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

Xingchi Liu; Qing Li; Jiaming Liang; Jinzheng Zhao; Peipei Wu; Chenyi Lyu; Shidrokh Goudarzi; Jemin George; Tien Pham; Wenwu Wang; Lyudmila Mihaylova; Simon Godsill

doi:10.1117/12.2618105

6 June 2022 Advanced machine learning methods for autonomous classification of ground vehicles with acoustic data

Xingchi Liu, Qing Li, Jiaming Liang, Jinzheng Zhao, Peipei Wu, Chenyi Lyu, Shidrokh Goudarzi, Jemin George, Tien Pham, Wenwu Wang, Lyudmila Mihaylova, Simon Godsill

Author Affiliations +

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 121131P (2022) https://doi.org/10.1117/12.2618105
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Conference Poster

Abstract

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.

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Xingchi Liu, Qing Li, Jiaming Liang, Jinzheng Zhao, Peipei Wu, Chenyi Lyu, Shidrokh Goudarzi, Jemin George, Tien Pham, Wenwu Wang, Lyudmila Mihaylova, and Simon Godsill "Advanced machine learning methods for autonomous classification of ground vehicles with acoustic data", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121131P (6 June 2022); https://doi.org/10.1117/12.2618105

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