Adaptive Subarray Partitioning for Large-Scale Phased Arrays Using ISODATA

Abstract

Phased array antennas, known for their high directivity and low sidelobe levels, are widely used in radar and communication systems. As the array size increases, controlling each element individually becomes increasingly complex and costly. To address this challenge, subarray partitioning is an effective solution. Traditional clustering algorithms, like K-means, require a predefined number of subarrays, limiting flexibility, especially in scenarios with complex geometries or irregular distributions. In contrast, the ISODATA algorithm dynamically adjusts the number of subarrays based on element variance and spatial distribution. This allows ISODATA to adapt to irregular array geometries and complex distributions, leading to more effective sidelobe suppression and beamforming. Additionally, ISODATA incorporates splitting and merging operations, offering greater flexibility in subarray configuration compared to the static K-means. Numerical experiments demonstrate that ISODATA outperforms K-means in terms of sidelobe suppression, beamforming, and partitioning flexibility, making it highly suitable for large-scale phased array applications.