Incremental Learning of Stochastic Grammars with Graphical EM in Radar Electronic Support

Although stochastic context-free grammars (SCFGs) appear promising for recognition of radar emitters, and for estimation of their level of threat in radar electronic support (ES) systems, well-known techniques for learning their production rule probabilities are computationally demanding, and cannot efficiently reflect changes in operational environments. Some techniques have been proposed for fast learning of SCFGs probabilities, yet, of those, only the HOLA technique can perform learning incrementally. In this paper, two incremental versions of the graphical EM (gEM) technique are proposed. The incremental gEM (igEM) and on-line incremental gEM (oigEM) allow for adapting production rule probabilities from new data, without having to retrain from the start on all accumulated training data. These new techniques are compared to HOLA using radar signal data. An experimental protocol has been defined such that the impact on performance of factors like the size of new data blocks for incremental learning, and the level of ambiguity of MFR grammars, may be observed. Results indicate that, contrary to HOLA, incremental learning of training data blocks with igEM and oigEM provides the same level of accuracy as learning from all cumulative data from scratch, even for small data blocks. As expected, incremental learning significantly reduces the overall time and memory complexities. Finally, it appears that while the computational complexity and memory requirements of igEM and oigEM may be greater than that of HOLA, they both provide a higher level of accuracy