The acoustic emissions of a ground vehicle contain a wealth of information, which can be used for vehicle classification, e.g. in the battlefield. However, features that are extracted from the acoustic measurements are time-varying and contain a lot of uncertainties, especially when the acoustic measurements are obtained from multiple terrains, which makes the classification challenging. In this paper we present our study on the multi-category classification of ground vehicles based on the acoustic data of four environmental conditions. The goal is to design one classifier that can operate in all four terrains without a priori knowledge of a specific terrain. We first perform the data pre-processing (including elimination of redundant records, processing of data distortion and generation of prototypes), feature extraction, and uncertainty analysis. We then develop the Bayesian classifier, and type-1 (T1) and interval type-2 (T2) fuzzy logic rule-based classifiers (FLRBC). These classifiers have similar architectures, consist of four sub-systems each for one terrain, and have one probability model (Bayesian classifier) or one fuzzy logic rule (T1 and interval T2 FLRBCs) for each kind of vehicle on each terrain. They differ in the way that this common architecture is implemented.
We also present the results of the experiments to evaluate the performance of all classifiers. Experimental results reveal that (1) both the T1 and interval T2 FLRBCs have better performance than the Bayesian classifier, and the interval T2 FLRBC has better performance than the T1 FLRBC; (2) each classifier has a smaller average but a slightly larger standard deviation of classification error rates when the majority voting-based temporal decision fusion is applied; and (3) when the majority voting-based temporal decision fusion is applied, both the T1 and interval T2 FLRBCs have better performance than the Bayesian classifier, and the interval T2 FLRBC has better performance than the T1 FLRBC.