Abstract
Spherical antenna array (SAA) has become highly attractive where hemispherical scan coverage is required as it can provide uniform directivity in all the scan directions. Various direction-of-arrival (DoA) estimation methods suffer from different problems, such as low accuracies in mismatched conditions, high computational complexity and poor estimation in a harsh environment. Another critical concern is mutual coupling (MC) characteristics between the array elements. These problems affect the quality of the navigation signal in harsh environments. This paper presents a robust DoA estimation and mutual coupling compensation technique based on convolutional neural network (CNN) for Spherical Array. Spherical harmonic decomposition (SHD) is used to facilitate feature extraction in two sets, which contains different features about the elevation and azimuth of the source for DoA estimation. The features serve as input to the learning technique for separate estimation of elevation and azimuth, which consequently reduce computational complexity as against the joint estimation of DoA. Learning methods for DoA estimation with few frames and dense search grids within the spherical array configuration are presented. To solve the MC error, the DoA estimation scheme is also used to obtain accurate spectrum peak in the multipath scenario with unknown MC and sharper spectrum peak via the unique structure of the MC matrix and spatial smoothing algorithms. In all, experimental results, which is the ground truth to test any procedure, show the effectiveness, validity, and potential practical application of the proposed technique.
Highlights
Learning methods have been successfully used in DoA estimation, where the feature is extracted from measurements and passed to a neural network that has been trained for DoA feature mapping [12]–[14]
The decomposition of signal pressure at the elements into separate functions of elevation and azimuth of the source can be facilitated via spherical harmonic decomposition (SHD) [30]
The Spherical antenna array (SAA) is stationed at the center of the chamber, and the source is located at 74 DoAs that are obtained from various combinations of 4 different elevations and 18 different azimuths
Summary
Deep learning approaches have been used to estimate DoA and solve source localization problem with microphone arrays [15]–[19]. Elbir [24] developed a data transformation technique for the estimation of DoA with 3-D antenna arrays with mutual coupling effect. There has been much focus on DoA estimation using linear, circular, and rectangular antenna arrays, with little or no effort so far on SAA. Disjointed estimation of DoA via separate finding of azimuth and elevation angles reduces computational complexity, as reported in [27]–[29]. This has not been examined in the spherical domain.
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