The EOSTAR model aims at assessing the performance of electro-optical (EO) sensors deployed in a maritime surface scenario, by providing operational performance measures (such as detection ranges) and synthetic images. The target library of EOSTAR includes larger surface vessels, for which the exhaust plume may constitute a significant signature element in the thermal wavelength bands. The main steps of the methodology to include thermal signatures of exhaust plumes in EOSTAR are discussed, and illustrative examples demonstrate the impact of the ship’s superstructure, the plume exit conditions, and the environment on the plume behavior and signature.
Infrared guided missiles are a threat for modern naval forces. The vulnerability of ships can be reduced by applying
countermeasures such as infrared decoys and infrared signature reduction.
This paper presents recent improvements in a simulation toolset which can be used for assessing the effectiveness of
these measures. The toolset consists of a chain of models, which calculate the infrared signature of a ship (EOSM) and
decoys, and generate infrared image sequences of the ship in a realistic sea and sky background (EOSTAR). A complete
missile fly-out model (EWM) uses these images in closed loop simulations for the evaluation of countermeasure
effectiveness against simulated seekers. All model components will be discussed. Typical simulation results will be shown.
Modern infrared imaging seekers can nowadays deal with higher resolution and less noisy sensor images. The testing of
new image processing or tracking algorithms requires a fitted set of relevant sensor images. When no actual recordings
are available, when experimental benches are not adapted or at an early stage of development, one can require a
simulation tool to generate synthetic infrared sensor images.
This paper presents the first version of ISISserver (Infrared Sensor Image Simulation Server) a software library
developed at TNO and used for infrared (IR) imaging seeker applications. Based on the EOSTAR Pro (Electro-Optical
Signal Transmission And Ranging) model suite, the set of functions offered by this toolkit allows analysis of synthetic
sensor images generated for various synthetic environments and targets. Typical targets from a database can be used as
well as externally user designed 3D targets. Simulation results using ISISserver toolkit with test data (not realistic or
physical data) will be shown.
Efficient military operations require insight in the capabilities of the available sensor package to reliably assess the
operational theatre, as well as insight in the adversary's capabilities to do the same. This paper presents the EOSTAR
model suite, an end-to-end approach to assess the performance of electro-optical sensor systems in an operational
setting. EOSTAR provides the user with coverage diagrams ("where can I see the threat?") and synthetic sensor images
("how do I perceive the threat?"), and allows assessing similar parameters for threat sensors. The paper discusses the
elements of EOSTAR and outlines a few of the possible applications of the model.
The environment is nowadays one of the most limiting factors for reliable detection, clear imagery and thus a successful
classification of potential threats by electro-optical (EO) sensors. However, the characterization of the environment and
the assessment of its impact on sensor performance remains a difficult issue. Measurements of meteorological
parameters are not always easy and cannot always be reliable. It becomes more and more interesting to extract the
information the environment by new methods. In this paper, the initial steps and the methodology of an inverse scheme
that retrieve valuable information about the EO propagation conditions from infrared (IR) camera images is proposed.
The use of the method under subrefractive conditions shows that features of the medium can be derived through a
thorough analysis of sensor images. By an original use of EO propagation modeling, it is possible to partially reconstruct
sensor images that were deformed by a refractive atmosphere.
For naval operations in a coastal environment, detection of boats is not sufficient. When doing surveillance near a
supposedly friendly coast, or self protection in a harbor, it is important to find the one object that means harm, among
many others that do not. For this, it is necessary to obtain information on the many observed targets, which in this
scenario are typically small vessels. Determining the exact type of ship is not enough to declare it a threat. However, in
the whole process from (multi-sensor) detection to the decision to act, classification of a ship into a more general class is
already of great help, when this information is combined with other data to assist an operator.
We investigated several aspects of the use of electro-optical systems. As for classification, this paper concentrates on
discriminating classes of small vessels with different electro-optical systems (visual and infrared) as part of the larger
process involving an operator. It addresses both selection of features (based on shape and texture) and ways of using
these in a system to assess threats. Results are presented on data recorded in coastal and harbor environments for several
Operating in a coastal environment, with a multitude of boats of different sizes, detection of small extended targets is
only one problem. A further difficulty is in discriminating detections of possible threats from alarms due to sea and
coastal clutter, and from boats that are neutral for a specific operational task. Adding target features to detections allows
filtering out clutter before tracking. Features can also be used to add labels resulting from a classification step. Both will
help tracking by facilitating association. Labeling and information from features can be an aid to an operator, or can
reduce the number of false alarms for more automatic systems.
In this paper we present work on clutter reduction and classification of small extended targets from infrared and visual
light imagery. Several methods for discriminating between classes of objects were examined, with an emphasis on less
complex techniques, such as rules and decision trees. Similar techniques can be used to discriminate between targets and
clutter, and between different classes of boats. Different features are examined that possibly allow discrimination
between several classes. Data recordings are used, in infrared and visual light, with a range of targets including rhibs,
cabin boats and jet-skis.
Within the marine atmospheric surface layer it is possible for a single camera to deduce passively the range to a point
target. Although this range determination would appear impossible at first glance, such a measurement exploits the
common occurrence of sub-refractive propagation conditions in the marine environment.
A calculation of the range to an object utilizes a geometric optics determination of slight angular differences between
two different ray trajectories to the object. This is most commonly done with the assumption of Euclidean or 'free-space'
conditions. In this paper we utilize the phenomenon of inferior mirages to provide two different ray-paths to an imaging
sensor. The primary assumption is that the environment containing the path from camera (or eye) to target is
homogeneous (but not isotropic).
The application of long-range infrared observation systems is challenging, especially with the currently available high spatial resolution infrared camera systems with resolutions comparable with their visual counterparts. As a result of these developments, the obtained infrared images are no longer limited by the quality of system but by atmospheric effects instead. For instance, atmospheric transmission losses and path radiance reduce the contrast of objects in the background and optical turbulence limits the spatial resolution in the images. Furthermore, severe image distortion can occur due to atmospheric refraction, which limits the detection and identification of objects at larger range. EOSTAR is a computer program under development to estimate these atmospheric effects using standard meteorological parameters and the properties of the sensor. Tools are provided to design targets and to calculate their infrared signature as a function of range, aspect angle, and weather condition. Possible applications of EOSTAR include mission planning, sensor evaluation and selection, and education. The user interface of EOSTAR is fully mouse-controlled, and the code runs on a standard Windows-based PC. Many features of EOSTAR execute almost instantaneous, which results in a user friendly code. Its modular setup allows its configuration to specific user needs and provides a flexible output structure.