Localization is one of the most important topics of cyber physical system. In the last decades, much attention has been paid to the precise localization and performance evaluation in wireless sensor networks. Inertial-measurement-unit and Time-of-Arrival fusion is a state-of-the-art method to solve the accumulative error and drifting problem faced by sole inertial-measurement-unit positioning and navigation. Network cooperative technology could effectively suppress the accumulative error. This paper presents a spatial-temporal constrained particle filter algorithm for cooperative target tracking, so as to solve the problem of multi-target high-precision position tracking in complex and highly dynamic environments. Firstly, we propose an error-ellipse-resampling particle filter method. In the resampling stage of the particle filter, error ellipses with different confidence probabilities would be established with the use of the known estimated center and confidence scale, to achieve hierarchical resampling optimization based on the geometrical position of particles. As for cooperative tracking, an optimization method of state estimation based on spatial distance constraint is proposed, so that the Bayesian filter can benefit from spatial information and achieve cooperative tracking of spatial-temporal information fusion. Numerical experimental results show that the proposed error-ellipse-resampling particle filter could decrease the growth rate of cumulative errors and reach a positioning accuracy of 1.05 m, multi-target cooperative error-ellipse-resampling particle filter can effectively eliminate the cumulative error and achieve a positioning accuracy of 0.24 m.

本地化是网络物理系统最重要的主题之一。在过去的几十年中，已经对无线传感器网络中的精确定位和性能评估给予了极大的关注。惯性测量单元和到达时间融合是解决唯一惯性测量单元定位和导航所面临的累积误差和漂移问题的最新方法。网络协作技术可以有效地抑制累积误差。提出了一种时空约束粒子滤波的协同目标跟踪算法，以解决复杂，高动态环境下的多目标高精度位置跟踪问题。首先，我们提出了一种误差椭圆重采样粒子滤波方法。在粒子过滤器的重采样阶段，将使用已知的估计中心和置信度比例建立具有不同置信度概率的误差椭圆，以基于粒子的几何位置实现分层重采样优化。对于协同跟踪，提出了一种基于空间距离约束的状态估计优化方法，以使贝叶斯滤波器可以从空间信息中受益，实现时空信息融合的协同跟踪。数值实验结果表明，提出的误差椭圆重采样粒子滤波器可以降低累积误差的增长速度，达到1.05 m的定位精度，多目标协作误差椭圆重采样粒子滤波器可以有效消除累积误差，实现定位精度为0.24 m。