform (DCT). The designed DNN is tested for event localization in both simulation and experiment. The simulation results show that the proposed model achieves an accuracy of 99% in predicting the location of one event within one segment error, an accuracy of 95% in predicting the location of one event out of the two within one segment error, and an accuracy of 78% in predicting the location of the two events within one segment error. The experimental results validate the simulation ones, demonstrating the proposed model’s effectiveness in accurately localizing events with high precision. In addition, the paper includes a discussion on extending the proposed model to sense more than two events simultaneously.

In this work, we investigate the performance of an ambiguity function-shaped waveform (AFSW) using a millimeter-wave photonics-based radar system at 100 GHz. An AFSW is a radar waveform whose ambiguity function can be shaped to increase the peak-to-sidelobe ratio (PSR) for better detectability of targets in a desired range/velocity region. To the best of the authors’ knowledge, this paper is the first in the literature that investigates the performance of such a waveform in a photonics-based radar system. We experimentally compare the AFSW performance to the conventional frequency-modulated continuous wave (FMCW). The experimental results show the ability of the AFSW to achieve a PSR of 38.35 dB compared to the PSR of 14.5 dB obtained using the conventional FMCW. Moreover, we investigate the effects of some optical system impairments on the AFSW, such as: (i) optical modulator nonlinearity, (ii) optical modulator bias drift, and (iii) sampling offset error between the transmitter and receiver.