Application of a minimum probability of error classifier with Linear Time-Varying pre-filters for buried target recognition

摘要

In this paper we overview the theory of Linear Time-Varying (LTV) filters and investigate via simulation their application to buried target classification in challenging nonstationary environments; in particular, environments where noise is not only nonstationary but exhibits statistical properties that are not known a priori. We then propose an extension of the Minimum Probability of Error (MPE) classifier (a/k/a Minimum Distance Receiver) by pre-processing the received data through a bank of LTV filters before the calculation of each test statistic via the MPE classifier. The proposed augmented MPE classifier is shown to outperform the conventional MPE classifier via simulation.