Abstract
SONAR target classification with complex-valued neural networks
The Journal of the Acoustical Society of America, Vol.155(3_Supplement), pp.A47-A47
03/01/2024
DOI: 10.1121/10.0026751
Abstract
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.]
Details
- Title: Subtitle
- SONAR target classification with complex-valued neural networks
- Creators
- Timothy Linhardt - University of IowaAnanya Sen Gupta - University of IowaMatthew BaysIvars P. Kirsteins - Naval Undersea Warfare Center
- Resource Type
- Abstract
- Publication Details
- The Journal of the Acoustical Society of America, Vol.155(3_Supplement), pp.A47-A47
- DOI
- 10.1121/10.0026751
- ISSN
- 0001-4966
- eISSN
- 1520-8524
- Language
- English
- Date published
- 03/01/2024
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984656630002771
Metrics
2 Record Views