Robust Nonlinear Array Interpolation for Direction of Arrival Estimation of Highly Correlated Signals

Kurzfassung

Important signal processing techniques require that the response of the di�fferent elements of the array have specifi�c characteristics, which are often not achievable for real systems due, for instance, to the fact that the responses of the array elements are a�ffected and distorted by mutual coupling. In such cases, in order to allow the application of ESPRIT, FBA, and SPS, it is necessary to apply array interpolation. Array interpolation provides a model or transformation between the real and a desired array with the necessary characteristics. As the real response becomes more distorted with respect to the desired one and the region of the fi�eld of view to be considered increases a nonlinear approach becomes necessary. In this work, two di�fferent methods for sector discretization are presented. An Unscented Transform (UT) based method and a principal component (PC) based method are discussed in detail. Two nonlinear interpolation methods are also presented, Multivariate Adaptive Regression Splines (MARS) and Generalized Regression Neural Networks (GRNNs). They are extended and applied to the problem of array interpolation. The performance of the proposed methods is examined using simulated and measured array responses of a specially designed physical system for research on mutual coupling in antenna arrays