2,308 research outputs found
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 m with of the
errors under m, which outperforms the conventional KNN algorithms and
probabilistic algorithms by approximately 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
Enhanced indoor location tracking through body shadowing compensation
This paper presents a radio frequency (RF)-based location tracking system that improves its performance by eliminating the shadowing caused by the human body of the user being tracked. The presence of such a user will influence the RF signal paths between a body-worn node and the receiving nodes. This influence will vary with the user's location and orientation and, as a result, will deteriorate the performance regarding location tracking. By using multiple mobile nodes, placed on different parts of a human body, we exploit the fact that the combination of multiple measured signal strengths will show less variation caused by the user's body. Another method is to compensate explicitly for the influence of the body by using the user's orientation toward the fixed infrastructure nodes. Both approaches can be independently combined and reduce the influence caused by body shadowing, hereby improving the tracking accuracy. The overall system performance is extensively verified on a building-wide testbed for sensor experiments. The results show a significant improvement in tracking accuracy. The total improvement in mean accuracy is 38.1% when using three mobile nodes instead of one and simultaneously compensating for the user's orientation
Advanced real-time indoor tracking based on the Viterbi algorithm and semantic data
A real-time indoor tracking system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy, that is, the environment of the object that is being tracked and a motion model. The starting point is a fingerprinting technique for which an advanced network planner is used to automatically construct the radio map, avoiding a time consuming measurement campaign. The developed algorithm was verified with simulations and with experiments in a building-wide testbed for sensor experiments, where a median accuracy below 2 m was obtained. Compared to a reference algorithm without Viterbi or semantic data, the results indicated a significant improvement: the mean accuracy and standard deviation improved by, respectively, 26.1% and 65.3%. Thereafter a sensitivity analysis was conducted to estimate the influence of node density, grid size, memory usage, and semantic data on the performance
Benchmarking of localization solutions : guidelines for the selection of evaluation points
Indoor localization solutions are key enablers for next-generation indoor navigation and track and tracing solutions. As a result, an increasing number of different localization algorithms have been proposed and evaluated in scientific literature. However, many of these publications do not accurately substantiate the used evaluation methods. In particular, many authors utilize a different number of evaluation points, but they do not (i) analyze if the number of used evaluation points is sufficient to accurately evaluate the performance of their solutions and (ii) report on the uncertainty of the published results. To remedy this, this paper evaluates the influence of the selection of evaluation points. Based on statistical parameters such as the standard error of the mean value, an estimator is defined that can be used to quantitatively analyze the impact of the number of used evaluation points on the confidence interval of the mean value of the obtained results. This estimator is used to estimate the uncertainty of the presented accuracy results, and can be used to identify if more evaluations are required. To validate the proposed estimator, two different localization algorithms are evaluated in different testbeds and using different types of technology, showing that the number of required evaluation points does indeed vary significantly depending on the evaluated solution. (C) 2017 Elsevier B.V. All rights reserved
Distributed and adaptive location identification system for mobile devices
Indoor location identification and navigation need to be as simple, seamless,
and ubiquitous as its outdoor GPS-based counterpart is. It would be of great
convenience to the mobile user to be able to continue navigating seamlessly as
he or she moves from a GPS-clear outdoor environment into an indoor environment
or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing
infrastructure-based indoor localization systems lack such capability, on top
of potentially facing several critical technical challenges such as increased
cost of installation, centralization, lack of reliability, poor localization
accuracy, poor adaptation to the dynamics of the surrounding environment,
latency, system-level and computational complexities, repetitive
labor-intensive parameter tuning, and user privacy. To this end, this paper
presents a novel mechanism with the potential to overcome most (if not all) of
the abovementioned challenges. The proposed mechanism is simple, distributed,
adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a
mobile blind device can potentially utilize, as GPS-like reference nodes,
either in-range location-aware compatible mobile devices or preinstalled
low-cost infrastructure-less location-aware beacon nodes. The proposed approach
is model-based and calibration-free that uses the received signal strength to
periodically and collaboratively measure and update the radio frequency
characteristics of the operating environment to estimate the distances to the
reference nodes. Trilateration is then used by the blind device to identify its
own location, similar to that used in the GPS-based system. Simulation and
empirical testing ascertained that the proposed approach can potentially be the
core of future indoor and GPS-obstructed environments
Inferring Person-to-person Proximity Using WiFi Signals
Today's societies are enveloped in an ever-growing telecommunication
infrastructure. This infrastructure offers important opportunities for sensing
and recording a multitude of human behaviors. Human mobility patterns are a
prominent example of such a behavior which has been studied based on cell phone
towers, Bluetooth beacons, and WiFi networks as proxies for location. However,
while mobility is an important aspect of human behavior, understanding complex
social systems requires studying not only the movement of individuals, but also
their interactions. Sensing social interactions on a large scale is a technical
challenge and many commonly used approaches---including RFID badges or
Bluetooth scanning---offer only limited scalability. Here we show that it is
possible, in a scalable and robust way, to accurately infer person-to-person
physical proximity from the lists of WiFi access points measured by smartphones
carried by the two individuals. Based on a longitudinal dataset of
approximately 800 participants with ground-truth interactions collected over a
year, we show that our model performs better than the current state-of-the-art.
Our results demonstrate the value of WiFi signals in social sensing as well as
potential threats to privacy that they imply
- …