TY - GEN
T1 - Underwater Acoustic Sensing for Target Detection with Fractional Scattering Network and Wavelet Neural Network
AU - Gao, Ang
AU - Leach, Mark
AU - Yu, Limin
AU - Sun, Jie
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Underwater target sensing is an essential topic for many civilian and military applications. Vision-based sensing techniques have limitations due to the high attenuation of electromagnetic waves in water. In this paper, acoustic sensing is applied for underwater target detection. A new neural network-based algorithm is developed for feature extraction and the classification of underwater target mobilities including the motion speed and moving directions. The fractional scattering neural network, FrScatNet is applied for feature extraction, followed by Wavelet Neural Network (WNN) for classification. To facilitate sufficient training data, a Geometric Ray Tracing Model is adopted for dataset generation. Different target sensing scenarios are simulated with varied target velocities and directions.
AB - Underwater target sensing is an essential topic for many civilian and military applications. Vision-based sensing techniques have limitations due to the high attenuation of electromagnetic waves in water. In this paper, acoustic sensing is applied for underwater target detection. A new neural network-based algorithm is developed for feature extraction and the classification of underwater target mobilities including the motion speed and moving directions. The fractional scattering neural network, FrScatNet is applied for feature extraction, followed by Wavelet Neural Network (WNN) for classification. To facilitate sufficient training data, a Geometric Ray Tracing Model is adopted for dataset generation. Different target sensing scenarios are simulated with varied target velocities and directions.
KW - fractional scattering network (FrScatNet)
KW - Geometric Ray Tracing Channel Model
KW - signal processing
KW - underwater acoustic sensing
KW - wavelet neural network (WNN)
UR - http://www.scopus.com/inward/record.url?scp=85175572631&partnerID=8YFLogxK
U2 - 10.1109/ICAC57885.2023.10275237
DO - 10.1109/ICAC57885.2023.10275237
M3 - Conference Proceeding
AN - SCOPUS:85175572631
T3 - ICAC 2023 - 28th International Conference on Automation and Computing
BT - ICAC 2023 - 28th International Conference on Automation and Computing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th International Conference on Automation and Computing, ICAC 2023
Y2 - 30 August 2023 through 1 September 2023
ER -