Aircraft HRRP classification based on RBFNN

Abstract

We present a classification scheme based on a new kind of RBFNN (radial basis function neural network) whose structure is similar to that of AWNN (adaptive wavelet neural network). To be more suitable for HRRP (high resolution range profile) classification, this kind of RBFNN substitutes wavelet basis functions in AWNN with Gaussian basis functions. In addition, we also devise an RBFNN initialization method of clear physical significance, and propose a decision rule based on average output vectors of RBFNNs. The new scheme is applied to HRRP classification of six aircraft at different SNR levels, and the results are compared with that obtained by MCCM (maximum correlation coefficient method). It is indicated that the RBFNN-based classification method has the potential in complex target classification and is promising to develop more practical HRRP classifiers.