Accession Number : ADA548702


Title :   Robust Statistics and Regularization for Feature Extraction and UXO Discrimination


Descriptive Note : Final rept.


Corporate Author : SKY RESEARCH INC VANCOUVER (CANADA)


Personal Author(s) : Billings, Stephen ; Beran, Laurens ; Oldenburg, Doug


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a548702.pdf


Report Date : Jul 2011


Pagination or Media Count : 68


Abstract : Current methods for Unexploded Ordnance (UXO) discrimination using magnetic and electromagnetic induction (EMI) data generally rely on feature vectors extracted from physics based dipole models. These feature vectors are obtained by solving an inverse problem that provides a best-fit to the observed data. Typically, this best-fit is defined as the model that minimizes the sum-of-squares of the residuals between observed and predicted data, with each residual weighted by an estimated standard deviation (the-so-called L2 norm). Thus, there is an implicit assumption that the residuals are normally distributed (Gaussian) and that the maximum likelihood solution is the most appropriate model to extract from the data. This assumption of Gaussian statistics may not be appropriate if the residuals have outliers (due to sensor or positional glitches) or if the residuals contain significant structure (model not adequate to represent the data). In those cases, the predicted feature vectors may be significantly in error and should not be relied upon for discrimination. In addition, the maximum likelihood solution does not account for any uncertainty in the recovered feature vectors and may not be the most appropriate criterion to use to assess UXO likelihood. In this project we researched the statistical structure of the underlying inversion process and developed methods for more accurate extraction of feature vectors from multi-time, multi-frequency and multi-component EMI data e four main areas explored with the first three involving different treatments of Bayes equation for combining a-priori knowledge with the constraints imposed by the observed data.


Descriptors :   *DIPOLES , *ELECTROMAGNETIC INDUCTION , *EXTRACTION , *UNEXPLODED AMMUNITION , *VECTOR ANALYSIS , CANADA , DISCRIMINATION , ERRORS , ESTIMATES , FEATURE EXTRACTION , MAGNETIC FIELDS , MAXIMUM LIKELIHOOD ESTIMATION , MODELS , RESIDUALS , SOLUTIONS(GENERAL) , STATISTICAL PROCESSES , WEIGHTING FUNCTIONS


Subject Categories : Industrial Chemistry and Chemical Processing
      Ammunition and Explosives
      Electricity and Magnetism


Distribution Statement : APPROVED FOR PUBLIC RELEASE