Reconstruction and Flight Attitude Estimation of Rocket HRRP Based on Truncated Mixture of Gaussians Model

With the rapid development of commercial spaceflight, the demand for rocket attitude estimation based on external measurement data has become increasingly urgent. In this paper, an in-depth analysis of the characteristics of high-resolution range profile (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 introduced combining multi-layer perceptrons and convolutional neural networks in different configurations for attitude estimation. Experimental results based on both simulated and flight HRRP sequences demonstrate that the reconstructed HRRP exhibits high signal-to-noise ratio and strong stability. By integrating multi-envelope detection, distance-based template matching, and length projection, 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.