无人机集群混合合作定位高效节点筛选算法
2025,47(1):198-206
张灿
国防科技大学 系统工程学院， 湖南 长沙 410073
李群
国防科技大学 系统工程学院， 湖南 长沙 410073
伦伟成
国防科技大学 系统工程学院， 湖南 长沙 410073
何永明
国防科技大学 系统工程学院， 湖南 长沙 410073
郭冬
国防科技大学 系统工程学院， 湖南 长沙 410073
国防科技大学 系统工程学院， 湖南 长沙 410073
李群
国防科技大学 系统工程学院， 湖南 长沙 410073
伦伟成
国防科技大学 系统工程学院， 湖南 长沙 410073
何永明
国防科技大学 系统工程学院， 湖南 长沙 410073
郭冬
国防科技大学 系统工程学院， 湖南 长沙 410073
摘要：
为解决无人机集群混合合作定位中高精度、高实时性的节点筛选问题，提出了一种高效节点筛选算法。该算法将定位方差作为评估定位精度的依据，利用减少一个节点导致定位方差变化的递推关系，筛选出指定数目的定位节点参与定位解算。与此同时，该算法增加了运算量调节因子，解决了传统节点筛选算法在较多节点数情况下计算量大、实时性差的问题。通过仿真实验，将该算法得到的方差和运算耗时与其他优化算法进行了实验对比，实验结果表明，该算法在大大减小计算量的同时保证了定位精度，具有良好的实时性和可行性。
为解决无人机集群混合合作定位中高精度、高实时性的节点筛选问题，提出了一种高效节点筛选算法。该算法将定位方差作为评估定位精度的依据，利用减少一个节点导致定位方差变化的递推关系，筛选出指定数目的定位节点参与定位解算。与此同时，该算法增加了运算量调节因子，解决了传统节点筛选算法在较多节点数情况下计算量大、实时性差的问题。通过仿真实验，将该算法得到的方差和运算耗时与其他优化算法进行了实验对比，实验结果表明，该算法在大大减小计算量的同时保证了定位精度，具有良好的实时性和可行性。
基金项目：
国家自然科学基金青年基金资助项目（72201273）
国家自然科学基金青年基金资助项目（72201273）
Efficient node screen algorithm for hybrid cooperative location of UAV swarm
ZHANG Can
College of Systems Engineering, National University of Defense Technology, Changsha 410073 , China
LI Qun
College of Systems Engineering, National University of Defense Technology, Changsha 410073 , China
LUN Weicheng
College of Systems Engineering, National University of Defense Technology, Changsha 410073 , China
HE Yongming
College of Systems Engineering, National University of Defense Technology, Changsha 410073 , China
GUO Dong
College of Systems Engineering, National University of Defense Technology, Changsha 410073 , China
College of Systems Engineering, National University of Defense Technology, Changsha 410073 , China
LI Qun
College of Systems Engineering, National University of Defense Technology, Changsha 410073 , China
LUN Weicheng
College of Systems Engineering, National University of Defense Technology, Changsha 410073 , China
HE Yongming
College of Systems Engineering, National University of Defense Technology, Changsha 410073 , China
GUO Dong
College of Systems Engineering, National University of Defense Technology, Changsha 410073 , China
Abstract：
In order to solve the problem of node screen with high precision and high real-time in the hybrid cooperative location of UAV swarm, an efficient node screen algorithm was proposed. The algorithm used the variance of positioning as the basis for evaluating positioning accuracy and used the recursive relationship between the variance of positioning caused by the removal of one node to select the specified number of nodes for positioning calculation. At the same time, the algorithm added an operation volume adjustment factor to solve the problem of large computation volume and poor real-time performance of traditional node selection algorithms in the case of a large number of nodes. Through simulation experiment, the variance and operation time consumption obtained by the algorithm are compared with the corresponding results of other optimization methods. The experimental results show that the algorithm greatly reduces the amount of calculation while ensuring the positioning accuracy, and has good real-time performance and feasibility.
In order to solve the problem of node screen with high precision and high real-time in the hybrid cooperative location of UAV swarm, an efficient node screen algorithm was proposed. The algorithm used the variance of positioning as the basis for evaluating positioning accuracy and used the recursive relationship between the variance of positioning caused by the removal of one node to select the specified number of nodes for positioning calculation. At the same time, the algorithm added an operation volume adjustment factor to solve the problem of large computation volume and poor real-time performance of traditional node selection algorithms in the case of a large number of nodes. Through simulation experiment, the variance and operation time consumption obtained by the algorithm are compared with the corresponding results of other optimization methods. The experimental results show that the algorithm greatly reduces the amount of calculation while ensuring the positioning accuracy, and has good real-time performance and feasibility.
收稿日期：
2023-03-30
2023-03-30
