基于改进樽海鞘群算法的到达时间差定位

doi:10.12066/j.issn.1007-2861.2237

Abstract

Abstract:

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.

Key words: indoor localization, time difference of arrival (TDOA), salp swarm algorithm (SSA), meta-heuristic algorithm

CLC Number:

TP391.9

MA Yiming, SHI Zhidong, ZHAO Kang, GONG Changlei, SHAN Lianhai. Time difference of arrival localization based on an improved salp swarm algorithm[J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(2): 238-249.

Figures/Tables 6

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References 23

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Time difference of arrival localization based on an improved salp swarm algorithm

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