Phenomenolgical model inversion with Fisher information metrics for unexploded ordnance detection

Many of the ongoing efforts to develop strategies for detecting and locating subsurface unexploded ordnance (UXO) use features based on phenomenological models to discriminate between UXO and harmless clutter. The process of generating features requires model inversion to fit the phenomenological model to the measured sensor data. In commonly-used model inversion processes, the standard measures of model fit error do not incorporate the spatial distribution of the data used in the model inversion. This study incorporates the Fisher information in a joint metric optimization to assess the spatial distribution of data and how well the model parameters are supported by the data used in the model inversion. The outcomes of this study indicate that some outliers in the feature space can be mitigated by considering the Fisher information in the model inversion process, resulting in improved unexploded ordnance detection rates in a test using data collected at Camp Sibert, Alabama.