Radar high-resolution range profile target identification method based on three-dimensional convolutional network

Abstract

The invention relates to a radar high-resolution range profile target recognition method based on a three-dimensional convolutional network. The method comprises the steps of: obtaining original datax, and dividing the original data x into a training sample set and a test sample set; calculating to obtain segmented and recombined data x'' '' ' according to the original data x; establishing a three-dimensional convolutional neural network model; constructing the three-dimensional convolutional neural network model according to the training sample set and the segmented and recombined data x'''' ' to obtain a trained convolutional neural network model; and performing target identification on the test sample set according to the trained convolutional neural network model. The method is highin robustness, is high in target recognition rate, and solves a major problem of a conventional high-resolution range profile recognition technology.

Description

technical field [0001] The invention belongs to the technical field of radar, and in particular relates to a radar high-resolution range image target recognition method based on a three-dimensional convolution network. Background technique [0002] The range resolution of the radar is proportional to the received pulse width after the matched filter, and the range unit length of the radar transmitted signal satisfies: ΔR is the distance unit length of the radar transmission signal, c is the speed of light, τ is the pulse width of matching reception, and B is the bandwidth of the radar transmission signal; the large bandwidth of the radar transmission signal provides high distance resolution (High Rang Resolution, HRR) . In fact, the distance resolution of the radar is relative to the observation target. When the size of the observed target along the radar line of sight is L, if L<<ΔR, the corresponding radar echo signal width is different from the radar emission puls...

AI Technical Summary

Problems solved by technology

[0005] At present, many target recognition methods for high-resolution range image data have been developed. For example, the more traditional support vector machine can be directly used to directly classify the target, or the feature extraction method based on the restricted Boltzmann machine can be used to first extract the data Projecting into a high-dimensional space and then classifying the data with a classifier; but the above methods only use the time domain characteristics of the signal, and the target recognition accuracy is not high

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