摘要： 分析了空間低軌目標(biāo)群的運(yùn)行特點(diǎn)，提出了基于時(shí)序向量相似性的空間目標(biāo)群匹配算法，提高了對(duì)低軌巨型星座的識(shí)別管理能力。首先，介紹了時(shí)序向量的降維方法，將目標(biāo)群高維觀測(cè)時(shí)序向量簡(jiǎn)化為空間構(gòu)型序列；而后，提出了基于動(dòng)態(tài)時(shí)間規(guī)整（Dynamic Time Warping，DTW）的目標(biāo)群空間構(gòu)型序列相似性判別算法；最后，利用星鏈衛(wèi)星目標(biāo)群仿真和實(shí)測(cè)數(shù)據(jù)對(duì)算法的匹配能力進(jìn)行驗(yàn)證。結(jié)果表明該算法可實(shí)現(xiàn)空間目標(biāo)群監(jiān)測(cè)數(shù)據(jù)快速匹配，仿真數(shù)據(jù)匹配過(guò)程中，在群內(nèi)目標(biāo)缺失30%的條件下匹配成功率可達(dá)100%，在低缺失條件下（缺失率5%以內(nèi)）群內(nèi)目標(biāo)識(shí)別成功率平均超過(guò)75%；實(shí)測(cè)數(shù)據(jù)匹配成功率可達(dá)100%。
Abstract： This article analyzes the motion characteristics of loworbit space target groups and proposes a space target group matching algorithm based on time series vector similarity, which improves the recognition and management ability of low orbit satellite constellations. Firstly, this article introduces the dimensionality reduction method of time series vectors, which simplifies the highdimensional observation time series vectors of the target group into spatial configuration sequences. Secondly, an algorithm based on Dynamic Time Warping (DTW) is proposed to identify the phase sequence similarity of observed data of target group. Finally, the matching ability of the algorithm is verified by the simulation and measured data of Starlink satellite target group. The results show that this algorithm can match the observed data of the space target group rapidly. During the simulation data matching process, the success rate of matching can reach 100% under the condition of 30% random missing targets within the group, and the average success rate of target recognition within the group exceeds 75% under low missing conditions (with a missing rate of less than 5%). The success rate of matching real observed data can also reach 100%.
Key words : low orbit space target group; time series vector; dynamic time warping; similarity identify
