Reduced-order model for underwater target identification using proper orthogonal decomposition

Research on underwater acoustics has seen major development over the past decade due to its widespread applications in domains such as underwater communication/navigation (SONAR), seismic exploration and oceanography. In particular, acoustic signatures from partially or fully buried targets can be used in the identification of buried mines for mine counter measures (MCM). Although there exist several techniques to identify target properties based on SONAR images and acoustic signatures, these methods first employ a feature extraction method to represent the dominant characteristics of a data set, followed by the use of an appropriate classifier based on neural networks or the relevance vector machine. The aim of the present study is to demonstrate the applications of proper orthogonal decomposition (POD) technique in capturing dominant features of a set of scattered pressure signals, and subsequent use of the POD modes and coefficients in the identification of partially buried underwater target parameters such as its location, size and material density. Several numerical examples are presented to demonstrate the performance of the system identification method based on POD. Although the present study is based on 2D acoustic model, the method can be easily extended to 3D models and thereby enables cost-effective representations of large-scale data.