Velocity Estimation for Space Infrared Dim Targets Based on Multi-Satellite Observation and Robust Locally Weighted Regression

Abstract

Velocity estimation of space moving targets is a key part of space situational awareness. However, most of the existing methods do not consider the satellite observation process, and the performance mainly depends on the preset target motion state, which has great limitations. To accurately obtain the motion characteristics of space infrared dim targets in space-based infrared detection, a velocity estimation method based on multi-satellite observation and robust locally weighted regression is proposed. Firstly, according to parameters such as satellite position, satellite attitude angle, and sensor line of sight, the overall target observation model from the sensor coordinate frame to the Earth-centered inertial coordinate frame is established, and the pixel coordinates of the target imaging point are extracted using the gray-weighted centroid method. Then, combined with the least squares criterion, the position sequence of the space target is obtained. Finally, a robust locally weighted regression operation is performed on the target position sequence to estimate the velocity. This study verified the feasibility of the proposed method through simulation examples, with the results showing that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the method were only 0.0733 m/s and 1.6640 m/s without measurement error. Moreover, the velocity estimation accuracy was better than that of other methods in most scenarios. In addition, the estimation accuracy under the impact of various measurement errors was analyzed, and it was found that the pixel coordinate extraction error had the greatest impact on velocity estimation accuracy. The proposed method provides a technical basis for the recognition of space infrared dim moving targets.

A Multi-Dimensional Feature Fusion Recognition Method for Space Infrared Dim Targets Based on Fuzzy Comprehensive with Spatio-Temporal Correlation

“…The intrinsic patterns and extraction methods of micro-motion features are discussed in detail in [9,10]. The study cited in [11] used cross-positioning and robust local weighted regression (RLWR) to achieve high-precision estimation of velocity characteristics under multi-satellite observation.…”