SONAR target classification with complex-valued neural networks

Popular acoustic signal processing techniques analyze acoustic color, which is a magnitude representation of a frequency spectrum. This paradigm allows for easy visualization of results and simpler models at the cost of throwing out phase information. Analysis and modeling of complex-valued data does have inherent difficulty from the nature of complex numbers. Optimization on the complex field requires alternate partial derivative definitions to circumvent consequences of the Cauchy–Riemann equations regarding holomorphic functions. To make use of phase information, we demonstrate classifier model optimization with complex-valued parameters on data with both magnitude and phase components and show how complex neural networks yield marked improvements over similarly shaped real-valued networks in both classification accuracy and generalization ability. We apply these techniques to small target SONAR with simulated Lamb wave resonance signals for hollow spheres, differentiating between different material classes. [Research funded by the DoD Navy (NEEC) Grant No. N001742010016.]