[1]浦佳祺,陈德旺.基于最小二乘法和BP神经网络的TOA 定位算法[J].计算机技术与发展,2018,28(05):5-8.[doi:10.3969/j.issn.1673-629X.2018.05.002]
　PU Jia-qi,CHEN De-wang.A TOA Positioning Algorithm Based on Least Square Method and BP Neural Network[J].,2018,28(05):5-8.[doi:10.3969/j.issn.1673-629X.2018.05.002]
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基于最小二乘法和BP神经网络的TOA 定位算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年05期
页码:
5-8
栏目:
智能、算法、系统工程
出版日期:
2018-05-10

文章信息/Info

Title:
A TOA Positioning Algorithm Based on Least Square Method and BP Neural Network
文章编号:
1673-629X(2018)05-0005-04
作者:
浦佳祺1   2; 陈德旺1   2
1．福州大学 数学与计算机科学学院，福建 福州 350108;
2．福州大学 轨道交通研究院，福建 福州 350108
Author(s):
PU Jia-qi 1   2 ; CHEN De-wang 1   2
1.School of Mathematics and Computer Science，Fuzhou University，Fuzhou 350108，China;
2.Academy of Rail Transport，Fuzhou University，Fuzhou 350108，China
关键词:
到达时间; 最小二乘法; BP 神经网络; 交叉实验
Keywords:
TOA; least square estimation; BP neural network; cross experiment
分类号:
TP301.6
DOI:
10.3969/j.issn.1673-629X.2018.05.002
文献标志码:
A
摘要:
针对室内复杂环境而导致低精度定位的问题，提出了一种新的基于信号到达时间的 TOA 定位算法。对最小二乘法引入了线性误差分析项，并且使用 BP 神经网络的训练方法取代传统方案进行测距模型的建立，以消除对环境的过度经验依赖，提高算法针对不同环境的普适性。选取均方根误差作为性能评价指标，将该方法和具有代表性的两种算法，即传统的最小二乘法和经典的Chan 算法进行了比较，利用实测的五组场景下的总计6 000 组定位数据进行了训练与验证。实验结果表明，该算法能取得比Chan 算法与最小二乘估计法这些经典算法优异的性能，使得定位精度大大增加。并且通过交叉实验，也说明了在不同场景下该模型具有一定的通用性。
Abstract:
In view of the problem of localization with low accuracy caused by complex indoor environment，we propose a new localization algorithm based on time of arrival (TOA)．The linear error term is introduced in the least square method，and the BP neural network training is used to create the ranging model instead of the traditional scheme，so as to eliminate the over－experience dependence on the environment，improve the universality of the algorithm for different environments．In this paper，we select the root mean square error as the
performance evaluation criterion，by which we perform a comparative testing of the proposed method and the two representative algorithms，traditional least squares method and classical Chan algorithm．By testing more than 6000 records in five different environments，the results show that the improved algorithm is able to obtain the better performance than the Chan algorithm and the least squares estimation method，and enhances the localization accuracy greatly．In addition，cross－experiments show that the models in different scenarios is universal．

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更新日期/Last Update: 2018-06-26