大象群声定位优化算法的实现与验证

S. D. Correia, M. Beko, Luís A. da Silva Cruz, Slavisa Tomic
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引用次数: 9

摘要

本文提出了一种基于声波能量的定位方法,利用象群优化算法解决NP-hard优化问题。测量用于验证和调整衰减声学模型,并在室外环境设置中进行了模拟,在该环境中实现并与实际声学测量进行了比较。研究了这两种情况下的性能,就精度而言,随着模型中使用的传感器节点数量的变化。现场实施表明,结果与仿真结果一致,验证了声学模型和使用象群优化等元启发式方法解决定位问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation and Validation of Elephant Herding Optimization Algorithm for Acoustic Localization
This work presents a field implementation of acoustic energy-based positioning, using Elephant Herding Optimization algorithm to solve the NP-hard optimization problem. Measurements are used to validate and adjust the decay acoustic model, and simulations are performed with an outdoor environment setup, where the implementation is fulfilled and compared with real acoustic measurements. The performance in both scenarios is studied, in terms of accuracy, with the variation of the number of sensors nodes used in the model. The field implementation shows that results are aligned with simulation ones, validating the acoustic model and the use of metaheuristic methods such as Elephant Herding Optimization for solving the localization problem.
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