Real-time object detection using dynamic principal component analysis

In this work, we contribute to the real-time detection of buried objects, with special emphasis on explosive ones, using the ground penetrating radar (GPR). When the buried objects have explosive substance, the moment of detection becomes vital. Therefore, we start the detection process right after the very first GPR signals begin to return from the buried objects. For this purpose, we adopted the studies focusing on the online process monitoring methods using principal component analysis (PCA), and adapted them to the dynamic conditions of the ground. Different objects with varying dielectric properties are buried in the test environment and used for the evaluation of the proposed method. With the observed results, it is validated that, the proposed method is employable towards the real-time object detection.

[1]  P. Gemperline,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[2]  G. Baker,et al.  Ground penetrating radar imaging of a 4th Century Roman Fort, Humayma, Jordan , 2007, 2007 4th International Workshop on, Advanced Ground Penetrating Radar.

[3]  Khiang-Wee Lim,et al.  On-Line Process Monitoring and Fault Isolation Using PCA , 2005, Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005..

[4]  J. Edward Jackson,et al.  A User's Guide to Principal Components: Jackson/User's Guide to Principal Components , 2004 .

[5]  Joseph N. Wilson,et al.  Detecting landmines with ground-penetrating radar using feature-based rules, order statistics, and adaptive whitening , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Rick L. Edgeman,et al.  Multivariate Statistical Process Control with Industrial Applications , 2004, Technometrics.

[7]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies , 2003, Comput. Chem. Eng..

[8]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[9]  Paul D. Gader,et al.  A linear prediction land mine detection algorithm for hand held ground penetrating radar , 2002, IEEE Trans. Geosci. Remote. Sens..

[10]  Weihua Li,et al.  Recursive PCA for adaptive process monitoring , 1999 .

[11]  Sam T. Roweis,et al.  EM Algorithms for PCA and SPCA , 1997, NIPS.

[12]  George A. McMechan,et al.  GPR characterization of buried tanks and pipes , 1997 .

[13]  David J. Daniels,et al.  Surface-Penetrating Radar , 1996 .

[14]  J. Edward Jackson,et al.  A User's Guide to Principal Components. , 1991 .

[15]  Diego Garcia-Alvarez,et al.  Fault Detection Using Principal Component Analysis ( Pca ) in a Wastewater Treatment Plant ( Wwtp ) , 2009 .

[16]  Jeffrey J. Daniels,et al.  Ground Penetrating Radar Fundamentals , 2000 .