A Robust and Accurate Indoor Localization Using Learning-Based Fusion of Wi-Fi RTT and RSSI

Abstract

Great attention has been paid to indoor localization due to its wide range of associated applications and services. Fingerprinting and time-based localization techniques are among the most popular approaches in the field due to their promising performance. However, fingerprinting techniques usually suffer from signal fluctuations and interference, which yields unstable localization performance. On the other hand, the accuracy of time-based techniques is highly affected by multipath propagation errors and non-line-of-sight transmissions. To combat these challenges, this paper presents a hybrid deep-learning-based indoor localization system called RRLoc which fuses fingerprinting and time-based techniques with a view of combining their advantages. RRLoc leverages a novel approach for fusing received signal strength indication (RSSI) and round-trip time (RTT) measurements and extracting high-level features using deep canonical correlation analysis. The extracted features are then used in training a localization model for facilitating the location estimation process. Different modules are incorporated to improve the deep model’s generalization against overtraining and noise. The experimental results obtained at two different indoor environments show that RRLoc improves localization accuracy by at least 267% and 496% compared to the state-of-the-art fingerprinting and ranging-based-multilateration techniques, respectively.

Indoor location strategy based on the fusion of RSSI and RTT based on multilayer perceptron

“…To address the issue that the location accuracy of the algorithm for matching location fingerprints build on MLP proposed in the previous section is not high enough, this section proposes an algorithm for matching location fingerprints build on the fusion of RSSI and RTT build on MLP [9]. Compared to the approach discussed in the preceding section, the algorithm adds RTT (round-trip time) as the second eigenvalue, and learns the relationship between the two feature sequences and the distance from the point to each AP point through the neural network.…”

Evaluation of a High-Accuracy Indoor-Positioning System with Wi-Fi Time of Flight (ToF) and Deep Learning

“…Rizk et al [37] presents an indoor-positioning system with Wi-Fi RTT and RSSI. To solve the problem of signal fuctuations, interference from fngerprinting, multipath propagation errors, and NLOS transmissions, the proposed system achieved a localization error of 0.51 m and 0.59 m, respectively, for ofce and lab environments.…”

A Quantum Algorithm for RF-based Fingerprinting Localization Systems

“…The classical fingerprinting localization systems depend on building a radio map for the environment by collecting the RSS signature of heard RPs at different locations in the area of interest during the offline phase [4], [9], [48]. In the online phase, these systems determine the location of the user by comparing the user's current online RSS measurements to the fingerprint [1], [6], [31], [32], [35]. This comparison is done using deterministic or probabilistic techniques [33], [48].…”