Space Precession Target Classification Based on Radar High-Resolution Range Profiles

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.

Modeling the ultra-high intensity laser pulse – cone target interaction for ion acceleration at CETAL facility

We study the interaction of an ultra-high intensity laser pulse with plastic flat-top cone targets with curved walls and cone targets with straight walls. We find the appropriate type, dimensions of the cone target, and the ultra-high intensity laser pulse parameters for which the accelerated ions have the maximum energy and their number is the highest for a lower angular divergence and a better laser absorption. This numerical study will allow one to prepare and optimize first laser-ion acceleration experiments on CETAL using micro-cone targets.