Combined Gaussian model radar target steady recognition method based on noise apriority

Abstract

The invention provides a combined Gaussian model radar target steady recognition method based on noise apriority, and the method is mainly used for solving the problem that a statistical model in an existing radar plane target recognition technology is unsteady in noise. The method is implemented through the following steps: preprocessing HRRP (High Resolution Range Profile) data of a radar; performing impression taking on the processed data so as to obtain time domain features of the preprocessed data; determining the average value, loading matrix and noise covariance matrix of each frame ofcombined Gaussian model of HRRP data of radar training targets; accounting the noise variance in the non-signal supporting region of HRRP data in a radar test and calculating the average value and the modified value of the noise covariance matrix of the combined Gaussian model according to the noise variance; calculating the posteriori probability values of time domain features, corresponding to each radar test target, of the HRRP data of the radar test targets; and determining the type attribute of the HRRP data of the radar test targets. The method has the advantage of being steady in noise, and can be used for the steady recognition of radar and plane targets.

Description

technical field [0001] The invention belongs to the technical field of radar and relates to robust automatic target recognition, in particular to a noise robust target recognition method for radar target high-resolution range images, which can be used for radar automatic target recognition. Background technique [0002] Radar automatic target recognition technology can provide information such as target attributes, categories, models, etc. High-resolution radar usually works in the microwave band, and the length of the target and its components is much longer than the wavelength. At this time, the radar target can be approximated as a set of discrete scattering points. Correspondingly, the radar transmitted signal is backscattered by the target scattering point, and the scattering point sub-echo is formed after the amplitude modulation delay, and the high-resolution range image of the radar target is the vector sum of each scattering point echo, which is the high-resolution r...

AI Technical Summary

Problems solved by technology

[0005] 1) Only the recognition performance of the radar automatic target recognition model under the various signal-to-noise ratios of the test radar target high-resolution range image is given, and the noise prior in the test radar target high-resolution range image is not used to improve the radar automatic target recognition statistics Model

[0006] 2) The proposed radar automatic target recognition statistical model is not robust, and the recognition performance is poor when the signal-to-noise ratio of the high-resolution range image of the test radar target is not high

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