Recurrent Neural Networks For Accurate RSSI Indoor Localization

This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating 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 relation among the received signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a weighted average filter is proposed for both input RSSI data and sequential output locations to enhance the accuracy among the temporal fluctuations of RSSI. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) 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 $1$ m, which outperforms the conventional KNN algorithms and probabilistic algorithms by approximately $30\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, recurrent neuron network (RNN), long shortterm memory (LSTM), fingerprint-based localizatio

Indoor localization methods based on Wi-Fi lateration and signal strength data collection

The paper describes two Wi-Fi lateration methods based on signal propagation model and signal strength data collection for indoor localization using Android-based mobile device. The considered methods use log-normal path loss model for signal propagation and received signal strength measurement collection for distance estimation and lateration approach for localization. The indoor signal propagation problem is resolved by received signal strength measuring and special ring radio map building those improve localization accuracy. Indoor localization technique opens possibilities for development various intelligent systems that provide the user location-based information inside buildings

Improved trilateration for indoor localization: Neural network and centroid-based approach

[EN] Location awareness is the key to success to many location-based services applications such as indoor navigation, elderly tracking, emergency management, and so on. Trilateration-based localization using received signal strength measurements is widely used in wireless sensor network-based localization and tracking systems due to its simplicity and low computational cost. However, localization accuracy obtained with the trilateration technique is generally very poor because of fluctuating nature of received signal strength measurements. The reason behind such notorious behavior of received signal strength is dynamicity in target motion and surrounding environment. In addition, the significant localization error is induced during each iteration step during trilateration, which gets propagated in the next iterations. To address this problem, this article presents an improved trilateration-based architecture named Trilateration Centroid Generalized Regression Neural Network. The proposed Trilateration Centroid Generalized Regression Neural Network-based localization algorithm inherits the simplicity and efficiency of three concepts namely trilateration, centroid, and Generalized Regression Neural Network. The extensive simulation results indicate that the proposed Trilateration Centroid Generalized Regression Neural Network algorithm demonstrates superior localization performance as compared to trilateration, and Generalized Regression Neural Network algorithm.Jondhale, SR.; Jondhale, AS.; Deshpande, PS.; Lloret, J. (2021). Improved trilateration for indoor localization: Neural network and centroid-based approach. International Journal of Distributed Sensor Networks (Online). 17(11):1-14. https://doi.org/10.1177/15501477211053997114171

Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks

The paper introduces a method which improves localization accuracy of the signal strength fingerprinting approach. According to the proposed method, entire localization area is divided into regions by clustering the fingerprint database. For each region a prototype of the received signal strength is determined and a dedicated artificial neural network (ANN) is trained by using only those fingerprints that belong to this region (cluster). Final estimation of the location is obtained by fusion of the coordinates delivered by selected ANNs. Sensor nodes have to store only the signal strength prototypes and synaptic weights of the ANNs in order to estimate their locations. This approach significantly reduces the amount of memory required to store a received signal strength map. Various ANN topologies were considered in this study. Improvement of the localization accuracy as well as speed-up of learning process was achieved by employing fully connected neural networks. The proposed method was verified and compared against state-of-the-art localization approaches in realworld indoor environment by using both stationary andmobile sensor nodes

A comparison between vector algorithm and CRSS algorithm for indoor localization

yesIn this paper a comparison between two indoor localization algorithms using received signal strength is utilized the vector algorithm and the Comparative Received Signal Strength algorithm. The comparison considered the effect of the radio map resolution, the number of access points, and the operating frequency on the accuracy of the localization process. The experiments were carried out using ray tracing software, measured values and MATLAB

Using Robots and SLAM for Indoor Wi-Fi Mapping in Indoor Geolocation

In the past navigation was mainly done for vehicles using GPS but since there has been an increase interest in Wi-Fi Localization indoor environments. This kind of localization using RSS (Received Signal Strength) to calculate position. This kind of unobtrusive accessing can be done in places outside of one’s home and will guarantee that the localization can be done anywhere there is a signal even places where GPS cannot. In this project we want to compare the performance of a human collected database and a robot collected database for indoor localization systems with other commercially available systems (such as Wi-Fi Compass, Google Maps, etc.) and determining whether a human can be replaced with a robot for this kind of data collection