An Adaptive Grouping Method for Efficient Multi-Beam Testing of Phased Arrays

Phased arrays are key for next-generation mobile communication, satellite communication, and radar systems. Radiation patterns are essential performance metrics for evaluating antennas, but conventional mechanically scanned measurements become inefficient when repeated for every beam of the phased array. Existing active-element-pattern-based methods reduce this burden by grouping elements with similar coupling environments, yet their grouping and representative-element selection are mainly based on physical intuition, and on-off-mode-based measurements may differ from the actual all-on operating state. This paper proposes a data-driven adaptive grouping method based on K-means unsupervised learning. Element positions and broadside phase information construct the feature matrix, and a composite evaluation function considering group size and electric-field variation subdivides high-dynamic regions. Representative element patterns are acquired in the all-on mode, with sparse angular sampling to further reduce measurement time. Simulation results of an 8×8 dipole array show that the proposed method reconstructs multi-beam array patterns using 16 representative elements, fewer than the 25 representative elements required by a 5×5 grouping strategy, with the same accuracy in the main lobe and improved accuracy in sidelobe nulls. The effectiveness of the proposed grouping method has also been demonstrated for the measured 4×4 mmWave phased array.