RCS and IR Signatures
Members
Activities
Throughout the domain of sensors, the knowledge of the object signature (real or simulated) is primordial to understand the detection, reconnaissance and identification performances of those sensors. Although sensors in the radar domain (mostly active) and sensors in the optronics domain (mostly passive) rely on different working principles, the importance of the object signature within the chain of the sensor system is comparable.
In the radar domain, expertise is built up by performing experimental measurements on the radar cross section of small drones (to make them more visible for training purposes) and on the effectiveness of radar camouflage materials.
In the optronics domain, research is conducted in the thermal IR1 part of the spectrum within the frame of Navy applications.
- A first research axis focuses on the use of electro-optical (EO) sensors onboard micro-satellites for non-cooperative ship detection when the Automated Identification System (AIS2) is not operational. The detection of ships is foreseen by applying line detection algorithms on the ship wakes in the thermal IR image taken by the sensor onboard the satellite. Optimum design of such sensors implies to be capable of simulating the evolution of sensor performance as a function of sensor or scene parameters before manufacturing the sensor. The used approach is based on computing Receiver Operating Characteristic (ROC) curves from a model of the probability density function (pdf) of pixels contained in the image. The proposed statistical methodology can be applied to other EO sensors, if appropriate models for the pdfs are used.
- A second research axis can be identified as ship based automatic detection of small floating objects on an agitated sea. The research on this topic focuses upon the use of IR video cameras mounted on a ship, due to the day and night capability and the relative ubiquity of this sensor on Navy vessels. The problem arises when facing an agitated sea. In this non-trivial case, the wave fronts on the surface will present a non-grazing angle to the camera, allowing it to see the emissions at water temperature. This leads to a complex scene, where against a background of sky temperature emissions, a large number of sea temperature objects (waves and mines) of similar scale and shape are appearing and disappearing. Our solution for this is to look at object behavior across a video sequence, rather than looking at object features in a static frame. Amongst existing video analysis algorithms, the best performance was obtained from a two-stage approach called behaviour subtraction, in which a classical background subtraction algorithm is used to detect activity, after which this activity is summed over a time window and compared with an activity model learned in a training phase.
- A third axis of research handles ship signature management. As signatures, we mean submarine and surface signatures. The knowledge and the management of surface signatures is unavoidable to be able to evaluate the ships fragility. This allows evaluating the stealth capacities of these platforms. They are thus important for ship security concerning the ships protection against threats. The acquired knowledge and the expertise thus serve for the redaction of specifications to acquire new ships. This project aims at characterizing the infrared signatures of ships and finding ways to reduce both of them. A dedicated research is done to define generic models of these signatures. These models are then validated using measurements made on 3D benchmark objects. The model predicting the infrared signature allows treating complex ship geometries and takes complex phenomena's into account such as solar and sky irradiances, heat convection and conduction and the influence of the sea signature on ship signature. The second part of the project is devoted to the conception of signature reduction techniques for the infrared signatures in order to improve the stealth of the ships. Classical methods to reduce the infrared signature are the use of low emissivity paintings, and the use of water injection in the engine exhaust gases. The most advanced method proceeds by watering of the ships'deck with water.
Research projects
Satellite Automatic Identification System Phase B1 - E-SAIL
A consortium lead by LuxSpace (LU) has introduced a proposal "SAT-AIS Phase B1 - E-SAIL" in answer to the ESA call ITT AO/1-6656/10/NL/AD which was accepted in September 2011. OIP Sensor systems (BE) will perform the activities related to the electro-optical (EO) payload as subcontractor of LuxSpace and signed the contract in december 2011. RMA has been asked as a subcontractor by OIP. The topic of the global proposal is a feasibility study of a spaceborne AIS (Automatic Identification System) extended with non-cooperative ship detection capability. The contribution of OIP/RMA is to examine whether an EO imager can be used as a complementary system for non-cooperative vessel detection. RMA will simulate the detection performance of the processing algorithms to be applied on the EO images. The Patrimony of RMA will receive 42000 euro for a contribution of 8 manmonths, which will be performed by Maj v/h Vlw Marijke VANDEWAL and Lt Mathias BECQUAERT.
Expertise-activities OMRA
The laboratory OMRA of the CISS department is asked regularly to perform radar and IR signature measurements amongst others in the domain of camouflage materials. To support these activities the activity OMRA-EXP has been created. A first forecast of duration has been set to 5 years, then the relevance of this activity will be reevaluated.
MRN13
This project addresses the problem of ship force protection agains asymmetrical threats such as swimmers, drifting mines/IEDs and small craft, this in a port or coastal context. For this it studies the detection of behavioural anomalies in infrared and optical video streams of the ship’s environment.
EDA DMD
DMD (Drifting Mines Detection) studies the new mothership/USV minehunting paradigm and its capabilities for detecting drifting mine threats at sea using time-integrated radar and behavioural analysis of infrared video data. RMA contribution is the capture and analysis of radar and video data from the sensor systems of the M-class frigate taking up the role of mothership simulator in this, and also the development and evaluation of drifting mine detection algorithms.
FREMM - MPIR : Frégate Européenne Multi Missions - Réalisation d\'un Module de Prédiction Infrarouge
The aim of the FREMM project is to concieve, realise, validate for the PSAD (Performances Senseurs et Aides à la Décision) module, a software that covers the following functionalities. First, the project computation of the effects of the atmospheric and geographical environment on the infrared transmission. Second, the projected computation of the effects of the atmospheric and geogrphical environment on the target and background irradiance. Third, the computation of the output performance (detection, identification) of optronics sensors. Fouth, the computation of images of infrared scenes. The MPIR module is responsible for the synthesis of infrared images of the scene. RMA is reponsible for integrating the OSMOSIS Thermal Model developed at RMA in the MPIR module of the FREMM project. OSMOSIS is an open-source software developed in the context of the MRN 03 study and that compute the thermal model of ships
MRN06
This project studies the useability of different sensors to detect small objects at the surface of the ocean. Applications are the detection of floating mines and wreck pieces in order to avoid collision, search and rescue of drowning pers. Optical, IR and radar sensors will be considered in order to be able to operate day and night and regardless of the weather conditions.
Publications
- Marijke Vandewal, Rainer Speck, and Helmut Sub. Efficient SAR Raw Data Generation Including Low Squint Angles and Platform Instabilities. IEEE Geoscience and Remote Sensing Letters, 5(1):26-30, January 2008.
- Marijke Vandewal. Investigation of High Resolution SAR-Systems aboard UAV-Platforms using Simulated Raw Data. PhD thesis, Vrije Universiteit Brussel and Deutsches Zentrum für Luft- und Raumfahrt, 2006.