| Title |
Visibility Graph-based Tree-structured Radar Scan Pattern Classification |
| DOI |
https://doi.org/10.5573/ieie.2025.62.2.43 |
| Keywords |
Electronic warfare support; Radar scan pattern; Visibility graph; Tree-structured classification |
| Abstract |
In dense electronic warfare environments, the conventional radar identification methods based on the basic parameters such as frequency, pulse width, and pulse repetition interval are confronted by the problem of identification ambiguity. To overcome this critical problem, a new approach that utilizes radar scan patterns has been introduced. In this paper, to more accurately classify radar scan patterns, we model a radar signal generation technique that considers various operational variables and reception environments within an electronic warfare support system and extract visibility graphs from the generated radar signals. To achieve high classification accuracy, we classify signals using a tree-structured approach and apply the GoogLeNet deep neural network using visibility graphs as input to improve scan pattern classification performance. Simulation results show that the proposed method achieves a performance of 92% accuracy, 90% precision, and 89% recall, demonstrating improved classification performance compared to conventional methods. |