Performance Evaluation of Self Organizing Neural Networks for Clustering in Esm Systems

Gencol, KenanGençol, KenanTora, HakanTora, HakanAirframe and Powerplant MaintenanceDepartment of Electrical & Electronics Engineering2024-10-062024-10-062014297814799487412165-0608[WOS-DOI-BELIRLENECEK-290]https://hdl.handle.net/20.500.14411/9042GENCOL, Kenan/0000-0003-4044-3482Electronic Support Measures (ESM) system is an important function of electronic warfare which provides the real time projection of radar activities. Such systems may encounter with very high density pulse sequences and it is the main task of an ESM system to deinterleave these mixed pulse trains with high accuracy and minimum computation time. These systems heavily depend on time of arrival analysis and need efficient clustering algorithms to assist deinterleaving process in modern evolving environments. On the other hand, self organizing neural networks stand very promising for this type of radar pulse clustering. In this study, performances of self organizing neural networks that meet such clustering criteria are evaluated in detail and the results are presented.trinfo:eu-repo/semantics/closedAccess[No Keyword Available]Performance Evaluation of Self Organizing Neural Networks for Clustering in Esm SystemsConference Object22332236WOS:000356351400538