Abstract
Large aperture arrays offer improved detection performance through increased gain, particularly in low signal-to-noise ratio (SNR) environments such as underwater acoustic (UWA) source detection. However, their performance can degrade due to phase errors arising from spatial coherence loss or mismatches between assumed and true signal models. A common mitigation strategy involves partitioning the array into smaller, local segments—subapertures—that are processed coherently, with their outputs subsequently combined. This approach parallels Welch’s method for spectral estimation, treating space analogously to time. Yet, identifying optimal subaperture structures remains challenging due to the dynamic and uncertain nature of underwater environments. Previous work introduced a universal partitioning framework that adaptively selects subarrays to optimize downstream tasks like detection and beamforming, using performance-driven criteria. In this work, we extend that framework by incorporating performance-weighted blending across candidate subarray partitions using structured models such as linear transition diagrams and context trees. These models allow the system to dynamically adjust to both spatial and temporal coherence variations without requiring environmental priors. Our method is modular and agnostic to downstream processing, making it applicable to beamforming, direction-of-arrival estimation, and spatial filtering. Using simulation-based experiments, we evaluate and compare a range of partitioning and blending schemes.