A comparison of the performance of statistical and fuzzy algorithms for unexploded ordnance detection. - Collins, L.M.
We focus on the development of signal processing algorithms that incorporate the underlying physics characteristic of the sensor and of the anticipated unexploded ordnance (UXO) target, in order to address the false alarm issue. In this paper, we describe several algorithms for discriminating targets from clutter that have been applied to data obtained with the multisensor towed array detection system (MTADS). This sensor suite includes both electromagnetic induction (EMI) and magnetometer sensors. We describe four signal processing techniques: a generalized likelihood ratio technique, a maximum likelihood estimation-based clustering algorithm, a probabilistic neural network, and a subtractive fuzzy clustering technique. These algorithms have been applied to the data measured by MTADS in a magnetically clean test pit and at a field demonstration. The results indicate that the application of advanced signal processing algorithms could provide up to a factor of two reduction in false alarm probability for the UXO detection problem
Keywords: Electromagnetic induction sensors; False alarm; Fuzzy clustering; Fuzzy set theory; Generalized likelihood ratio; Magnetometer sensors; Maximum likelihood estimation; Multisensor towed array detection system; Probabilistic neural network; Signal processing; Statistical analysis; Unexploded ordnance detection