Identifying an Unknown Flying Target in Missile Defense Systems Using Intelligent Fuzzy Neural Networks

Abstract

For radar automatic target recognition (RATR), this paper aims at identifying the incoming unknown flying missiles in the Missile Defense Systems (MDS), using intelligent fuzzy neural networks (FNNs) with intelligent feature extraction. The training data for FNNs is obtained by sampling the Radar Power Signal Envelop (RPSE) of the radar echo signal (from High Resolution Range Profile (HRRP) radar) for the incoming flying missiles under different azimuth and elevation angles. The RPSEs under different irradiation angles of incoming flying targets can be generated by a high frequency structural simulator (HFSS) package, which is close to real RPSE and has been adopted by academic researchers in this area. The premise part in FNN is composed of a set of Uniform Distributed Gaussian Membership Functions (UDGMFs) and the consequent part is a two layer Neural Network (NN) which can be trained by Dynamic Optimal Training Algorithm (DOTA). By using this approach, the identification of five different flying missiles is performed in this paper. From the bench mark test, this intelligent FNN configuration can identify the unknown flying missiles very accurately due to the fact that the number of training patterns is well below the capacity of the proposed FNN configuration under a certain noise intensity.