| Popis: |
Using data-driven methods for characterizing spectral and spatiotemporal structures in underwater acoustic environments complements the traditional use of physics-based acoustic propagation models that often focus on capturing average environmental characteristics. One-way clustering of acoustic data in time, space and frequency can reveal individual, that is per mode, structure, but fails to identify couplings among the structures in these modes. Co-clustering is a clustering approach that can identify groups of similar elements (co-clusters) across all modes in a tensor. Our proposed co-clustering approach acts on a tensor build with acoustic data captured by a hydrophone array. It identifies the co-clusters, and the spectral and spatiotemporal structure that defines them by coupling one-way clustering with a Tucker approximation model. One-way clustering reveals the tensor structure per mode, namely time, frequency, and space, while the Tucker model captures trilinear structures coupling each mode. A co-clustering algorithm based on the alternating directions method of multipliers and the Levenberg-Marquardt method is developed. Numerical test on real acoustic-array data are used to illustrate the performance of the proposed co-clustering algorithm. |
