Abstract
Kohonen neural network is capable of self-organizing and recognizingclustering center, which is used in many artificial intelligence (AI) fields. One electronic support measures (ESM) system must sort the received radar pulses to cells with same features by pulse parameters, such as radio frequency (RF), angle of arrival (AOA), pulse width (PW), Pulse Repetition Interval(PRI), etc. Kohonen SOFM algorithm is one valid method for clustering, which can be used to accomplish such radar pulses sorting. Considering the variety character of pulses parameters which is the character of modern radar system, a new definition of “distance” in the SOFM neural net is proposed in this paper, which decreases the effect of large variety range of special parameter among them. This paper employs the “distance” to improve the clustering capability in such special environments. The computer simulation shows the validity of these improvements.
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Zhao, C., Zhao, Y. (2006). Application of Improved Kohonen SOFM Neural Network to Radar Signal Sorting. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_62
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DOI: https://doi.org/10.1007/11893257_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
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