电子测量与仪器学报

夏斌,张立晔.基于麻雀搜索的协同定位算法研究[J].电子测量与仪器学报,2024,38(3):152-158

基于麻雀搜索的协同定位算法研究

Research on cooperative localization algorithm based on sparrow search

DOI：

英文关键词:sparrow search cooperative localization fitness function positioning accuracy

基金项目:国家自然科学基金（62001272）项目资助

作者	单位
夏斌	山东理工大学电气与电子工程学院淄博255000
张立晔	山东理工大学计算机科学与技术学院淄博255000

Author	Institution
Xia Bin	School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China
Zhang Liye	School of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China

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中文摘要:

无线传感器网络的定位问题可以转化为适应度函数最优问题，并通过经典的麻雀搜索算法进行求解。然而该算法所用的适应度函数并未使用未知节点之间的测量距离数据，从而导致定位精度的提升有限。为了解决这一问题，提出了一种基于麻雀搜索的协同定位算法。该算法主要包括两个搜索阶段：粗略搜索和精细搜索。在粗略搜索阶段，未知节点到锚节点之间的测量距离数据被用于确定未知节点的初始位置。在精细搜索阶段，未知节点之间的测量距离数据被用来确定未知节点的精确位置。首先，采用Cat混沌映射方法来保证初始种群的均匀分布，从而有助于确定最佳位置。其次，构建了两种不同的适应度函数，一种用于粗略搜索，另一种用于精细搜索。其中，用于精细搜索的适应度函数利用未知节点之间的测量距离数据来提高定位精度。最后，提出了一种新的精细搜索方法，以避免协同定位结果收敛到局部最优位置。通过对仿真和实测数据进行分析，验证了所提方法的有效性。

英文摘要:

The localization problem of wireless sensor network can be transformed into a fitness function optimization problem, which is solved by the classical sparrow search algorithm. However, the fitness function used in this algorithm does not use measured distance data between unknown nodes, resulting in limited improvement in positioning accuracy. To address this issue, a cooperative localization algorithm based on sparrow search is proposed. This algorithm mainly includes two search stages: rough search and fine search. In the rough search stage, the measured distance data between the unknown node and the anchor node is used to determine the initial position of the unknown node. In the fine search stage, the measured distance data between unknown nodes is used to determine the precise position of the unknown node. Firstly, the Cat chaotic mapping method is used to ensure the uniform distribution of the initial population, which helps to determine the optimal location. Secondly, two different fitness functions are constructed, one for rough search and the other for fine search. Among them, the fitness function used for fine search utilizes the measured distance data between unknown nodes to improve positioning accuracy. Finally, a new fine search method is proposed to avoid the convergence of cooperative localization results to the local optimal position. The effectiveness of the proposed method is verified through analysis of simulation and measured data.

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