This PDF file contains the front matter associated with SPIE Proceedings Volume 10434, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

In this paper, a new modulator design suited for hybrid Lidar-radar applications is proposed and implemented. This modulator delivers a stable and tunable modulated optical pulse. Modulation frequency is in the GHz range, and associated with a bandpass filtering at the detection allow detecting a target echo embedded in the backscattering noise. This principle is known as hybrid Lidar-radar. We expose in this article theoretical principle of this new modulator and its experimental implementation. As polarization filtering can be coupled with the hybrid Lidar-radar technique to further improve target return, polarimetric sensitivity of this modulator was investigated. Since, theoretical results mismatched the experimental ones, thus, further investigations were taken. Mechanical constraint induced by mirror mount caused birefringent behavior to the mirror substrate. As this effect was not homogeneously distributed in the material, we were not being able to compensate it by modelling. However, we propose an experimental approach to solve this problem.

In this article the development of a newly designed Time-of-Flight (ToF) image sensor for underwater applications is described. The sensor is developed as part of the project UTOFIA (underwater time-of-flight image acquisition) funded by the EU within the Horizon 2020 framework. This project aims to develop a camera based on range gating that extends the visible range compared to conventional cameras by a factor of 2 to 3 and delivers real-time range information by means of a 3D video stream.

The principle of underwater range gating as well as the concept of the image sensor are presented. Based on measurements on a test image sensor a pixel structure that suits best to the requirements has been selected. Within an extensive characterization underwater the capability of distance measurements in turbid environments is demonstrated.

Laser radar 3D imaging has the potential to improve target recognition in many scenarios. One case that is challenging for most optical sensors is to recognize targets hidden in vegetation or behind camouflage. The range resolution of timeof- flight 3D sensors allows segmentation of obscuration and target if the surfaces are separated far enough so that they can be resolved as two distances. Systems based on time-correlated single-photon counting (TCSPC) have the potential to resolve surfaces closer to each other compared to laser radar systems based on proportional mode detection technologies and is therefore especially interesting. Photon counting detection is commonly performed with Geigermode Avalanche Photodiodes (GmAPD) that have the disadvantage that they can only detect one photon per laser pulse per pixel. A strong return from an obscuring object may saturate the detector and thus limit the possibility to detect the hidden target even if photons from the target reach the detector. The operational range where good foliage penetration is observed is therefore relatively narrow for GmAPD systems. In this paper we investigate the penetration capability through semi-transparent surfaces for a laser radar with a 128×32 pixel GmAPD array and a 1542 nm wavelength laser operating at a pulse repetition frequency of 90 kHz. In the evaluation a screen was placed behind different canvases with varying transmissions and the detected signals from the surfaces for different laser intensities were measured. The maximum return from the second surface occurs when the total detection probability is around 0.65-0.75 per pulse. At higher laser excitation power the signal from the second surface decreases. To optimize the foliage penetration capability it is thus necessary to adaptively control the laser power to keep the returned signal within this region. In addition to the experimental results, simulations to study the influence of the pulse energy on penetration through foliage in a scene with targets behind vegetation are presented. The optimum detection of targets occurs here at a slightly higher total photon count rate probability because a number of pixel have no obscuration in front the target in their field of view.

According to the current great interest concerning Large-Scale Metrology applications in many different fields of manufacturing industry, technologies and techniques for dimensional measurement have recently shown a substantial improvement. Ease-of-use, logistic and economic issues, as well as metrological performance, are assuming a more and more important role among system requirements. The project is planned to conduct experimental studies aimed at identifying the impact of the application of the basic laws of chip and microlasers as radiators on the linear-angular characteristics of existing measurement systems. The project is planned to conduct experimental studies aimed at identifying the impact of the application of the basic laws of microlasers as radiators on the linear-angular characteristics of existing measurement systems. The system consists of a distributed network-based layout, whose modularity allows to fit differently sized and shaped working volumes by adequately increasing the number of sensing units. Differently from existing spatially distributed metrological instruments, the remote sensor devices are intended to provide embedded data elaboration capabilities, in order to share the overall computational load.

