Recurrent Neural Networks for Accurate RSSI Indoor Localization

Abstract

This paper proposes recurrent neural networks (RNNs) for WiFi fingerprinting indoor localization. Instead of locating a mobile user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the correlation among the received signal strength indicator (RSSI) measurements in a trajectory. To enhance the accuracy among the temporal fluctuations of RSSI, a weighted average filter is proposed for both input RSSI data and sequential output locations. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional RNN (BiRNN), bidirectional LSTM (BiLSTM) and bidirectional GRU (BiGRU) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of 0.75 m with 80% of the errors under one meter, which outperforms KNN algorithms and probabilistic algorithms by approximately 30% under the same test environment.Index Terms-Received signal strength indicator, WiFi indoor localization, recurrent neuron network, long short-term memory, fingerprint-based localization.

Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach

“…Leveraging modern machine learning frameworks such as discriminant-adaptive neural network [26], robust extreme learning machines [27], and multi-layer neural networks [28], RSSI fingerprinting-based indoor localization methods showed improved localization performance over classical machine learning approaches. More recently, [29] proposed to apply recurrent neural networks (RNNs) to RSSI measurements for utilizing trajectory information.…”

“…Given its ubiquitous presence, WiFi stands out as a technology for infrastructure-free indoor localization. Most WiFi-based indoor localization frameworks use either fine-grained channel state information (CSI) from the physical layer [3]- [12] or coarse-grained RSSI measurements from the MAC layer [13]- [29] for fingerprinting or direct localization; see more detailed literature review in the next section.…”

An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning

“…Moreover, support vector machine (SVM) [43], artificial neural network (ANN) [44,45] and deep learning [46] have been proposed to aid RSSI purification and high positioning accuracy. However, in indoor environments, all of the above algorithms still face challenges of spatial ambiguity, RSSI instability, and RSSI's short collecting time per location [47,48].…”

Semi-Sequential Probabilistic Model for Indoor Localization Enhancement

“…In the literature, the idea of exploiting the measurements in previous time steps to locate the current location was adopted in the research of recurrent neural network (RNN) [30], Kalman filter [31]- [34] and soft range limited K-nearest neighbors (SRL-KNN) [29]. In Ref.…”