Abstract
This paper describes a novel target recognition scheme using High Range Resolution (HRR) radar signatures. AutoRegressive (AR) method is used to extract features from HRR radar echoes based on scattering center model of target. The optimal linear transformation based on Euclidian distribution distance criterion is performed on AR model parameter vectors to reduce dimension of feature vectors further and improve the class discrimination capability of feature vectors. The optimization algorithm is designed utilizing the quadratic property of criterion function and Gaussian kernel based Parzen window density function estimator. The concept of Stochastic Information Gradient (SIG) is incorporated into the gradient of cost function to decrease the computational complexity of the algorithm. Simulation results using three real airplanes, data show the effectiveness of the proposed method.
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Supported by the Basic Research Foundation of Tsinghua National Laboratory for Information Science and Technology (TNList) and the Major Program of the National Natural Science Foundation of Foundation of China (No. 60496311).
Communication author: Xie Deguang, born in 1965, male, Ph.D. candidate.
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Xie, D., Zhang, X. Feature extraction and recognition for echoes of HRR radar. J. Electron.(China) 26, 788–793 (2009). https://doi.org/10.1007/s11767-008-0103-y
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DOI: https://doi.org/10.1007/s11767-008-0103-y
Key words
- Radar target recognition
- Feature extraction
- AutoregRessive (AR) model
- Density function estimation
- Stochastic Information Gradient (SIG)