Modelling Aspects of Planar Multi-Mode Antennas for Direction-of-Arrival Estimation

Multi-mode antennas are an alternative to classical antenna arrays, and hence a promising emerging sensor technology for a vast variety of applications in the areas of array signal processing and digital communications. An unsolved problem is to describe the radiation pattern of multi-mode antennas in closed analytic form based on calibration measurements or on electromagnetic field (EMF) simulation data. As a solution, we investigate two modeling methods: One is based on the array interpolation technique (AIT), the other one on wavefield modeling (WM). Both methods are able to accurately interpolate quantized EMF data of a given multi-mode antenna, in our case a planar four-port antenna developed for the 6-8.5 GHz range. Since the modeling methods inherently depend on parameter sets, we investigate the influence of the parameter choice on the accuracy of both models. Furthermore, we evaluate the impact of modeling errors for coherent maximum-likelihood direction-of-arrival (DoA) estimation given different model parameters. Numerical results are presented for a single polarization component. Simulations reveal that the estimation bias introduced by model errors is subject to the chosen model parameters. Finally, we provide optimized sets of AIT and WM parameters for the multi-mode antenna under investigation. With these parameter sets, EMF data samples can be reproduced in interpolated form with high angular resolution

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

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 array