Polarimetric information has been shown to provide means for potentially enhancing the capacity of electro-optical sensors in areas such as target detection, recognition and identification. The potential benefit must be weighed against the added complexity of the sensor and the occurrence and robustness of polarimetric signatures. While progress in the design of novel systems for snapshot polarimetry may result in compact and lightweight polarimetric sensors, the aim of this work is to report on the design, characterization and performance of a polarimetric imager, primarily designed for polarimetric signature assessment of static scenes in the long wave thermal infrared. The system utilizes the division-of-time principle and is based on an uncooled microbolometer camera and a rotating polarizing filter. Methods for radiometric and polarimetric calibrations are discussed. A significant intrinsic polarization dependency of the microbolometer camera is demonstrated and it is shown that the ability to characterize, model and compensate for various instrument effects play a crucial role for polarimetric signature assessment.

In attempt to supply a reasonable fire plume detection, multinational cooperation with significant capital is invested in the development of two major Infra-Red (IR) based fire detection alternatives, single-color IR (SCIR) and dual-color IR (DCIR). False alarm rate was expected to be high not only as a result of real heat sources but mainly due to the IR natural clutter especially solar reflections clutter. SCIR uses state-of-the-art technology and sophisticated algorithms to filter out threats from clutter. On the other hand, DCIR are aiming at using additional spectral band measurements (acting as a guard), to allow the implementation of a simpler and more robust approach for performing the same task.

In this paper we present the basics of SCIR & DCIR architecture and the main differences between them. In addition, we will present the results from a thorough study conducted for the purpose of learning about the added value of the additional data available from the second spectral band. Here we consider the two CO₂ bands of 4-5 micron and of 2.5-3 micron band as well as off peak band (guard). The findings of this study refer also to Missile warning systems (MWS) efficacy, in terms of operational value. We also present a new approach for tunable filter to such sensor.

This paper presents a method of recognition of maritime objects based on their images made by infrared sensors (FLIR – forward looking infra-red) using the time series comparison DTW method (DTW - Dynamic Time Warping). The DTW method allows to find the smallest distance between two time series when the run of time one of the series has been deformed (stretched or compressed). In the presented classifier of maritime objects images the DTW method is used to compare the combined horizontal and vertical brightness histograms for a recognized object and pattern objects. The DTW method allows to compare the histograms of objects whose FLIR images were taken at different angles. To determine the silhouette of a maritime object the Otsu segmentation algorithm is used in this paper. The paper describes the Otsu threshold method, the method of comparing time series DTW and the method of constructing combined histograms of maritime objects silhouettes. The final part of the paper presents the results of research on the developed method of maritime objects classification using a set of FLIR images registered in the Baltic Sea.

The need for capabilities of automated visual content analysis has substantially increased due to presence of large number of images captured by surveillance cameras. With a focus on development of practical methods for extracting effective visual data representations, deep neural network based representations have received great attention due to their success in visual categorization of generic images. For fine-grained image categorization, a closely related yet a more challenging research problem compared to generic image categorization due to high visual similarities within subgroups, diverse applications were developed such as classifying images of vehicles, birds, food and plants. Here, we propose the use of deep neural network based representations for categorizing and identifying marine vessels for defense and security applications. First, we gather a large number of marine vessel images via online sources grouping them into four coarse categories; naval, civil, commercial and service vessels. Next, we subgroup naval vessels into fine categories such as corvettes, frigates and submarines. For distinguishing images, we extract state-of-the-art deep visual representations and train support-vector-machines. Furthermore, we fine tune deep representations for marine vessel images. Experiments address two scenarios, classification and verification of naval marine vessels. Classification experiment aims coarse categorization, as well as learning models of fine categories. Verification experiment embroils identification of specific naval vessels by revealing if a pair of images belongs to identical marine vessels by the help of learnt deep representations. Obtaining promising performance, we believe these presented capabilities would be essential components of future coastal and on-board surveillance systems.

Detecting and tracking moving objects in real-time from an airborne infrared (IR) camera offers interesting possibilities in video surveillance, remote sensing and computer vision applications, such as monitoring large areas simultaneously, quickly changing the point of view on the scene and pursuing objects of interest. To fully exploit such a potential, versatile solutions are needed, but, in the literature, the majority of them works only under specific conditions about the considered scenario, the characteristics of the moving objects or the aircraft movements. In order to overcome these limitations, we propose a novel approach to the problem, based on the use of a cheap inertial navigation system (INS), mounted on the aircraft. To exploit jointly the information contained in the acquired video sequence and the data provided by the INS, a specific detection and tracking algorithm has been developed. It consists of three main stages performed iteratively on each acquired frame. The detection stage, in which a coarse detection map is computed, using a local statistic both fast to calculate and robust to noise and self-deletion of the targeted objects. The registration stage, in which the position of the detected objects is coherently reported on a common reference frame, by exploiting the INS data. The tracking stage, in which the steady objects are rejected, the moving objects are tracked, and an estimation of their future position is computed, to be used in the subsequent iteration. The algorithm has been tested on a large dataset of simulated IR video sequences, recreating different environments and different movements of the aircraft. Promising results have been obtained, both in terms of detection and false alarm rate, and in terms of accuracy in the estimation of position and velocity of the objects. In addition, for each frame, the detection and tracking map has been generated by the algorithm, before the acquisition of the subsequent frame, proving its capability to work in real-time.

