Efficient Active Azimuth-Elevation DOA Estimations via Massive MIMO Radar

Massive multiple-input multiple-output (MIMO) systems have gained increasing importance in applications such as target detection, tracking, and wireless communications, where accurate and efficient direction-of-arrival (DOA) estimation is crucial. However, the high computational complexity involved in estimating both azimuth and elevation angles using massive antenna arrays poses a significant challenge for real-time implementation. In this paper, two novel approaches are proposed for efficient active azimuth-elevation DOA estimations in massive MIMO radar systems: the onedimensional (1D) spatial searches approach (1D-SSA) and the spatial searchless approach (SSLA). These approaches address the high computational complexity typically associated with multi-dimensional spectral searches and solving optimization problems in massive MIMO systems. The 1D-SSA reduces the computational burden by creating a special arrangement of the collected data and breaking the spectral search into multiple 1D searches, while the SSLA eliminates the need for spectral searches entirely by leveraging structured subarrays and eigendecomposition techniques. Both approaches are evaluated in terms of accuracy, resolution and computational efficiency. Also, the performance of different methods is compared with the Cramér–Rao bounds (CRBs) derived in this paper. The proposed approaches are shown to be well-suited for near real-time massive MIMO radar systems with high accuracy and reduced computational overhead.