A comparison of optimal and suboptimal processors for classification of buried metal objects

Classification of metal objects is important for landmine and unexploded or dnance applications. Previously, we have investigated optimal classificatio n of landmine-like metal objects using wideband frequency-domain electromag netic induction data [1]. Here, a suboptimal processor, which is computatio nally less burdensome than the optimal processor, is discussed. The data is first normalized, exploiting the fact that the level of the response chang es significantly while the structure of the magnitude of the response chang es only slightly as the target/sensor orientation changes for the class of objects considered. Results indicate that the suboptimal processor performa nce approaches that of the optimal classifier on normalized data. Thus, nor malization mitigates the uncertainty resulting from the target/sensor orien tation.