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.

Abstract-In this paper, the angular-diversity radar recognition of ships is given by transformation based approaches with noise effects taken into consideration. The ships and sea roughness are considered by simplified models in the simulation. The goal is to identify the similarity between the unknown target ship and known ships. Initially, the angular-diversity radar cross sections (RCS) from a ship are collected to constitute RCS vectors (usually largedimensional). These RCS vectors are projected into the eigenspace (usually small-dimensional) and radar recognition is then performed on the eigenspace. Numerical examples show that high recognition rate can be obtained by the proposed schemes. The radar recognition of ships in this study is straightforward and efficient. Therefore, it can be applied to many other radar applications.

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