Abstract
We present an information fusion approach for ground vehicle classification based on the emitted acoustic signal. Many acoustic factors can contribute to the classification accuracy of working ground vehicles. Classification relying on a single feature set may lose some useful information if its underlying sound production model is not comprehensive. To improve classification accuracy, we consider an information fusion diagram, in which various aspects of an acoustic signature are taken into account and emphasized separately by two different feature extraction methods. The first set of features aims to represent internal sound production, and a number of harmonic components are extracted to characterize the factors related to the vehicle’s resonance. The second set of features is extracted based on a computationally effective discriminatory analysis, and a group of key frequency components are selected by mutual information, accounting for the sound production from the vehicle’s exterior parts. In correspondence with this structure, we further put forward a modified Bayesian fusion algorithm, which takes advantage of matching each specific feature set with its favored classifier. To assess the proposed approach, experiments are carried out based on a data set containing acoustic signals from different types of vehicles. Results indicate that the fusion approach can effectively increase classification accuracy compared to that achieved using each individual features set alone. The Bayesian-based decision level fusion is found to be improved than a feature level fusion approach.
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Acknowledgments
The Correspondence author is currently sponsored by Zhejiang Provincial “Qianjiang Rencai” Project of China (Grant No. 2010R10011), and partially sponsored by National Natural Science Foundation of China (Grant No. 61004119) and National Basic Research Program of China (973 Program, Grant No. 2009CB320600 or No. 2009CB320602). The research was sponsored by the US Army Research Laboratory and the UK Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001. The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the official policies, either expressed or implied, of the US Army Research Laboratory, the US Government, the UK Ministry of Defence or the UK Government. The US and UK Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
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Guo, B., Nixon, M.S. & Damarla, T. Improving acoustic vehicle classification by information fusion. Pattern Anal Applic 15, 29–43 (2012). https://doi.org/10.1007/s10044-011-0202-5
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DOI: https://doi.org/10.1007/s10044-011-0202-5