A Multiclass Support Vector Machine Based Direction-of-Arrival Estimation Technique using Spherical Antenna Array with Undefined Mutual Coupling

In antenna array signal processing, estimating the direction-of-arrival (DoA) remains a challenge and basic problem. In this paper, a DoA estimation technique using support vector machine (SVM) classification is developed using spherical antenna array (SAA). The source signal impinging on SAA is decomposed using spherical harmonics (SH). Both magnitude and phase features are computed from the decomposed SH signals. The magnitude and phase features are classified into DoA classes using multi-class SVM (MC-SVM) algorithm. Due to the deterministic and non-probabilistic nature of SVM algorithm, it exhibits high computational speed and less complex than the neural network-dependent learning algorithms. Numerical experiments and experimental measured data (generally accepted ground to test any method) are used to evaluate the performance of the proposed technique. The developed algorithm exhibit high level of robustness at different signal-to-noise ratios (SNR) in the estimation of DoA. Root mean square error (RMSE) performance metrics is employed in the analysis of the proposed method against the state-of-the-art. The results obtained are motivating enough for the deployment of the proposed algorithm in practical scenarios.