Low Complexity Single Source 2-D DOA Estimation Based on Reduced Dimension SVR

Conventional direction of arrival (DOA) estimation algorithms suffer from performance degradation due to antenna pattern distortion and substantial computational complexity in real-time execution. The support vector regression (SVR) approach is a potential technique to handle those issues. Unlike the existing SVR works, which primarily operate on the elevation plane, we propose a sequential estimation method for both the azimuthal and elevation angles (2-D). Our method combines the reduced dimension SVR (for the azimuthal plane) with a closedform approach (for the elevation plane). Thus, the training and testing are only required for the azimuthal angles (1-D) which makes it very attractive from the implementation complexity point of view. Our numerical analysis demonstrates that the proposed algorithm exhibits root mean square error performance comparable to the popular MUSIC algorithm with a significantly less computational burden.