高技术通讯2018，Vol.28Issue(3)：227-232,6.DOI:10.3772/j.issn.1002-0470.2018.03.006
基于RBF神经网络的地磁车位检测优化算法
An optimization algorithm for geomagnetic parking place detection based on RBF neural networks
摘要
Abstract
Cars' geomagnetic parking place detection is studied .Considering that the baseline drift occurs under cars '' long parking , thus the traditional geomagnetic detection algorithm is prone to missed detection and misleading dur-ing long parking , an algorithm for geomagnetic parking detection based on radial basis function ( RBF) neural net-works is proposed .The algorithm compensates the baseline under long time parking condition to obtain more accu-rate baseline values to improve the detection accuracy .The experimental results show that the baseline value ob-tained by this algorithm quickly approximates the real value , and the compensation effect on the baseline drift is im-proved.After compensating the baseline by RBF neural networks , the false detection rate is reduced by 6.65%and the accuracy rate is improved by 7 .31%.关键词
地磁检测算法/基线漂移/径向基函数(RBF)/车位检测Key words
geomagnetic detection algorithm/baseline drift/radial basis function (RBF)/parking place de-tection引用本文复制引用
顾夫挺,郭海锋,何德峰,彭明洋..基于RBF神经网络的地磁车位检测优化算法[J].高技术通讯,2018,28(3):227-232,6.基金项目
国家自然科学基金(61374111),浙江省自然科学基金(LY14F030012)和浙江省教育科学规划(2016SCG241)资助项目. （61374111）
