Comparison between measured and calculated bistatic RCS data on a rough metallic background surface

Bistatic radar measurements have been performed on a manufactured rough aluminum surface. The extracted normalized cross sections from the measurements are used to validate two different calculation methods, i.e. the iterative physical optics (IPO) and the integral equation method (IEM). The IPO method is based on solving the magnetic field integral equation (MFIE) in an iterative procedure and requires a faceted representation of the object. Each iteration step can be considered as an internal interaction contribution to the cross section. The IPO-method is suitable for using on objects that are too large for "exact" methods, e.g. the method of moment (MoM) and the finite difference in time domain (FDTD), and where high frequency methods, e.g. physical optics (PO), do not provide results good enough. The IEM method is based on solving the Stratton-Chu integral equation by describing the roughness of a surface by two statistical measures, i.e. the correlation length and the height deviation. The output result is given by an average value of the diffuse part of the normalized cross section. The huge advantage with IEM compared to other calculation methods is that IEM is of the order of several magnitudes faster, in terms of CPU-time. The results from the measurements and IPO are generally in good agreement over all geometries and polarization combinations. The IEM results exhibit very good agreement with IPO and measurement results for some geometries while for other geometries the IEM results will tend to underestimate the normalised cross sections. The conclusion is that the IEM method has potential to be used to model backgrounds and target-background interaction contributions. Further investigations have to be made concerning variance measures of the diffuse part of the normalized cross section, how to include material properties into IEM and how to combine other methods, e.g. PO, with IEM to be able to make calculations on large scenarios.