Motion estimation is an effective way to rectify the distortion caused by motion existed in captured point clouds of space targets. However, the non-cooperative and motion complexity of instability space targets make it difficult to precisely estimate the motion parameters, especially in the linear measurement system with a single sensor. In order to solve this problem, we present a novel motion representation for motion estimation of instability space targets. The motion representation, called the transmission model, converts a nonlinear system of motion parameters into a polynomial system of orthogonal matrices, which improve the efficiency of motion estimation. Moreover, we exploit a self-constrained form to express the target orthogonal matrices, which makes them be further solved efficiently by transferring the constrained optimization problem into a self-constrained one. Additionally, an effective strategy is devised to solve the polynomial system of orthogonal matrices by progressively increasing the consecutive number of point clouds until the precision is attained. Experimental results have demonstrated that our approach achieves a high estimation accuracy and a good performance of reconstruction within few seconds under a variety of motion states in our research background.

运动估计是纠正空间目标的捕获点云中存在的运动引起的失真的有效方法。然而，不稳定空间目标的非合作和运动复杂性使得难以精确估计运动参数，尤其是在具有单个传感器的线性测量系统中。为了解决这个问题，我们提出了一种新颖的运动表示形式，用于不稳定性空间目标的运动估计。运动表示，称为传输模型，将运动参数的非线性系统转换为正交矩阵的多项式系统，从而提高了运动估计的效率。此外，我们利用自约束形式来表达目标正交矩阵，这可以通过将约束优化问题转换为自约束矩阵来进一步有效地解决。此外，通过逐步增加点云的连续数量直至达到精度，设计了一种有效的策略来解决正交矩阵的多项式系统。实验结果表明，在我们的研究背景下，在各种运动状态下，我们的方法都能在几秒钟内获得较高的估计精度和良好的重建性能。