Target detection and identification using synthetic aperture acoustics

Recent research has shown that synthetic aperture acoustic (SAA) imaging may be useful for object identification. The goal of this work is to use SAA information to detect and identify four types of objects: jagged rocks, river rocks, small concave capped cylinders, and large concave capped cylinders. More specifically, we examine the use of frequency domain features extracted from the SAA images. We utilize Support Vector Machines (SVMs) for target detection, where an SVM is trained on target and non-target (background) examples for each target type. Assuming perfect target detection, we then compare multivariate Gaussian models for target identification. Experimental results show that SAA-based frequency domain features are able to detect and identify the four types of objects. © 2014 SPIE.6.

Duke Scholars

Author Stacy L. Tantum Electrical and Computer Engineering

Author Leslie M. Collins Electrical and Computer Engineering