大尺寸分布式测量网络重构关键技术研究

Research on Key Technologies for Large-Scale Distributed Measurement Network Reconstruction

大尺寸分布式测量系统是基于多观测量交会融合的测量网络,网型结构是提升网络性能的关键,对测量精度、效率甚至成本都有重要影响。为了解决遮挡物、测量对象、任务需求不断变化而产生的网络结构重构问题,首先基于网络节点的覆盖特性,研究了基于快速碰撞检测的光路遮挡判定方法,解决了测量盲点判断问题。其次,研究了基于下一最佳观测方位(NBV)思想的测量网络重构算法。同时,为了提高组网效率,利用改进的灰狼算法作为最优位置搜索算法,重新规划了网络中的节点数量和位置以实现高效组网,提升重构精度。最后,以室内空间测量定位系统(wMPS)为验证平台,通过改变不同测量条件和需求,从测量覆盖性、精度以及组网效率三个方面验证了所提方法的有效性。

The large-scale distributed measurement system is a measurement network based on multiple measurement fusion.The network structure is the key to improve the network performance,which has an important impact on the measurement accuracy,efficiency and even cost.To solve the network structure reconstruction caused by the changing of obstacles,measurement objects and requirements,based on the coverage performance of the network nodes,the method to determine the lost-of-light issue based on the fast collision detection algorithm is first investigated and the problem of blind spot judgement is solved.Then,the network reconstruction algorithm based on Next-Best-View(NBV)is studied.At the same time,in order to improve the networking efficiency,the improved grey wolf optimization algorithm is used as the best location search algorithm to rearrange the numbers and positions of nodes,and thus efficient networking is realized and the reconstruction accuracy is improved.Finally,with the workshop Measurement Positioning System(wMPS)as the verification platform and from three aspects of measurement coverage,accuracy and networking efficiency,the effectiveness of the proposed method is confirmed by changing measurement conditions and requirements.