Remote detection of toxic chemicals of very low vapour pressure deposited on surfaces in form of liquid films, droplets or powder is a capability that is needed to protect operators and equipment in chemical warfare scenarios and in industrial environments. Infrared spectroscopy is a suitable means to support this requirement. Available instruments based on passive emission spectroscopy have difficulties in discriminating the infrared emission spectrum of the surface background from that of the contamination. Separation of background and contamination is eased by illuminating the surface with a spectrally tune-able light source and by analyzing the reflectivity spectrum.

The project AMURFOCAL (Active Multispectral Reflection Fingerprinting of Persistent Chemical Agents) has the research topic of stand-off detection and identification of chemical warfare agents (CWAs) with amplified quantum cascade laser technology in the long-wave infrared spectral range. The project was conducted under the Joint Investment Programme (JIP) on CBRN protection funded through the European Defence Agency (EDA).

The AMURFOCAL instrument comprises a spectrally narrow tune-able light source with a broadband infrared detector and chemometric data analysis software. The light source combines an external cavity quantum cascade laser (EC-QCL) with an optical parametric amplifier (OPA) to boost the peak output power of a short laser pulse tune-able over the infrared fingerprint region. The laser beam is focused onto a target at a distance between 10 and 20 m. A 3D data cube is registered by tuning the wavelength of the laser emission while recording the received signal scattered off the target using a multi-element infrared detector. A particular chemical is identified through the extraction of its characteristic spectral fingerprint out of the measured data.

The paper describes the AMURFOCAL instrument, its functional units, and its principles of operation.

A matrix of optical fiber sensors eligible for remote measurements is reported in this paper. The aim of work was to monitor the air quality with a device, which does not need any electricity on site of the measurement. The matrix consists of several sensors detecting carbon dioxide concentration, relative humidity and temperature. Sensors utilize active optical materials, which change their color when exposed to varied conditions. All the sensors are powered with standard light emitting diodes. Light is transmitted by an optical fiber from the light source and then it reaches the active layer which changes its color, when the conditions change. This results in a change of attenuation of light passing through the active layer. Modified light is then transmitted by another optical fiber to the detector, where simple photoresistor is used. It is powered by a stabilized DC power supply and the current is measured. Since no expensive elements are needed to manufacture such a matrix of sensors, its price may be competitive to the price of the devices already available on the market, while the matrix also exhibits other valuable properties.

The new scheme of the reference channel of a remote gas analyzer has been offered and realized. The use of additional reflective elements with the low reflection factor allows to receive full interception of laser radiation and to provide a linear operating mode of a photodetector even for the strongest laser lines. At the measurement of a concentration of air pollutions on a differential method the weak dependence of reflection factors for used reflecting plates from wavelength can be easy to take into account by adding corresponding factors in the formula for calculations of air pollution concentrations. The application of the new optical scheme of the reference channel for the gas analyzer and the addition new correcting temperature constants in the calculations has allowed carrying out correct measurements of air pollutants with the high accuracy.

Fiber optic sensors (FOS) are insensitive to external EM fields and are intrinsically safe (as no electrical power is needed at the sensing point), so the measurement can be performed in areas where standard electronic devices cannot easily be applied. What is more, due to the very low silica fiber attenuation the measurement point can be located kilometers away from a light source and detector, which makes the sensors independent of a local power source. Furthermore the FOS are small so they can be used for sensing in mechanical mechanisms where there is not much free space. They can also be easily integrated with the structure of different materials for military applications (e.g. in tanks and airplanes).

In this work we propose an intrinsically safe temperature sensor based on fiber optic technology. The presented sensor is entirely passive and benefits from all of the advantages mentioned above, which allows it to be applied in the most demanding environments. The construction of the presented sensor is based on a dedicated microstructured optical fiber which allows both the range and sensitivity of the sensor to be adjusted to a specific application.

