Abstract
When applying Parameter Discriminant Analysis (PDA) in extracting features of radar target High-Resolution Range Profile (HRRP), the construction of scatter matrices relies on the assumption that HRRPs in all classes satisfy the Gaussian distribution with the same covariance matrix. However, the distribution of HRRP is actually complex. In order to tackle this problem, a radar target recognition approach based on nonparametric feature analysis and back cloud model is proposed in this paper. Compared with PDA, nonparametric feature analysis (NFA) estimates the contribution of the K nearest neighbors (KNN) points to calculate the between-class scatter matrix. NFA makes use of class boundary information and relaxes the requirement of Gaussian distribution assumption in PDA. Moreover, back cloud model better describes the complex distribution of the HRRP NFA subspace due to the representation of signal’s randomness and fuzziness. Simulation results based on a HRRP dataset of five aircraft models demonstrate the effectiveness of the proposed approach.
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