KEYWORDS: Infrared signatures, Data modeling, Sensors, Infrared radiation, Data archive systems, Thermography, Infrared imaging, Data storage, Data conversion, Cameras
Aircraft self-protection against heat seeking missile threats is an extremely important topic worldwide, recently even more so with the instability in the Middle East region due to, for example, the large number of man-portable air defense systems (MANPADS) that were stolen from army arsenals. A fundamental step in successfully achieving self-protection is the ability to capture and identify aircraft infrared signatures. This work discusses some of our efforts and results in creating an asset database for infrared signatures. The database was designed in a way that will feed an image processing engine to allow for automated feature and signature extraction. A common failing in the handling of target signature raw data is the fact that raw data files can become unreadable because of changes in technology, software applications or weak media archiving technology (e.g. corrupt DVD media). A second shortcoming is often the fact that large volumes of raw or processed data are stored in an unstructured manner, resulting in poor recall later. A third requirement is the portability of data between various processing software packages, legacy, current and future. This paper demonstrates how the challenge of future-proofing measured data is met with reference to the archiving and analysis of data from a recent measurement campaign. Recommendations for future work are given, based on the experience gained.
Present-day naval operations take place in coastal environments as well as narrow straits all over the world. Coastal
environments around the world are exhibiting a number of threats to naval forces. In particular a large number of
asymmetric threats can be present in environments with cluttered backgrounds as well as rapidly varying atmospheric
conditions. During trials executed in False Bay a large amount of target, background and atmosphere data was gathered
that is of use in analysis of optical characteristics of targets and backgrounds. During the trials a variety of backgrounds
were recorded. We have used these backgrounds to validate the TNO background model MIBS to incorporate also
coastal backgrounds and sunlit sea backgrounds. In the paper we show results of the background analysis, for coastal
bay backgrounds. In particular the detection of small targets by automatic system may be hampered by small surface
structure variations at the surface and near the horizon. The data that we analyzed are sea surface structure, temporal
behaviour, and spectral differences during different environmental conditions that occurred during the trials. This data is
essential to feed detection algorithms, and performance models for the assessment of sensor performance in coastal
environment.
Present-day naval operations take place in coastal environments as well as narrow straits all over the world. Coastal
environments around the world are exhibiting a number of threats to naval forces. In particular a large number of
asymmetric threats can be present in environments with cluttered backgrounds as well as rapidly varying atmospheric
conditions. During trials executed in False Bay a large amount of target, background and atmosphere data was gathered
that is of use in analysis of optical characteristics of targets and backgrounds. During the trials a variety of backgrounds
were recorded. We have used these backgrounds to validate the TNO background model MIBS to incorporate also
coastal backgrounds and sunlit sea backgrounds. In the paper we show results of the background analysis, for coastal
bay backgrounds. In particular the detection of small targets by automatic system may be hampered by small surface
structure variations at the surface and near the horizon. The data that we analyzed are sea surface structure, temporal
behaviour, and spectral differences during different environmental conditions that occurred during the trials. This data is
essential to feed detection algorithms, and performance models for the assessment of sensor performance in coastal
environment. Some sensor management approaches for application in IRST systems is discussed.
Present-day naval operations take place in coastal environments as well as narrow straits all over the world. Coastal
environments around the world are exhibiting a number of threats to naval forces. In particular a large number of
asymmetric threats can be present in environments with cluttered backgrounds as well as rapidly varying atmospheric
conditions. In these conditions the threat contrast may be low and varying, and the amount of background clutter can be
severe. These conditions require the electro-optical means of detection and classification to be optimized in order to
have more time to act against threats. In particular the assessment of classification means is an important issue. Beside
short-range coastal paths, long-range horizontal paths with variable atmospheric conditions are of interest. The small
differences between types of vessel can help us determine the classification of the vessel type. Different payloads and
people on-board can be clues to the classification of the vessel. Operations in warmer environments, limiting the
atmospheric transmission due to water vapour absorption, are challenging. Understanding of the impact of the different
environments on the optical characteristics of threats is of great importance. For this purpose a trial was planned to
assess the optical characteristics of different types of small surface vessels in a coastal environment. During this trial a
number of small targets were used during different parts of the day and night. Furthermore positional as well as
atmospheric characterisation was performed as ground truth information. From this data a first analysis was performed
showing strong intensity fluctuation in target as well as background signal levels. At longer ranges and in coastal
environments these target signals may well be hidden within the background clutter. This data is essential to feed
models for the assessment of sensor performance in coastal environment.
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