发表机构
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对标准IMM固定TPM需调参及闭环自适应失效的问题，提出参考驱动IMM（RD-IMM），经天基光学传感器评估，可检测小推力 burn、降低位置误差且虚警少。
AI 中文摘要
非合作卫星的机动检测是空间态势感知的核心任务。交互式多模型（IMM）滤波器已被用于检测未宣告的机动，但标准IMM假设存在固定的马尔可夫转移概率矩阵（TPM），该矩阵需在未知目标机动速率的情况下进行调参。为避免该调参过程，自适应TPM方法会基于IMM内部计算的统计量在线更新TPM。然而，这些统计量已通过混合步骤依赖于TPM，形成了反馈回路。在轨道跟踪的动态失配下，该回路会在无机动时持续保持高机动概率，或在机动发生时抑制其上升。本文中，TPM由参考 coast 滤波器的归一化新息平方（NIS）驱动，该参考滤波器不与IMM混合，且NIS被连续映射为 coast-to-maneuver 转移概率。参考驱动IMM（RD-IMM）通过四个天基光学传感器对地球静止轨道目标的跟踪进行评估。RD-IMM可检测到固定TPM IMM无法检测到的小推力 burn，在无机动运行中几乎无虚警，且避免了闭环自适应的两种失效情况。此外，与单滤波器方法相比，RD-IMM在小沿迹推力 burn 后将位置误差降低了50%以上。
英文摘要
Maneuver detection of non-cooperative satellites is an essential task of space situational awareness. The interacting multiple model (IMM) filter has been applied to detect unannounced maneuvers, but the standard IMM assumes a fixed Markov transition probability matrix (TPM) that must be tuned without knowledge of the maneuver rate of the target. To avoid this tuning, adaptive-TPM approaches update the TPM online from statistics computed inside the IMM. However, these statistics already depend on the TPM through the mixing step, and this dependence forms a feedback loop. Under the dynamics mismatch of orbit tracking, the loop can keep the maneuver probability high in the absence of a maneuver or suppress its rise when a maneuver occurs. In this paper, the TPM is driven by the normalized innovation squared (NIS) of a reference coast filter that is not mixed with the IMM, and the NIS is mapped continuously to the coast-to-maneuver transition probability. The reference-driven IMM (RD-IMM) is evaluated with four space-based optical sensors tracking a geosynchronous target. RD-IMM detects small burns that the fixed-TPM IMM fails to detect, with few false declarations in maneuver-free runs, and avoids both failures of the closed-loop adaptation. Moreover, RD-IMM reduces the position error after a small in-track burn by more than 50% compared with the single-filter approaches.
Comments14 pages, 6 figures
