The countermeasure of enemy phased array radar by mounting electronic warfare equipment on cluster platform is one of the important tactical means to make up for the short board of single-unit countermeasure capability. Interactive and intelligent comprehensive analysis of the same radiation source information perceived by individuals in the phased array radar group is an indispensable step before the comprehensive identification, prediction and decision-making of the operation status and intent of the radar group. The existing method searches target information among all the individuals in the cluster, which not only increases the unnecessary network online interaction and computational burden but also generates a large amount of redundant information. To solve the above problems, this paper designs a decentralized cluster radar countermeasure system framework based on cluster intelligence and neural networks for information fusion. Under this framework, based on swarm intelligence technology (SI), an individual local interaction mechanism is designed, and optimal subgroups representing the characteristics of radiation sources emerged through self-organization among individuals. Finally, the feasibility of the framework is verified by the simulation, which demonstrates that the recognizable radar word fragments can be increased by the cascade of neural networks under information fusion.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.