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首页> 外文期刊>International Journal of Computational Intelligence and Applications >FUZZY LOGIC AND NEURAL NETWORK BASED INDOOR FINGERPRINT POSITIONING ALGORITHMS IN WiFi
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FUZZY LOGIC AND NEURAL NETWORK BASED INDOOR FINGERPRINT POSITIONING ALGORITHMS IN WiFi

机译：WiFi中基于模糊逻辑和神经网络的室内指纹定位算法

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摘要

In this paper, a method that uses RBF-BP neural network to generate fingerprint database (FD) is proposed to improve the quality. The reference points in the database present regular tetrahedron distribution in three-dimensional space. To improve the accuracy of selecting which points are considered to locate currently and estimating their weights, two positioning algorithms based on signal strength difference values (SSDV) are proposed through analyzing the characteristic of difference values between mobile receiver and reference points. The first one is fuzzy logic algorithm (FLA). It uses different fuzzy logic models to calculate the weights of considered points. The second one is RBF-BP neural network algorithm (NNA). It uses different neural network models to estimate the spatial distances between mobile receiver and reference points. The points which have small sum of distances are considered. Their weights are calculated by a newly proposed method. The proposed algorithms use more than one weight to describe the distance to one considered point, which is more accurate. The test results demonstrate the improvement and effectiveness of proposed methods by comparing with other existing methods.
机译：为了提高质量，本文提出了一种利用RBF-BP神经网络生成指纹数据库的方法。数据库中的参考点在三维空间中呈现规则的四面体分布。为了提高选择哪些点被认为当前位置并估计其权重的准确性，通过分析移动接收机和参考点之间的差值特征，提出了两种基于信号强度差值（SSDV）的定位算法。第一个是模糊逻辑算法（FLA）。它使用不同的模糊逻辑模型来计算考虑点的权重。第二种是RBF-BP神经网络算法（NNA）。它使用不同的神经网络模型来估计移动接收器和参考点之间的空间距离。考虑距离总和较小的点。它们的权重是通过新提出的方法计算的。所提出的算法使用多个权重来描述到一个所考虑点的距离，这更加准确。测试结果通过与其他现有方法进行比较证明了所提出方法的改进和有效性。

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