Abstract
A novel short-wave passive location algorithm based on radial basis function neural networks (RBFNN) is proposed. Using the RBFNN's characteristic of approximating any continuous nonlinear function, the fusion of electromagnetic spectrum sensing data is realized by sample learning. And the mapping model of short-wave monitoring data, detecting data and targets' position information is constructed. Moreover, the effect of error and ionosphere disturbance on the accuracy of location accuracy can be relieved by using the generalization and robustness of RBFNN. A large number of measured data verify the scientificity and the accuracy of the proposed algorithm.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
