基于截断混合高斯模型的火箭HRRP重构和飞行姿态估计OA

With the rapid development of commercial spaceflight,the demand for rocket attitude estimation based on external meas-urement data has become increasingly urgent.In this paper,an in-depth analysis of the characteristics of high-resolution range pro-file(HRRP)of rocket is first conducted.To address the issues of high noise and fluctuation in HRRP data,the HRRP of rocket is then reconstructed using a truncated mixture of Gaussians models based on a parameterized representation approach.Considering different scenarios of whether the rocket model is known,two methods:template matching and projection estimation are developed to achieve attitude estimation under both cases with and without a template library.Meanwhile,a neural network architecture is in-troduced combining multi-layer perceptrons and convolutional neural networks in different configurations for attitude estimation.Ex-perimental results based on both simulated and flight HRRP sequences demonstrate that the reconstructed HRRP exhibits high sig-nal-to-noise ratio and strong stability.By integrating multi-envelope detection,distance-based template matching,and length pro-jection,the rocket separation detection and attitude estimation using HRRP data are successfully and stably performed.The study in this paper provides a valuable reference for future work on attitude estimation of rockets using external measurements.