Feature extraction method for one-dimensional distance image of true and false targets with non-linear discriminant learning

Abstract

The invention discloses a feature extraction method for one-dimensional distance image of true and false targets with non-linear discriminant learning, and belongs to the technical field of radar target recognition. The invention first uses a non-linear function to map the one-dimensional distance image to a high-dimensional feature space, then obtains a projection transformation matrix through discriminant learning in the high-dimensional feature space, and then obtains feature vector of the one-dimensional distance image of any feature to be extracted based on the projection transformation matrix. The feature extraction method of the invention can well represent the non-linearity in the distribution of sample data, thereby improving the target recognition performance, overcoming the disadvantage that the existing conventional subspace method is not suitable for the non-linear data distribution, and effectively improving classification performance of radar true and false targets.

Description

technical field [0001] The invention belongs to the technical field of radar target recognition, and in particular relates to a one-dimensional distance image feature extraction method for nonlinear discrimination learning true and false targets which can be used for radar target recognition. Background technique [0002] Radar target recognition needs to extract the relevant information signs and stable features (target features) of the target from the radar echo of the target and determine its attributes. It identifies targets based on their electromagnetic backscatter. Using the characteristics of the scattered field generated by the target in the far zone of the radar, information for target identification (target information) can be obtained. The acquired target information is processed by computer and compared with the characteristics of existing targets, so as to achieve the purpose of automatic target identification. Radar target recognition includes two parts: fea...

AI Technical Summary

Problems solved by technology

[0008] However, the conventional subspace method is a linear method, which is only suitable for the case where the sample data distribution is linear. In practice, the sample data distribution may appear nonlinear, resulting in a significant decline in the recognition performance of the conventional subspace method.

There is room for further improvement in the recognition performance of existing conventional subspace methods

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