A Beamspace Dimension Reduction Technique With Application to DOA estimation in Low-angle Tracking

We develop a dimension reduction algorithm that against coherent signals with application to direction of arrival (DOA) estimation in low-angle tracking. The dimension reduction is done as a linear transformation which is commonly referred to as beamspace (BS). In this paper, the BS covariance matrix is reconstituted from several beams which can be applied to synthetic vector maximum likelihood (SVML) estimator. The proposed method is computationally less expensive to obtain similar DOA estimation performance as compared to the existing element space (ES) technique. Simulation results and field data sets validate the robustness of the developed algorithm, and they show its effectiveness and superiority to the ES SVML algorithm.