Detection and imaging of targets in the presence of clutter based on angular correlation function

The detection of targets in clutter environments is a problem of practical importance in both military and civil uses. It is quite often that targets are embedded in clutter environment. Examples are: mines and piples buried under ground, tanks hidden in trees, submarines under water, boats on ocean surface, tumors inside bodies and so forth. Among all of the examples, the surrounding media (random media or random rough surfaces) can also scatter and radiate waves, causing clutter. The clutter can be comparable to or larger than the target signal, which can obscure the targets and make the detection of targets difficult.; In the past, radar cross section (RCS) had been studied for target detection. It is difficult to distinguish target scattering from clutter with the RCS measurements when the background clutter is strong. Recently, advanced techniques, such as wavelet transform, polarimetric measurements, image processing, and matched filtering method have been used. In this thesis, we study a method based on the calculation of angular correlation function (ACF) of two received wave fields corresponding two incident waves.; ACF of random media scattering was first studied. This includes the derivation of general expression, volume scattering, rough surface scattering, and averaging techniques. It has been shown that the memory effect is a result of the statistically translational invariance of random scattering. Random rough surface scattering exhibits a memory line, while volume scattering exhibits two memory dots corresponding to self-correlation and reciprocity path. For the purpose of target detection, the memory effect is suggested to be avoided to suppress the clutter.; Numerical studies of target detection consist of two steps: (1) calculate the scattered wave fields, and (2) process the simulated data. To obtain wave fields scattered by a target embedded in clutter, we formulated problems exactly by using surface and volume integral equations. The equations are then solved numerically with the use of the method of moment and fast methods, which give exact solution of Maxwell's equations. Coherent addition approximation has also been used to generate data for SAR and ACF processing. It has been found that ACF is more effective in suppressing the clutter due to the random media and rough surface scattering than RCS, and ACF imaging gives larger signal-clutter ratios and finer resolution than the conventional SAR imaging. That is because the memory effect of random media scattering has been avoided and the frequency dependence of target scattering is overcome in ACF imaging.