Abstract
Since the development of pulse compression in the mid-1950’s the concept has become an indispensable feature of modern radar systems. A matched filter is used on reception to maximize the signal to noise ratio of the received signal. The actual waveforms that are transmitted are chosen to have an autocorrelation function with a narrow peak at zero time shift and the other values, referred to as sidelobes, as low as possible at all other times. A new approach to radar pulse compression is introduced, namely the Reproducing Kernel Hilbert Space (RKHS) method. This method reduces sidelobe levels significantly. The paper compares a second degree polynomial kernel RKHS method to a least squares and L 2P -norm mismatched filter, and concludes with a presentation of the representative testing results.
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Jordaan, J.A., van Wyk, M.A., van Wyk, B.J. (2007). Reproducing Kernel Hilbert Space Methods to Reduce Pulse Compression Sidelobes. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_28
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DOI: https://doi.org/10.1007/978-3-540-77226-2_28
Publisher Name: Springer, Berlin, Heidelberg
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