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

Abstract

The invention discloses a radar high-resolution range profile target identification method based on a one-dimensional convolutional neural network. The radar high-resolution range profile target identification method comprises the steps of: determining Q different radars, wherein a target exists within detection ranges of the Q different radars, acquiring high-resolution radar echoes of the Q different radars, then acquiring Q-type high-resolution range imaging data from the high-resolution radar echoes of the Q different radars, dividing the Q-type high-resolution range imaging data into a training sample set and a test sample set, and recording the Q-type high-resolution range imaging data as original data x; calculating to obtain data x'' ' after mean normalization processing accordingto the original data x; setting a one-dimensional convolutional neural network model, and constructing the one-dimensional convolutional neural network model by using the training sample set and the data x'' ' after mean normalization processing, so as to obtain a trained convolutional neural network; and performing target identification on the trained convolutional neural network by using the test sample set, so as to obtain a radar high-resolution range profile target identification result based on the one-dimensional convolutional neural network.

Description

technical field [0001] The invention belongs to the field of radar technology, in particular to a radar high-resolution range image target recognition method based on a one-dimensional convolutional neural network, which is suitable for target recognition on radar high-resolution range image data, and for environment detection and track tracking . 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: △R is the distance unit length of the radar transmission signal, c is the speed of light, τ is the matching received pulse width, and B is the bandwidth of the radar transmission signal; the large bandwidth of the radar transmission signal provides high distance resolution [0003] (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 ...

AI Technical Summary

Problems solved by technology

[0006] 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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