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Abstract-In this paper, the radar target recognition is given by projected features of frequency-diversity RCS (radar cross section). The frequency diversity means signals are collected by sweeping the frequency of the incident illumination. Initially, the frequencydiversity RCS data from targets are collected and projected onto the PCA (principal components analysis) space. The elementary recognition of targets is efficiently performed on the PCA space. To achieve well separate recognition of targets, the features of the PCA space are further projected onto the LDA (linear discriminant algorithm) space. Simulation results show that accurate results of radar target recognition can be obtained by the proposed frequencydiversity scheme. In addition, the proposed frequency-diversity scheme has good ability to tolerate noise effects in radar target recognition.

Abstract-The noise effect is very challenging in radar target recognition. It usually degrades the accuracy of target recognition and then makes the recognition unreliable. In this study, we present a noise-reduction technique to improve the accuracy of radar target recognition. Our noise-reduction technique is based on the SVD (singular value decomposition). The PCA (principal components analysis) based radar recognition algorithm is utilized to verify our noise-reduction scheme. In our treatment, the received signals are arranged into a Hankel-form matrix. This Hankel-form matrix is decomposed into two subspaces, i.e., the noise-related subspace and clean-signal subspace. The noise reduction is obtained by suppressing the noise-related subspace and retaining the clean-signal space only. Simulation results show that the accuracy of target recognition is greatly improved as the received signals are first processed by the SVD noise-reduction technique. With the use of proposed noise-reduction scheme, the radar target recognition can tolerate more noises and then becomes more reliable. The noise-reduction technique in this study can also be applied to many other problems in radar engineering.