Abstract:

This paper develops an anomaly detection algorithm for subsurface object detection using the handheld ground penetrating radar. The algorithm is based on the Mahalanobis distance measure with adaptive update of the background statistics. It processes the data sequentially for each data sample in a causal manner to generate detection confidences. The algorithm is applied to process the data from two different radars, an impulse and a step-frequency, for performance evaluation.

Links:

Citation:

K. C. Ho, S. Harris, A. Zare, and M. Cook, “Anomaly detection of subsurface objects using handheld ground-penetrating radar,” in Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 2015. 

@InProceedings{ho2015anomaly,
Title = {Anomaly detection of subsurface objects using handheld ground-penetrating radar},
Author = {K. C. Ho and Samuel Harris and Alina Zare and Matthew Cook},
Booktitle = {Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX},
Year = {2015},
Month = {May},
Number = {94541B},
Volume = {9454},
Doi = {10.1117/12.2178584},
}