An intention inference method for the space non-cooperative target based on BiGRU-Self Attention

Abstract

Intention inference for space non-cooperative targets is the key to space situational awareness and assistant decision for collision avoidance. Given that the problem of target intention inference is essential to learn the dynamically changing time-series characteristics of space non-cooperative target intentions and infer their relative motion patterns for threat warning, this paper adopts a deep learning-based approach, introduces a bidirectional propagation mechanism and self-attention mechanism based on Gated Recurrent Unit (GRU) and proposes a bidirectional Gated Recurrent Unit (BiGRU)-Self Attention-based space non-cooperative target intention inference model. BiGRU is used to learn deep information in time-series characteristics of the space non-cooperative target, and self-attention mechanism is used to adaptively extract and assign weights to key characteristics to capture the internal correlations in time-series information, thus improving model performance. The line-of-sight measurements are used as the characteristics of target intention inference, and the typical target motion intentions are defined. Subsequently, the proposed model is trained and tested on the test set, with the accuracy reaching 97.1%. Besides, the effectiveness and advantages of the proposed model are verified by the simulation of a case study and comparison evaluations. The results demonstrate that our proposed model could significantly improve the accuracy, computational efficiency, and noise resistance for the space non-cooperative target intention inference compared with the existing intention inference models.