收稿日期: 2019-12-16
网络出版日期: 2020-08-06
基金资助
国家重点研发计划资助项目(2019YFB2101602)
Time difference of arrival localization based on an improved salp swarm algorithm
Received date: 2019-12-16
Online published: 2020-08-06
针对室内到达时间差(time difference of arrival, TDOA)位置估计中的非线性最优化问题, 提出用改进的樽海鞘群算法搜索目标位置. 通过选择最优主基站构造改进的适应度函数, 使适应度函数可以更好地反映解的优劣程度, 提高了搜索精度. 在初始樽海鞘种群中引入近似解, 使全局搜索的步骤得到简化, 加快了算法前期收敛速度. 采用自适应跟随策略更新追随者位置, 解决局部开发低效问题, 加快了算法后期收敛速度. 仿真结果表明, 基于改进樽海鞘群算法的 TDOA 定位技术相比其他元启发式算法具有更高的定位精度和更快的收敛速度.
马一鸣, 石志东, 赵康, 贡常磊, 单联海 . 基于改进樽海鞘群算法的到达时间差定位[J]. 上海大学学报(自然科学版), 2022 , 28(2) : 238 -249 . DOI: 10.12066/j.issn.1007-2861.2237
To address the nonlinear optimization problem of indoor time difference of arrival (TDOA) location estimation, an improved salp swarm algorithm (SSA) is proposed to search target locations. An improved fitness function is constructed by selecting the optimal master base station so that the fitness function can better reflect the quality of the solution, thereby enhancing search accuracy. The approximate solution is introduced into an initial salp population to simplify global exploration, and the convergence speed of the algorithm is accelerated in the early stage. An adaptive following strategy is used to update follower locations to solve the problem of low efficiency in local exploitation, which accelerates the algorithm convergence speed in the later stage. Simulation results show that the TDOA localization technology based on the improved SSA has higher localization accuracy and faster convergence speed than other meta-heuristic algorithms.
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