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 sy...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2024-08, Vol.60 (4), p.5060-5072
Main Authors: Feng, Han Cong, Jiang, Kai Li, Zhou, Zhixin, Zhao, YuXin, Yan, Hai Xin, Tian, Kailun, Tang, Bin
Format: Article
Language:English
Subjects:
Discrete event systems
Electronic warfare
Grammar
Maximum likelihood estimation
Modelling
multifunction radar (MFR)
Neural networks
Parsing algorithms
Production
Radar
Representations
Resource management
sequence-to-sequence (seq2seq) learning
Stochastic processes
Symbols
syntactic modeling
syntactic pattern recognition
Syntactics
Task analysis
Threat evaluation
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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, can be well described by formal languages. This article 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 MFRs 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 nonideal channel conditions.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2024.3384950