Abstract
Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.
Highlights
In recent years, space activities have been expanded with the growing emphasis on space utilization by countries around the world [1]
The main features used by classification methods that have been widely studied and applied for space targets include radar high-resolution range profiles (HRRPs) [4, 7, 8], micromotion features [9, 10], ISAR images [6], RCS features [11], and polarization features [12]
The first method we present is to extract a kind of commonly used predefined feature called histograms of oriented gradient (HOG) feature and input it into a multiclass support vector machine (SVM)
Summary
Space activities have been expanded with the growing emphasis on space utilization by countries around the world [1]. The micro-Doppler (m-D) analysis of echo signal could be used for micromotion parameter inversion, providing important feature information for target classification. Beom-Seok et al [14] extracted eight statistical and geometrical features from decomposed waveforms by empirical-mode decomposition (EMD), showing encouraging accuracy performance for International Journal of Antennas and Propagation mini-UAV classification. Applying these handcrafted features has achieved good results in the field of micromotion target classification.
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