Information-driven Guidance and Control of Heterogenous Underwater Sensor N

Information-driven Guidance and Control of Heterogenous Underwater Sensor Networks for Adaptive Target Detection and Classification

Project Description:

This project develops information-driven navigation and control algorithms for underwater target detection and identification based on the Convolutional Neural Networks (CNN).

Peer-Reviewed Publications:

Pingping Zhu, Jason Isaacs, Bo Fu, Silvia Ferrari, “Deep Learning Feature Extraction for Target Recognition and Classification in Underwater Sonar Images,” IEEE Conference on Decision and Control (CDC), 2017. [Link]
Shi Chang, Jason Isaacs, Bo Fu, Jaejeong Shin, Pingping Zhu, and Silvia Ferrari, “Confidence Level Estimation in Multi-target Classification Problems,” SPIE Defense + Commercial Sensing, 2018. [Link][PDF]

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