Abstract
A new pre-processing stage for neural radar detectors is presented in order to reduce the detector performance dependence on the Training Signal-to-Noise Ratio (TSNR). The proposed scheme combines Time-frequency Analysis for transforming radar echoes into a feature space where the detection task is easier, and Principal Component Analysis for dimensionality reduction. The results are compared with those obtained when using a single MLP, demonstrating that the new detection scheme can match the best receiver operating characteristic of the single MLP radar detector, for any value of TSNR, avoiding the laborious trial-and-error process that is necessary to select the optimum TSNR for a single MLP radar detector.
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© 2001 Springer-Verlag Berlin Heidelberg
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Jarabo Amores, P., Rosa Zurera, M., López Ferreras, F. (2001). Design of a Pre-processing Stage for a voiding the Dependence on TSNR of a Neural Radar Detector. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_79
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DOI: https://doi.org/10.1007/3-540-45723-2_79
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