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.
Abstract-In this paper, the ICA (independent component analysis) technique is applied to PCA (principal component analysis) based radar target recognition. The goal is to identify the similarity between the unknown and known targets. The RCS (radar cross section) signals are collected and then processed to serve as the features for target recognition. Initially, the RCS data from targets are collected by angular-diversity technique, i.e., are observed in directions of different elevation and azimuth angles. These RCS data are first processed by the PCA technique to reduce noise, and then further processed by the ICA technique for reliable discrimination. Finally, the identification of targets will be performed by comparing features in the ICA space. The noise effects are also taken into consideration in this study. Simulation results show that the recognition scheme with ICA processing has better ability to discriminate features and to tolerate noises than those without ICA processing. The ICA technique is inherently an approach of high-order statistics and can extract much important information about radar target recognition. This property will make the proposed recognition scheme accurate and reliable. This study will be helpful to many applications of radar target recognition.
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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