SVDD radar target one-dimensional distance image identification method based on density weight and hybrid kernel function

Abstract

The invention discloses an SVDD radar target one-dimensional distance image identification method based on density weight and a hybrid kernel function and belongs to the radar target identification field. The K-type kernel function has strong generalization capability, extraction of global characteristics of training data is facilitated, complex index operation of a radial basic kernel function isavoided, and properties of small calculation complexity of a polynomial kernel function and high approximation precision of the radial basic kernel function are realized. The radial basic kernel function has quite good local characteristics, the K-type kernel function and the radial basic kernel function are combined to replace a kernel function of a traditional SVDD algorithm, moreover, a localdensity algorithm based on interception distance is employed to calculate local density between a support vector and training sample data in the high-dimensional kernel characteristic space, accordingto density distribution, the shape of a super-closed ball is adjusted, and identification performance of radar one-dimensional distance image single type targets is effectively improved.

Description

Technical field [0001] The invention is suitable for the field of radar target recognition, and specifically relates to a one-dimensional range profile recognition of SVDD radar target based on density weight and hybrid kernel function. Background technique [0002] The Radar High Resolution Range Profile (HRRP) is the projected vector sum of the scattered point echoes of the target to be identified along the line of sight of the radar, which reflects the distribution of the scattered points of the target on the line of sight of the radar. Compared with the radar cross-section (RCS) of the target obtained by the low-resolution radar, it can obtain more information about the structure and shape of the target; compared with the synthetic aperture radar (Synthetic ApertureRadar, SAR) image Compared with the Inverse Synthetic Aperture Radar (ISAR) image, it has the characteristics of easy acquisition and small storage capacity, so it has been widely used in the field of Radar Automat...

AI Technical Summary

Problems solved by technology

The traditional SVDD algorithm usually uses the radial basis kernel function as the kernel function, but the radial basis kernel function has the disadvantages of large amount of calculation and weak generalization performance, which leads to the decline of the performance of the entire recognition algorithm.

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