Radar target identification method based on improved time sequence convolutional network

Abstract

The invention discloses a radar target recognition method based on an improved time sequence convolutional network, and the method comprises the steps: firstly carrying out the preprocessing of an original HRRP sample set, and extracting the data features of HRRP through a TCN model; a multi-level attention module is adopted, and feature importance of different segments of the data is scaled in a self-adaptive mode; and finally, more effective features are reserved through a full connection layer, and the output of the network is classified by adopting softmax. According to the method, the causal sequence characteristics of the HRRP are established through causal convolution, the receptive field of the model is expanded through expansion convolution and stacking model depth, and more comprehensive feature information is extracted. A multi-level attention mechanism for a multi-level time convolution network is provided, importance adjustment is performed on target structure features reflected by different levels, high-separability level features are highlighted, useless features are inhibited, and the influence of output of each level on a recognition result is adaptively adjusted.

Description

technical field [0001] The invention belongs to the field of radar target recognition, in particular to a radar target recognition method based on an improved time series convolutional network Background technique [0002] With the development of radar technology, the range resolution of modern broadband radar is much smaller than the size of the target along the radar line of sight. The radar observation target changes from a "point" target to a "surface" target in the echo, and the target echo is The sum of the sub-echoes of the scattered points on the "surface", that is, the sum of the echoes of the important components of the target. In fact, the reflected echoes of targets such as ships and airplanes can be regarded as composed of multiple independent range units, and each range unit is superimposed by scattering points of different intensities. Usually, the echo signal received by such range high-resolution broadband radar is called the high-resolution one-dimensional...

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Problems solved by technology

At the same time, we use the temporal convolutional network to solve the problem that the traditional convolutional network model cannot model the time series of HRRP samples, and make up for the long-term dependence decay problem of the RNN model

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