Advanced machine learning methods for autonomous classification of ground vehicles with acoustic data

This paper presents a distributed multi-class Gaussian process (MCGP) algorithm for ground vehicle classification using acoustic data. In this algorithm, the harmonic structure analysis is used to extract features for GP classifier training. The predictions from local classifiers are then aggregated into a high-level prediction to achieve the decision-level fusion, following the idea of divide-and-conquer. Simulations based on the acoustic-seismic classification identification data set (ACIDS) confirm that the proposed algorithm provides competitive performance in terms of classification error and negative log-likelihood (NLL), as compared to an MCGP based on the data-level fusion where only one global MCGP is trained using data from all the sensors.