Syntactic Modeling and Neural-Based Parsing for Multifunction Radar Signal Interpretation

The analysis of intercepted multifunction radar (MFR) signals and the assessment of potential threats are critical in the field of radar countermeasures. The complexity of MFR control mechanisms poses serious challenges in modeling and recognizing their internal states. MFRs, being discrete event systems (DESs), can be well described by formal languages. This paper presents a generalized version of the previous syntactic modeling of MFRs, utilizing the proposed latent variable controlled stochastic context-free grammar (LVCSCFG). The enhanced model incorporates a mapping between the external environment and the production probabilities, offering a more effective representation of the resource management process. Additionally, we introduce a neural-based approach for maximum likelihood estimation of the MFR's internal states, which are represented by a grammar tree. LVCSCFG exhibits enhanced representation of MFR dynamic behaviors, and simulation results indicate that the neural network outperforms traditional statistical parsing algorithms in terms of speed and precision under various non-ideal channel conditions.