We present research on optical fiber sensors based on microstructured multi-core fiber. Elaborated sensor can be advantageously used in hard-to-reach areas by taking advantage of the fact, that optical fibers can play both the role of sensing elements and they can realize signal delivery. By using the sensor, it is possible to increase the level of the safety in the explosive endangered areas, e.g. in mine-like objects. As a base for the strain remote sensor we use dual-core fibers. The multi-core fibers possess a characteristic parameter called crosstalk, which is a measure of the amount of signal which can pass to the adjacent core. The strain-sensitive area is made by creating the tapered section, in which the level of crosstalk is changed. Due to this fact, we present broadened conception of fiber optic sensor designing. Strain measurement is realized thanks to the fact, that depending on the strain applied, the power distribution between the cores of dual-core fibers changes. Principle of operation allows realization of measurements both in wavelength and power domain.

Revolutionary progress in the photonic technology provides the ability to develop military systems of new properties not possible to obtain with the use of classical technologies. In recent years, this progress has resulted in developing advanced, complex, multifunctional and relatively cheap Photonic Integrated Circuits (PIC) or Hybrid Photonics Circuits (HPC) built of a collection of standardized optical, optoelectronic and photonic components. This idea is similar to the technology of Electronic Integrated Circuits, which has revolutionized the microelectronic market. The novel approach to photonic technology is now revolutionizing the photonics’ market. It simplifies the photonics technology and enables creation of technological centers for designing, development and production of advanced optical and photonic systems in the EU and other countries. This paper presents some selected photonic technologies and their impact on such defense systems like radars, radiolocation, telecommunication, and radio-communication systems.

Passive optical imaging for long range target classification has its practical limitations due to the demand on high transverse sensor resolution associated with small pixel sizes, long focal lengths and large aperture optics. It is therefore motivated to look for 1D laser range profiling for target classification which can preserve high resolution in the depth domain.

Laser range profiling is attractive because the maximum range can be substantial, especially for a small laser beam width. A range profiler can also be used in a scanning mode to detect targets within a certain sector and can also be used for active imaging when the target comes closer and is angular resolved. Although the profiling may by itself be sufficient for target classification the discrimination capabilities among a group of anticipated targets candidates may be uncertain due to uncertainty in the target aspect angles, atmospheric effects and sensor limitations. It is therefore motivated to look at a sensor fusion approach in which the profiling data is combined with imaging data even when these data have a rather low resolution. Example of both simulated and experimental data will be investigated and analyzed for target classification purposes.

Today it is easily possible to generate dense point clouds of the sensor environment using 360° LiDAR (Light Detection and Ranging) sensors which are available since a number of years. The interpretation of these data is much more challenging. For the automated data evaluation the detection and classification of objects is a fundamental task. Especially in urban scenarios moving objects like persons or vehicles are of particular interest, for instance in automatic collision avoidance, for mobile sensor platforms or surveillance tasks.

In literature there are several approaches for automated person detection in point clouds. While most techniques show acceptable results in object detection, the computation time is often crucial. The runtime can be problematic, especially due to the amount of data in the panoramic 360° point clouds. On the other hand, for most applications an object detection and classification in real time is needed.

The paper presents a proposal for a fast, real-time capable algorithm for person detection, classification and tracking in panoramic point clouds.

Primarily, a laser gated-viewing (GV) system provides range-gated 2D images without any range resolution within the range gate. By combining two GV images with slightly different gate positions, 3D information within a part of the range gate can be obtained. The depth resolution is higher (super-resolution) than the minimal gate shift step size in a tomographic sequence of the scene. For a state-of-the-art system with a typical frame rate of 20 Hz, the time difference between the two required GV images is 50 ms which may be too long in a dynamic scenario with moving objects.

Therefore, we have applied this approach to the reset and signal level images of a new short-wave infrared (SWIR) GV camera whose read-out integrated circuit supports correlated double sampling (CDS) actually intended for the reduction of kTC noise (reset noise). These images are extracted from only one single laser pulse with a marginal time difference in between.

The SWIR GV camera consists of 640 x 512 avalanche photodiodes based on mercury cadmium telluride with a pixel pitch of 15 μm. A Q-switched, flash lamp pumped solid-state laser with 1.57 μm wavelength (OPO), 52 mJ pulse energy after beam shaping, 7 ns pulse length and 20 Hz pulse repetition frequency is used for flash illumination.

In this paper, the experimental set-up is described and the operating principle of CDS is explained. The method of deriving super-resolution depth information from a GV system by using CDS is introduced and optimized. Further, the range accuracy is estimated from measured image data.

