Radar Target Recognition Based on Semiparametric Density Estimation of SLC

Abstract

In order to solve the problem of the decline of accuracy when using the nonparametric method—Stochastic Learning of the Cumulative (SLC) to estimate the density of High-Resolution Range Profile (HRRP) in radar target recognition under the condition that the samples are not enough, a radar target recognition approach based on the semiparametric density estimation of SLC is proposed in this paper. This method has the ability to make use of empirical knowledge which is known as the approximate Gamma distribution of amplitudes in each HRRP range cells, and the Gamma density estimate is then corrected by multiplying with SLC of a correction factor. Obviously, both advantages of parametric method and nonparametric method of SLC are merged in the semiparametric density estimation of SLC. Simulation results based on the HRRP dataset of five aircraft models demonstrate the effectiveness of the proposed approach.

Keywords

• Radar target recognition
• High-Resolution Range Profile (HRRP)
• Density estimation
• Semiparametric density estimation of SLC (Stochastic Learning of the Cumulative)