Abstract

To obtain an antenna array with isotropic radiation, spherical antenna array (SAA) is the right array configuration. The challenges of locating signals transmitted within the proximity of antenna array have been investigated considerably in the literature. However, near-field (NF) source localization of signals has hitherto not been investigated effectively using SAA in the presence of mutual coupling (MC). MC is another critical problem in antenna arrays. This paper presents an NF range and direction-of-arrival (DoA) estimation technique via the direction-independent and signal invariant spherical harmonics (SH) characteristics in the presence of mutual coupling. The energy of electromagnetic (EM) signal on the surface of SAA is captured successfully using a proposed pressure interpolation approach. The DoA estimation within the NF region is then calculated via the distribution of pressure. The direction-independent and signal invariant characteristics, which are SH features, are obtained using the DoA estimates in the NF region. We equally proposed a learning scheme that uses the source activity detection and convolutional neural network (CNN) to estimate the range of the NF source via the direction-independent and signal invariant features. Considering the MC problem and using the DoA estimates, an accurate spectrum peak in the multipath situation in conjunction with MC and a sharper spectrum peak from a unique MC structure and smoothing algorithms are obtained. For ground truth performance evaluation of the SH features within the context of NF localization, a numerical experiment is conducted and measured data were used for analysis to incorporate the MC and consequently computed the root mean square error (RMSE) of the source range and NF DoA estimate. The results obtained from numerical experiments and measured data indicate the validity and effectiveness of the proposed approach. In addition, these results are motivating enough for the deployment of the proposed method in practical applications.

Highlights

  • Antenna arrays, in which the distribution of radiating elements is over a spherical surface, remain the answer to the isotropic requirements [1]

  • We proposed a learning scheme that uses the source activity detection and convolutional neural network (CNN) to estimate the range of the source of the NF via the direction-independent and signal invariant features

  • We evaluated the performance of source localization by calculating the root mean square error (RMSE) of range and DoA estimation experiment using the acquired data from Spherical antenna arrays (SAA)

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Summary

Introduction

In which the distribution of radiating elements is over a spherical surface, remain the answer to the isotropic requirements [1]. Spherical antenna arrays (SAA) have the capability of receiving electromagnetic (EM) wave with equal strength regardless of the polarization and the direction-of-arrival (DoA). To take advantage of the highest degree of freedom, the antenna array under consideration must have the ability to determine the DoA and polarization of an incoming EM wave, impinging from all directions on the unit sphere. This paper focuses on sources that are in the near-field (NF) of SAA; in this case, it will be possible to estimate DoA and range of the source. This has not been considered in the previous works.

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