A Score Function for Adapting Large Array Partitions for Source Detection

Large aperture arrays improve detection performance with higher gain, especially in low signal-to-noise ratio (SNR) applications such as underwater acoustic (UWA) source detection. However, large arrays are susceptible to phase errors because of limited spatial coherence of signals or a mismatch between the assumed signal models and the true signal models. To mitigate this issue, the array can be partitioned into smaller segments of sensors known as subapertures. The subapertures are processed coherently, and then the power outputs of the subapertures are combined. This processing is the spatial analog of the classic Welch power spectrum estimator which averages periodograms across time windows of a recording. Identifying the subaperture size which optimizes detection performance remains an open problem. We proposed a score function that indicates the detection performance of different array partitions without access to the ground truth. Numerical experiments using the SWellEx-96 data corrupted by additional noise show that the subaperture maximizing this detection score function achieves a better receiver operating characteristic (ROC) in low SNR cases compared to any fixed array partition candidate. [Work supported by ONR Code 321US.]