We present a pulsed all-fiber Er/Yb-doped master oscillator power amplifier at 1.55 μm wavelength. In a simple two-stage amplifier design, the source delivers 140 μJ pulses at 25 kHz and 50 kHz pulse repetition frequency, and 100 μJ at 100 kHz, with pulse durations of ∼10 ns and beam quality of M² = 1.3. The MOPA is suitable as a robust field source for ladar applications, and has been tested in an in-house developed scanning ladar system.

Small unmanned aerial vehicles (UAVs) are becoming increasingly popular and affordable the last years for professional and private consumer market, with varied capacities and performances. Recent events showed that illicit or hostile uses constitute an emergent, quickly evolutionary threat. Recent developments in UAV technologies tend to bring autonomous, highly agile and capable unmanned aerial vehicles to the market. These UAVs can be used for spying operations as well as for transporting illicit or hazardous material (smuggling, flying improvised explosive devices).

The scenario of interest concerns the protection of sensitive zones against the potential threat constituted by small drones. In the recent past, field trials were carried out to investigate the detection and tracking of multiple UAV flying at low altitude. Here, we present results which were achieved using a heterogeneous sensor network consisting of acoustic antennas, small FMCW RADAR systems and optical sensors. While acoustics and RADAR was applied to monitor a wide azimuthal area (360°), optical sensors were used for sequentially identification.

The localization results have been compared to the ground truth data to estimate the efficiency of each detection system. Seven-microphone acoustic arrays allow single source localization. The mean azimuth and elevation estimation error has been measured equal to 1.5 and -2.5 degrees respectively. The FMCW radar allows tracking of multiple UAVs by estimating their range, azimuth and motion speed. Both technologies can be linked to the electro-optical system for final identification of the detected object.

Experiences from recent conflicts show the strong need for smart sensor suites comprising different multi-spectral imaging sensors as core elements as well as additional non-imaging sensors. Smart sensor suites should be part of a smart sensor network – a network of sensors, databases, evaluation stations and user terminals. Its goal is to optimize the use of various information sources for military operations such as situation assessment, intelligence, surveillance, reconnaissance, target recognition and tracking. Such a smart sensor network will enable commanders to achieve higher levels of situational awareness.

Within the study at hand, an open system architecture was developed in order to increase the efficiency of sensor suites. The open system architecture for smart sensor suites, based on a system-of-systems approach, enables combining different sensors in multiple physical configurations, such as distributed sensors, co-located sensors combined in a single package, tower-mounted sensors, sensors integrated in a mobile platform, and trigger sensors. The architecture was derived from a set of system requirements and relevant scenarios. Its mode of operation is adaptable to a series of scenarios with respect to relevant objects of interest, activities to be observed, available transmission bandwidth, etc.

The presented open architecture is designed in accordance with the NATO Architecture Framework (NAF). The architecture allows smart sensor suites to be part of a surveillance network, linked e.g. to a sensor planning system and a C4ISR center, and to be used in combination with future RPAS (Remotely Piloted Aircraft Systems) for supporting a more flexible dynamic configuration of RPAS payloads.

Small target detection in wide-area surveillance is a challenging task. Current imaging staring sensors in practical systems are characterized by large pixel counts and wide field of view (WFOV). Therefore, it is not suitable to detect targets simply via a single algorithm in different background types. We solve this problem by local windowing approach and take different background suppression and target enhancement methods for different surveillance scenarios. Monte Carlo simulations are provided and the experimental results demonstrate that we can effectively detect dim small targets with a very low false alarm rate and an acceptable detection rate in the proposed detection architecture.

Protection of persons and assets is the key challenge of Smart City safeguards technologies. Conventional security technologies are often outdated and easy to breach. Therefore, new technologies that could complement existing systems or replace them are developed. The use of optical fibers and their subsequent application in sensing is a trend of recent years. This article discusses the use of fiber-optic sensors in perimeter protection. The sensor consists of optical fibers and couplers only and being constructed without wires and metal parts bring many advantages. These include an absence of interference with electromagnetic waves, system presence can be difficult to detect as well as affect its operation. Testing installation of perimeter system was carried out under reinforced concrete structure. Subjects walked over the bridge at different speeds and over the different routes. The task for the system was an absolute detection of all subjects. The proposed system should find application mainly in areas with the presence of volatile substances, strong electromagnetic fields, or in explosive areas.