Comparison of classifiers for radar emitter type identification

Comparison of classifiers for radar emitter type identification

Granger, Eric and Grossberg, Stephen and Lavoie, Pierre and Rubin, Mark A.

Intelligent Engineering Systems Through Artificial Neural Networks 1999

Abstract : ARTMAP neural network classifiers are considered for the identification of radar emitter types from their waveform parameters. These classifiers can represent radar emitter type classes with one or more prototypes, perform on-line incremental learning to account for novelty encountered in the field, and process radar pulse streams at high speed, making them attractive for real-time applications such as electronic support measures (ESM). The performance of four ARTMAP variants – ART-EMAP (Stage 1), ARTMAP-IC, fuzzy ARTMAP and Gaussian ARTMAP – is assessed with radar data gathered in the field. Simulation results indicate that fuzzy ARTMAP and Gaussian ARTMAP achieve an average classification rate consistently higher than that of the other ARTMAP classifiers, and comparable to that of the reference k nearest neighbor (kNN) and radial basis function (RBF) classifiers. ART-EMAP, ARTMAP-IC and fuzzy ARTMAP require fewer training epochs than Gaussian ARTMAP and RBF, and substantially fewer prototype vectors (thus, smaller physical memory requirements and faster fielded performance) than Gaussian ARTMAP, RBF and kNN. Overall, fuzzy ARTMAP performs at least as well as the other classifiers in both accuracy and computational complexity, and better than each of them in at least one of these aspects of performance. Incorporation into fuzzy ARTMAP of the MT-feature of ARTMAP-IC is found to be essential for convergence during on-line training with this data set.