A comparison of self-organizing neural networks for fast clustering of radar pulses

Four self-organizing neural networks are compared for automatic deinterleaving of radar pulse streams in electronic warfare systems. The neural networks are the Fuzzy Adaptive Resonance Theory, Fuzzy Min-Max Clustering, Integrated Adaptive Fuzzy Clustering, and Self-Organizing Feature Mapping. Given the need for a clustering procedure that ooers both accurate results and computational eeciency, these four networks are examined from three perspectives | clustering quality, convergence time, and computational complexity. The clustering quality and convergence time are measured via computer simulation, using a set of radar pulses collected in the eld. The eeect of the pattern presentation order is analyzed by presenting the data not just in random order, but also in radar-like orders called burst and interleaved. Estimation of the worst-case running time for each network allows for the assessment of computational complexity.