WiFi Based Fingerprinting Positioning Based on Seq2seq Model

Abstract

<jats:p>Indoor positioning technologies are of great use in GPS-denied areas. They can be partitioned into two types of systems—infrastructure-free based and infrastructure-dependent based. WiFi based indoor positioning system is somewhere between the infrastructure-free and infrastructure-dependent systems. The reason is that in WiFi based systems, Access Points (APs) as pre-installed infrastructures are necessary. However, the APs do not need to be specially installed, because WiFi APs are already widely deployed in many indoor areas, for example, offices, malls and airports. This feature makes WiFi based indoor positioning suitable for many practical applications. In this paper, a seq2seq model based, deep learning method is proposed for WiFi based fingerprinting. The model can learn from different length of training sequences, and thus can exploit the context information for positioning. The context information denotes the information contained in the sequence, which can help finding the correspondences between RSS fingerprints and the coordinate positions. A simple example piece of context information is human walking routine (such as no sharp turns). The proposed method shows an improvement with an open source dataset, when compared against deep learning based counterpart methods.</jats:p>

Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review

“…Several papers focus on combining different algorithms in order to choose the one that provides the best results in a particular case [21][22][23], while other papers focus on processing data collected from APs [24][25][26][27]; finally, we found two papers [28,29] that relied on applying a double algorithm, one to approximate the location and another to detail it more precisely from the first approximation. In the following subsections, we look more deeply into the specifications of NN, SVM, and RF.…”

Ensemble learning model for Wifi indoor positioning systems

“…The most commonly used estimation method is the K-Nearest Neighbor (KNN). More complicated methods include support vector machine (SVM), deep neural networks (DNN), the hidden markov model (HMM), and Gaussian Process Assisted are also implemented [2,[22][23][24][25]. Now, let us formalize the methods using a mathematical model.…”

High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

“…This RNN-based research is a representative case where long-short term memory (LSTM) and gated recurrent unit (GRU) [ 41 , 42 , 43 , 44 ] are applied. These neural network-based methods are mainly studied to improve the location accuracy without distinguishing between fingerprint or TOF.…”