粒子群优化
颗粒过滤器
初始化
萤火虫算法
控制理论(社会学)
萤火虫协议
蒙特卡罗局部化
粒子(生态学)
辅助粒子过滤器
滤波器(信号处理)
精密点定位
计算机科学
算法
数学优化
卡尔曼滤波器
全球导航卫星系统应用
数学
扩展卡尔曼滤波器
集合卡尔曼滤波器
人工智能
全球定位系统
计算机视觉
海洋学
动物
地质学
生物
程序设计语言
电信
控制(管理)
作者
Ling‐Feng Shi,Miaoxin Yu,Wei Yin
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:71: 1-9
被引量:3
标识
DOI:10.1109/tim.2021.3128706
摘要
Particle filter is commonly used in various indoor positioning schemes, but sample impoverishment and weight degradation generally exist in particle filter. To solve this problem, this article uses the firefly algorithm to optimize particle filter. Using the global optimal value in the particle swarm to guide the remaining particles to update their positions, the particles tend to move to the high likelihood area, which can more accurately describe the true state of the target observation. At the same time, to avoid particles falling into local optimum and oscillating repeatedly at the extreme point, the mathematical model of the firefly algorithm is reconstructed in this article, which is named as adaptive optimization firefly algorithm (AOFA). The experimental results show that the positioning scheme can provide the positioning accuracy with an average error of less than 0.5 m. Compared with the traditional particle filter, the positioning accuracy is improved by 120%.
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