We present numerical and experimental results on a new generation fibre optic perimeter sensor based on a Sagnac and Michelson interferometers configuration. In particular, an original signal processing scheme is presented. The sensor can detect a potential intruder and determine its position along a protected zone. We propose a localization method that offers the inherent properties of both interferometers. After demodulation of the signals from both interferometers, the obtained amplitude characteristic of the Sagnac interferometer depends on a position of a disturbance along the interferometer, while amplitude characteristic of the Michelson interferometer does not depend on this position. So, quotient of both demodulated characteristics makes it possible to localize the disturbance. During investigations of a laboratory model of the sensor, it was possible to detect the position of the disturbance with a resolution of about 40 m along the 6-km-long sensor.

Widespread availability of drones is associated with many new fascinating possibilities, which were reserved in the past for few. Unfortunately, this technology also has many negative consequences related to illegal activities (surveillance, smuggling). For this reason, particularly sensitive areas should be equipped with sensors capable of detecting the presence of even miniature drones from as far away as possible. A few techniques currently exist in this field; however, all have significant drawbacks. This study addresses a novel approach for small (<5 kg) drones detection technique based on a laser scanning and a method to discriminate UAVs from birds. The latter challenge is fundamental in minimizing the false alarm rate in each drone monitoring equipment. The paper describes the developed sensor and its performance in terms of drone vs. bird discrimination. The idea is based on simple cross-polarization ratio analysis of the optical echo received as a result of laser backscattering on the detected object. The obtained experimental results show that the proposed method does not always guarantee 100 percent discrimination efficiency, but provides certain confidence level distribution. Nevertheless, due to the hardware simplicity, this approach seems to be a valuable addition to the developed anti-drone laser scanner.

This paper aims to introduce a standardized test methodology for drone detection, tracking, and identification systems. It is the aim that this standardized test methodology for assessing the performance of counter-drone systems will lead to a much better understanding of the capabilities of these solutions. This is urgently needed, as there is an increase in drone threats and there are no cohesive policies to evaluate the performance of these systems and hence mitigate and manage the threat. The presented methodology has been developed within the framework of the project COURAGEOUS funded by European Union’s Internal Security Fund Police. This standardized test methodology is based upon a series of standard user-defined scenarios representing a wide set of use cases. At this moment, these standard scenarios are geared toward civil security end users. However, the proposed standard methodology provides an open architecture where the standard scenarios can be modularly extended, providing standard users the possibility to easily add new scenarios. For each of these scenarios, operational needs and functional performance requirements are provided. Using this information, an integral test methodology is presented that allows for a fair qualitative and quantitative comparison between different counter-drone systems. The standard test methodology concentrates on the qualitative and quantitative evaluation of counter-drone systems. This test methodology was validated during three user-scripted validation trials.

We compared terahertz transmission spectra of explosives measured by means of Time Domain Spectroscopy (TDS) and an Optical Parametric Oscillator-based system (OPO). Reflection spectra of pure explosives were measured in reflection configuration by means of TDS in the range 0.3-2.5THz. We also analyzed influence of the surface roughness on reflectance and phase spectra of RDX-plastic based explosives. Next, we present a thermal phantom of human body working in THz range, which was developed for testing of THz cameras. We demonstrate the possibility of improvement of the quality of the image captured by a commercially available passive THz camera.

There are many separated infrastructural objects within a harbor area that may be considered "critical", such as gas and oil terminals or anchored naval vessels. Those objects require special protection, including security systems capable of monitoring both surface and underwater areas, because an intrusion into the protected area may be attempted using small surface vehicles (boats, kayaks, rafts, floating devices with weapons and explosives) as well as underwater ones (manned or unmanned submarines, scuba divers). The paper will present the concept of multisensor security system for a harbor protection, capable of complex monitoring of selected critical objects within the protected area. The proposed system consists of a command centre and several different sensors deployed in key areas, providing effective protection from land and sea, with special attention focused on the monitoring of underwater zone. The initial project of such systems will be presented, its configuration and initial tests of the selected components. The protection of surface area is based on medium-range radar and LLTV and infrared cameras. Underwater zone will be monitored by a sonar and acoustic and magnetic barriers, connected into an integrated monitoring system. Theoretical analyses concerning the detection of fast, small surface objects (such as RIB boats) by a camera system and real test results in various weather conditions will also be presented.

Smuggling of drugs and cigarettes in small inflatable boats across border rivers is a serious threat to the EU’s financial interests. Early detection of such threats is challenging due to difficult and changing environmental conditions. This study reports on the automatic detection of small inflatable boats and people in a rough wild terrain in the infrared thermal domain. Three acquisition campaigns were carried out during spring, summer, and fall under various weather conditions. Three deep learning algorithms, namely, YOLOv2, YOLOv3, and Faster R-CNN working with six different feature extraction neural networks were trained and evaluated in terms of performance and processing time. The best performance was achieved with Faster R-CNN with ResNet101, however, processing requires a long time and a powerful graphics processing unit.