Parameter Precise Estimation Technology of Active Segment of Non-cooperative Targets Based on Long Short-Term Memory

Abstract

Traditional algorithms do not fully utilize the timing information of non-cooperative targets, and setting too many motion parameters can lead to complex dynamic model calculations. This paper proposes a long short-term memory (LSTM) network-based method for estimating the parameters of the active segment of the non-cooperative target under single-satellite observation. Based on the simulation training set of the active segment of the non-cooperative target, the network parameters of the LSTM network are designed, the motion characteristics of the active segment of the non-cooperative target are fully excavated through data-driven methods, and the candidate cutting trajectories are screened and predicted to realize the estimation of the motion parameters of the active segment of the non-cooperative target under the condition of single-satellite observation. The experimental results show that the estimation method proposed in this paper can effectively deal with the inaccurate problem with the non-cooperative target’s active segment motion model established under the condition of single-satellite observation, obtain more accurate active segment motion parameters, and provide a feasible new idea and method for the parameter estimation of the active segment of the non-cooperative target under the single-satellite observation.