Paramarine with CADRCS for Radar Cross Section

GRC with CSS of Denmark provide a seamless Paramarine connectivity in the field of RCS. The method of RCS analysis is based on CAD geometry and developed by CSS.

This integrated modelling and RCS analysis capability gives a baseline capability for assessing the RCS of ships. CADRCS is a special encodement of the Physical Optics approach including shadowed areas through a combination of graphical and numerical results a ship can be rapidly analysed from a CAD definition to identify the RCS hotspots. Solutions to eradicate hotspots can be determined either using a simulated RAM coverage or reshaping the structure.

It is the HRR plot that allows CADRCS to identify the corner reflectors shown on the high-resolution range profile as curved lines in red.

When a corner reflector has been picked-out from the HRR plot then the user can examine the source on the ship geometry by bringing up the individual RCS bitmap picture for a given viewing angle.

Here we see the use of the high-resolution range profile to spot the corner reflectors and the corresponding bit map view to identify the source on the ship geometry.

Paramarine generates the facet body data used by CADRCS. In the model show below the geometric facet data was imported from a NATO format file.

Paramarine can create CAD elements (and may be used to import other CAD geometry as STL, DXF, STEP, IGES or NATO format) such as sheet bodies from points and place these sheets as cover for a corner reflector identified and set their coefficient of reflection to a low value representative of a RAM characteristic. Once the model has been modified, the analysis is re-run and the results checked to see that the solution has been successful (as illustrated below).

Confidence in the RCS prediction by CADRCS has been gained through comparisons with ship measurements and the wealth of fifteen years experience at the Danish Research Establishment (DDRE) of the author Gert Lindqvist (CSS). As can be seen below CADRCS has been found to be a good predictor. Despite the high quality of the prediction, the package is remarkably affordable.