Graphical EM for on-line learning of grammatical probabilities in radar Electronic Support

Abstract

Although Stochastic Context-Free Grammars (SCFGs) appear promising for the recognition and threat assessment of complex radar emitters in radar Electronic Support (ES) systems, techniques for learning their production rule probabilities are computationally demanding, and cannot efficiently reflect changes in operational environments. On-line learning techniques are needed in ES applications to adapt SCFG probabilities rapidly, as new training data are collected from the field. In this paper, an efficient on-line version of the fast learning technique known as the graphical Expectation-Maximization (gEM) technique – called on-line gEM (ogEM) – is proposed. A second technique called on-line gEM with discount factor (ogEM-df) expands ogEM to allow for tuning the learning rate. The performance of these new techniques has been compared to HOLA, the only other fast on-line learning technique, from several perspectives – perplexity, error rate, complexity, and convergence time – and using complex radar signals. The impact on performance of factors like the size of new data blocks, and the level of ambiguity of grammars has been observed. Results indicate that on-line learning of new training data blocks with ogEMand ogEM-df provides the same level of accuracy as batch learning with gEM using all cumulative data from the start, even for small data blocks. As expected, on-line learning significantly reduces the overall time and memory complexities associated with updating probabilities with new data in an operational environment. Finally, while the computational complexity and memory requirements of ogEM and ogEM-df may be greater than that of HOLA, they both provide a significantly higher level of accuracy.