Dynamical classification of hydroacoustic data from a Baltic Sea experiment

The authors analyze the acoustic signatures of a small boat using nonlinear dynamical signal models. Specifically, they discuss the estimation of the parameters of a nonlinear delay differential equation from this data. Using the model coefficients as detection features they implement a Mahalanobis distance-based decision criteria to perform rigorous hypothesis testing. By analyzing acoustic data recorded in shallow water in the Baltic Sea, they compare the performance of the dynamical detector with a frequency band-matched energy detector and show that the former provides increased detection performance. In addition, they demonstrate that the delay differential equation model can distinguish details of the boat´s signature, such as the engine RPM, hence it could be used for classification purposes as well