RSS
计算机科学
水准点(测量)
指纹(计算)
人工智能
样品(材料)
克里金
人工神经网络
数据挖掘
模式识别(心理学)
算法
机器学习
化学
色谱法
地理
操作系统
大地测量学
作者
Mohammad Nabati,Seyed Ali Ghorashi
标识
DOI:10.1016/j.eswa.2022.118889
摘要
In fingerprint-based positioning methods, the received signal strength (RSS) vectors from access points are measured at reference points and saved in a database. Then, this dataset is used for the training phase of a pattern recognition algorithm. Several noise types impact the signals in radio channels, and RSS values are corrupted correspondingly. These noises can be mitigated by averaging the RSS samples. In real-time applications, the users cannot wait to collect uncorrelated RSS samples to calculate their average in the online phase of the positioning process. In this paper, we propose a solution for this problem by leveraging the distribution of RSS samples in the offline phase and the preceding state of the user in the online phase. In the first step, we propose a fast and accurate positioning algorithm using a deep neural network (DNN) to learn the distribution of available RSS samples instead of averaging them at the offline phase. Then, the similarity of an online RSS sample to the RPs’ fingerprints is obtained to estimate the user’s location. Next, the proposed DNN model is combined with a novel state-based positioning method to more accurately estimate the user’s location. Extensive experiments on both benchmark and our collected datasets in two different scenarios (single RSS sample and many RSS samples for each user in the online phase) verify the superiority of the proposed algorithm compared with traditional regression algorithms such as deep neural network regression, Gaussian process regression, random forest, and weighted KNN.
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