A Parameter-Optimized LSSVM Method for Operation Modes Recognition of Airborne Fire Control Radar
WANG Yubing
CHENG Siyi
ZHOU Yipeng
GUO Pengcheng
Abstract：In the light of realizing the self-learning operation mode recognition of air borne fire control radar,an optimized LSSVM algorithm based on grid search and K-fold verification is proposed.First,This paper extracts feature parameters from non-cooperative radar signal and establishes library base of radar signal as training sample in LSSVM model.Next,the paper applies grid search method in parameter optimization to realize model adjustment under the circumstance of uncertain sample range.In the sequel,The paper utilizes K-fold cross validation for realizeing performance evaluation and reduceing model error caused by sample randomness improv generalization ability.Simulation results show that recognition accuracy of VS/RWS/TWS/STT modes reaches 97%,thus having a good recognition performance and practical value of proposed method.
Keywords：airborne fire control radarworking modes recognitionLSSVMgrid searchK-fold cross validation
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 49-53 )