Stacking integration algorithm-based spatial micro-motion target identification method

Abstract

The invention discloses a spatial micro-motion target recognition method based on a Stacking integration algorithm, and mainly solves the problems that the feature extraction of an existing spatial micro-motion target recognition method is single, and a traditional single classifier cannot fully mine the feature classification potential. According to the implementation scheme, the method comprises the following steps: 1) extracting target time domain, frequency domain and time-frequency domain features by using a multi-transform domain feature extraction method, and generating a training sample set and a test sample set; 2) constructing a Stacking integrated classifier model which is formed by connecting four primary classifiers in parallel and then cascading the primary classifiers with a secondary classifier; 3) training the Stacking ensemble classifier by using the training sample set and a cross validation method and 4) inputting the test sample set into the trained Stacking ensemble classifier to obtain a classification result. The method can fully represent the target characteristics, excavates the classification potential of the characteristics, improves the recognition rate of the spatial micro-motion target, and can be used for trajectory target recognition.

Description

technical field [0001] The invention belongs to the technical field of radar target recognition, in particular to a space micro-movement target recognition method, which can be used for ballistic target recognition. Background technique [0002] Micro-movement is a unique movement form of ballistic targets such as mid-section warheads and decoys. The echo of a micro-moving target usually contains important characteristics such as its shape, structure, and motion. Ground-based radar can acquire the echoes of long-distance space targets such as micro-moving ballistic targets all day and all-weather, and then extract micro-movement features from them to realize classification and recognition, that is, to realize space micro-moving target recognition. According to different feature extraction methods, the existing spatial micro-moving target recognition methods can be divided into two categories: classification and recognition methods based on radar target cross-sectional area ...

AI Technical Summary

Problems solved by technology

Although this method combines RCS features and micro-Doppler features to characterize target characteristics, the extracted features are few, and it is difficult to accurately and fully describe the characteristics of micro-movement targets. When the target micro-motion forms are similar, the recognition accuracy is low

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