Abstract
Inspired by biological systems’ (such as human’s) innate ability to process and integrate information from disparate, network-based sources, we apply biologically-inspired information integration mechanisms to target detection in cognitive radar sensor network. Humans’ information integration mechanisms have been modelled using maximum-likelihood estimation (MLE) or soft-max approaches. In this paper, we apply these two algorithms to radar sensor networks target detection. Discrete-cosine-transform (DCT) is used to process the integrated data from MLE or soft-max. We apply fuzzy logic system (FLS) to automatic target detection based on the AC power values from DCT. Simulation results show that our MLE-DCT-FLS and soft-max-DCT-FLS approaches perform very well in the radar sensor network target detection, whereas the existing 2-D construction algorithm doesn’t work in this study.
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© 2009 Springer-Verlag Berlin Heidelberg
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Liang, Q. (2009). Biologically-Inspired Target Recognition in Radar Sensor Networks. In: Liu, B., Bestavros, A., Du, DZ., Wang, J. (eds) Wireless Algorithms, Systems, and Applications. WASA 2009. Lecture Notes in Computer Science, vol 5682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03417-6_12
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DOI: https://doi.org/10.1007/978-3-642-03417-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03416-9
Online ISBN: 978-3-642-03417-6
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