Abstract
Emitter signal parameter analysis has been widely recognized as one crucial task for communication, electronic reconnaissance and radar intelligence analysis. However, the parameter measurements are characteristic of uncertainty in the form of intervals. In addition, the measurements are typically accumulated continuously. Existing machine learning methods for interval-valued data are unfit in such a case as they generally assume a uniform distribution and are restricted to static data analysis. To address the above problems, we bring forward an incremental class discriminant analysis method on interval-valued emitter signal parameters. Experimental results have validated its effectiveness.
This work was supported by National Natural Science Foundation of China (No. 61402426, 61373129) and partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization.
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Xu, X., Wang, W., Lu, J. (2015). Incremental Class Discriminant Analysis on Interval-Valued Emitter Signal Parameters. In: Liu, A., Ishikawa, Y., Qian, T., Nutanong, S., Cheema, M. (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture Notes in Computer Science(), vol 9052. Springer, Cham. https://doi.org/10.1007/978-3-319-22324-7_28
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DOI: https://doi.org/10.1007/978-3-319-22324-7_28
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