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The observable infrared signature of naval platforms is crucial in determining survivability and is therefore an important issue in warfare. Increased detectability in the EM spectrum leads to higher susceptibility against threats like sea skimming anti-ship missiles. The infrared platform signature is not a fixed quantity but a highly dynamic property that varies significantly depending on the prevailing conditions. To reduce susceptibility, active reduction measures can be deployed that reduce surface and exhaust gas temperatures and therefore actively obfuscate the signature. Since infrared decoy countermeasures compete with the platform signature, these reduction measures can positively affect countermeasure effectiveness, enabling enhanced interruption of the kill chain. To enable accurate signature reduction, improved awareness of the platform’s current signature is required. A sensor driven system on board of a platform, combined with specialized modelling capacity is capable of monitoring the current and predicting the near future signature. The output of such a system can subsequently be used for improved countermeasure deployment by linking it to an onboard Electronic Warfare Tactical Decision Aid (EWTDA). This paper addresses a methodology for evaluating the effect of different signature reduction measures on decoy effectiveness by using engagement simulations. The importance of the fidelity in background (e.g. clutter statistics) and ship signature modelling is also discussed. Finally, an example will be given of a concept signature monitoring system consisting of sensors and signature models which is currently being deployed on an operational Royal Netherlands Navy (RNLN) vessel. Future versions of this system are expected to provide input to the onboard EWTDA to enable more effective decoy deployments.

Infrared imaging of the sea surface is used for many purposes, such as remote sensing of large oceanographic structures, environmental monitoring, surveillance applications and platform signature research. Many of these studies rely on determining the contrast of a target feature with its background and therefore benefit from accurately predicting the signature of the underlying sea surface background. We here present a model that synthesizes infrared spectral images of sea surfaces. This model traces explicitly the behaviour of the sea wave structure and light propagation. To self-consistently treat spatial and temporal correlations of the clutter, geometrical realizations of sea surfaces are built based on realistic sea wave spectra and their temporal behaviour is subsequently followed. A camera model and a ray tracer are used to determine which parts of the sea surface are observable by individual camera pixels. Atmospheric input elements of the model, being sky dome, path radiance and transmission, are computed with MODTRAN for a chosen atmosphere.

Using seasonally averaged meteorological and spectrally resolved aerosol profiles extracted from a maritime environment, this paper investigated how the resolution of the vertical profiles influences the 3-5μm and 8-12μm average transmittance and integrated path radiance computations conducted by MODTRAN in high-elevation scenarios. First, the minimum altitude to which the atmosphere should be defined in order to accurately determine the transmittance and path radiance along vertical and slant paths was investigated by recursively removing vertical layers until the relative changes in the transmittance and path radiance became smaller than those due to instrument uncertainty. Once this minimum height was found, the vertical resolution in the atmosphere below the minimum altitude was systematically varied. The suitability of several gradient-based criteria has been investigated to determine the optimal discretization of the vertical profiles. The results indicate that, depending on the quantity to be calculated, vertical discretizations based on the gradient in either the pressure, temperature or humidity serve as optimal discretizations in maritime high-elevation scenarios. Moreover, the followed methodology demonstrates how to adaptively implement a vertical resolution in a generic atmosphere, which generates crucial knowledge in supporting signature and sensor performance modelling for high-elevation scenarios.

Most models that predict the infrared signature of an object are based on steady-state equilibrium conditions and do not model the dynamic nature of the real world. To gain more understanding of the dynamic infrared signatures of an object, several outdoor experiments were performed, using a CUBI and a small vessel as an object. Dynamic changes were (intentionally) made to the object, while the temperatures of the facets, the meteorological parameters, and the infrared signature were being monitored. The influence of environmental parameters on the dynamic infrared signature of an object is discussed in this paper. A first attempt to model the decrease in object temperature is made. © 2016 SPIE. The Society of Photo-Optical Instrumentation Engineers (SPIE)

A CUBI, a simple geometric metal test-object, is placed in an outdoor environment to monitor the infrared signature. The (daily) temperature evolution of the individual facets is monitored as function of environmental parameters, such as solar irradiance and ambient temperature. This provides insight in the parameters that have the strongest effects on the thermal signature of the CUBI. The CUBI is also imaged by infrared cameras and these recordings are used to estimate the temperature of the CUBI. The recorded images are also used to provide insight in the amount of air turbulence generated by the radiance of the hot CUBI facets. The amount of turbulence is compared with the ambient turbulence as calculated by standard bulk theories.

For maritime situational awareness, it is important to identify currently observed ships as earlier encounters. For example, past location and behavior analysis are useful to determine whether a ship is of interest in case of piracy and smuggling. It is beneficial to verify this with cameras at a distance, to avoid the costs of bringing an own asset closer to the ship. The focus of this paper is on ship recognition from electro-optical imagery. The main contribution is an analysis of the effect of using the combination of descriptor localization and compact representations. An evaluation is performed to assess the usefulness in persistent tracking, especially for larger intervals (i.e. re-identification of ships). From the evaluation on recordings of imagery, it is estimated how well the system discriminates between different ships.

Long-term tracking is important for maritime situational awareness to identify currently observed ships as earlier encounters. In cases of, for example, piracy and smuggling, past location and behavior analysis are useful to determine whether a ship is of interest. Furthermore, it is beneficial to make this assessment with sensors (such as cameras) at a distance, to avoid costs of bringing an own asset closer to the ship for verification. The emphasis of the research presented in this paper, is on the use of several feature extraction and matching methods for recognizing ships from electro-optical imagery within different categories of vessels. We compared central moments, SIFT with localization and SIFT with Fisher Vectors. From the evaluation on imagery of ships, an indication of discriminative power is obtained between and within different categories of ships. This is used to assess the usefulness in persistent tracking, from short intervals (track improvement) to larger intervals (re-identifying ships). The result of this assessment on real data is used in a simulation environment to determine how track continuity is improved. The simulations showed that even limited recognition will improve tracking, connecting both tracks at short intervals as well as over several days. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.