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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.

An Intelligent Deep Learning-Based Direction-of-Arrival Estimation Scheme Using Spherical Antenna Array With Unknown Mutual Coupling