Identification method for radar disoperative target based on mixed model

Abstract

The invention provides an identification method for a radar disoperative target based on a mixed model, which is used for solving the problem that the disoperative target is low in identification rate. The method comprises the following steps: establishing a standard body model library of a refined scattering point model; structurally decomposing the disoperative target according to the standard body model library to generate a first scattering point model; shielding the first scattering point model to obtain an effective scattering point model; calculating RCS intensity for the effective scattering point to obtain intensity information and combining the intensity information to generate a scattering point matrix; adding a statistic characteristic into the scattering point matrix to obtain a second scattering point model of the disoperative target containing coordinate information and the RCS intensity information; carrying out multi-scattering point radar return simulation on the second scattering point model to establish a high resolution one-dimensional range profile template library; and identifying the tested high-resolution one-dimensional range profile by adopting a K near neighbor classifier by virtue of the high resolution one-dimensional range profile template library. According to the method provided by the invention, the target identification performance of the radar system can be improved.

Description

technical field [0001] The invention relates to the field of radar technology, in particular to a method for identifying radar non-cooperative targets based on a mixed model. Background technique [0002] The radar target recognition technology is based on the detection and positioning of the target by the radar, extracting the characteristic information of the target contained in the radar echo, and realizing the judgment of the attribute and type of the target. In the battlefield environment, the main task of the radar target recognition system is to effectively identify and classify the enemy's non-cooperative targets on the basis of eliminating non-interesting targets. For the classification problem in pattern recognition, the target recognition system needs to establish a template library for all possible target categories, and on this basis, use machine learning theory to complete the system training. However, for the enemy's non-cooperative targets, it is impossible ...

AI Technical Summary

Problems solved by technology

The method of moments is an integral numerical method based on functional analysis theory, which is suitable for solving various problems such as antennas and electromagnetic compatibility. space, so it is not suitable for real-time processing of larger objects

Both GRECO and SBR are established on the basis of high-frequency progressive methods. GRECO mainly considers specular reflection and edge scattering, and uses a combination of physical optics (PO) and physical theory of diffraction (PTD). method to solve the scattering field of the target, but it cannot solve the dihedral angle and cavity structure that are common in large targets; SBR uses a hybrid method of geometrical optics (GO) and PO to solve the The edge scattering description in

However, in radar target recognition systems, synthetic broadband technology is usually used to generate radar high-resolution one-dimensional range images, and conventional frequency-domain high-frequency electromagnetic scattering calculation methods are not suitable for generating radar high-resolution one-dimensional range images

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