基于融合集成学习的鲁棒近超声室内定位方法  

A Near-Ultrasonic Robust Indoor Localization Method Based on Stacking Ensemble Learning

作　　者：贾乃征 薛灿[1] 杨骝 王智[1] Jia Naizheng;Xue Can;Yang Liu;Wang Zhi(College of Control Science and Engineering,Zhejiang University,Hangzhou 310013)

机构地区：[1]浙江大学控制科学与工程学院,杭州310013

出　　处：《计算机研究与发展》2025年第2期488-502,共15页Journal of Computer Research and Development

基　　金：国家重点研发计划项目(2021YFB3900800)。

摘　　要：近年来随着经济的发展,室内定位系统的需求越来越迫切.传统的室内定位系统如WIFI定位和蓝牙定位面临着定位精度低、易受非视距(non-line-of-sight,NLOS)和噪声干扰等挑战.针对这些问题,提出了一种基于融合集成学习的近超声室内定位方法.首先,使用优化的增强互相关方法有效地抵消多径干扰.与传统基于峰值提取或固定阈值的方法相比,此法在混响环境中明显提升了测距的精度.然后,利用到达时间差(time difference of arrival,TDOA)作为特征进行提取.最终,采用了融合集成学习模型,对设定好的训练集进行交叉融合训练,并输入特征,从而得到修正的定位结果.仿真和实验测试结果表明,所提出的方法可以在室内NLOS和噪声干扰的情况下克服较大误差实现精确定位,并且精度优于对比方法50%~90%.本文核心数据公布在https://github.com/ChirsJia/JSJYF上.Recent economic advancements have significantly supported the popularity of indoor positioning systems(IPS)and indoor localization-based services(ILBS).This trend is particularly obvious as global navigation satellite systems(GNSS)are ineffective in indoor environments.Traditional IPS,such as WIFI and Bluetooth positioning,face challenges like low accuracy and are prone to non-line-of-sight(NLOS)and noise interference.In response to this issue,we propose a novel near-ultrasonic robust indoor localization method based on the stacking ensemble model.Initially,the method employs an optimized enhanced cross-correlation technique to effectively mitigate multipath interference in acoustic ranging.Compared with the conventional methods based on peak extraction or fixed thresholding,this approach significantly improves ranging accuracy in reverberant environments.Subsequently,time difference of arrival(TDOA)is extracted as a feature.Finally,we utilize a stacking ensemble learning model,incorporating optimized machine learning models,to train a pre-set dataset.This method,integrating the extracted feature,enables to achieve correct localization results in NLOS and large ranging error.Numerical simulations,raytracing acoustic analyses,and empirical validations suggest that our approach notably mitigates errors prevalent in NLOS and acoustically noisy indoor environments and yielding localization accuracy significantly exceeds current methods by 50%−90%.The core dataset available at https://github.com/ChirsJia/JSJYF.

关 键 词：室内定位 近超声定位 信号处理 机器学习 到达时间差 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TN911.23[自动化与计算机技术—控制科学与工程]