31,834 research outputs found
Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach
Global Navigation Satellite Systems typically perform poorly in urban
environments, where the likelihood of line-of-sight conditions between the
devices and the satellites is low, and thus alternative localization methods
are required for good accuracy. We present LocUNet: A convolutional, end-to-end
trained neural network for the localization task, able to estimate the position
of a user from the received signal strength (RSS) from a small number of Base
Stations (BSs). In the proposed method, the user to be localized simply reports
the measured RSS to a central processing unit, which may be located in the
cloud. Using estimations of pathloss radio maps of the BSs and the RSS
measurements, LocUNet can localize users with state-of-the-art accuracy and
enjoys high robustness to inaccuracies in the estimations of radio maps. The
proposed method does not require pre-sampling of new environments and is
suitable for real-time applications. Moreover, two novel datasets that allow
for numerical evaluations of RSS and ToA methods in realistic urban
environments are presented and made publicly available for the research
community. By using these datasets, we also provide a fair comparison of
state-of-the-art RSS and ToA-based methods in the dense urban scenario and show
numerically that LocUNet outperforms all the compared methods.Comment: Submitted to IEEE Transactions on Wireless Communication
RF-Based Location Using Interpolation Functions to Reduce Fingerprint Mapping
Indoor RF-based localization using fingerprint mapping requires an initial training step, which represents a time consuming process. This location methodology needs a database conformed with RSSI (Radio Signal Strength Indicator) measures from the communication transceivers taken at specific locations within the localization area. But, the real world localization environment is dynamic and it is necessary to rebuild the fingerprint database when some environmental changes are made. This paper explores the use of different interpolation functions to complete the fingerprint mapping needed to achieve the sought accuracy, thereby reducing the effort in the training step. Also, different distributions of test maps and reference points have been evaluated, showing the validity of this proposal and necessary trade-offs. Results reported show that the same or similar localization accuracy can be achieved even when only 50% of the initial fingerprint reference points are taken
PEOPLEx: PEdestrian Opportunistic Positioning LEveraging IMU, UWB, BLE and WiFi
This paper advances the field of pedestrian localization by introducing a
unifying framework for opportunistic positioning based on nonlinear factor
graph optimization. While many existing approaches assume constant availability
of one or multiple sensing signals, our methodology employs IMU-based
pedestrian inertial navigation as the backbone for sensor fusion,
opportunistically integrating Ultra-Wideband (UWB), Bluetooth Low Energy (BLE),
and WiFi signals when they are available in the environment. The proposed
PEOPLEx framework is designed to incorporate sensing data as it becomes
available, operating without any prior knowledge about the environment (e.g.
anchor locations, radio frequency maps, etc.). Our contributions are twofold:
1) we introduce an opportunistic multi-sensor and real-time pedestrian
positioning framework fusing the available sensor measurements; 2) we develop
novel factors for adaptive scaling and coarse loop closures, significantly
improving the precision of indoor positioning. Experimental validation confirms
that our approach achieves accurate localization estimates in real indoor
scenarios using commercial smartphones
Selective AP-sequence Based Indoor Localization without Site Survey
In this paper, we propose an indoor localization system employing ordered
sequence of access points (APs) based on received signal strength (RSS). Unlike
existing indoor localization systems, our approach does not require any
time-consuming and laborious site survey phase to characterize the radio
signals in the environment. To be precise, we construct the fingerprint map by
cutting the layouts of the interested area into regions with only the knowledge
of positions of APs. This can be done offline within a second and has a
potential for practical use. The localization is then achieved by matching the
ordered AP-sequence to the ones in the fingerprint map. Different from
traditional fingerprinting that employing all APs information, we use only
selected APs to perform localization, due to the fact that, without site
survey, the possibility in obtaining the correct AP sequence is lower if it
involves more APs. Experimental results show that, the proposed system achieves
localization accuracy < 5m with an accumulative density function (CDF) of 50%
to 60% depending on the density of APs. Furthermore, we observe that, using all
APs for localization might not achieve the best localization accuracy, e.g. in
our case, 4 APs out of total 7 APs achieves the best performance. In practice,
the number of APs used to perform localization should be a design parameter
based on the placement of APs.Comment: VTC2016-Spring, 15-18 May 2016, Nanjing, Chin
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