1,179 research outputs found
Massive MIMO-based Localization and Mapping Exploiting Phase Information of Multipath Components
In this paper, we present a robust multipath-based localization and mapping
framework that exploits the phases of specular multipath components (MPCs)
using a massive multiple-input multiple-output (MIMO) array at the base
station. Utilizing the phase information related to the propagation distances
of the MPCs enables the possibility of localization with extraordinary accuracy
even with limited bandwidth. The specular MPC parameters along with the
parameters of the noise and the dense multipath component (DMC) are tracked
using an extended Kalman filter (EKF), which enables to preserve the
distance-related phase changes of the MPC complex amplitudes. The DMC comprises
all non-resolvable MPCs, which occur due to finite measurement aperture. The
estimation of the DMC parameters enhances the estimation quality of the
specular MPCs and therefore also the quality of localization and mapping. The
estimated MPC propagation distances are subsequently used as input to a
distance-based localization and mapping algorithm. This algorithm does not need
prior knowledge about the surrounding environment and base station position.
The performance is demonstrated with real radio-channel measurements using an
antenna array with 128 ports at the base station side and a standard cellular
signal bandwidth of 40 MHz. The results show that high accuracy localization is
possible even with such a low bandwidth.Comment: 14 pages (two columns), 13 figures. This work has been submitted to
the IEEE Transaction on Wireless Communications for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
A Deep Learning Approach for Generating Soft Range Information from RF Data
Radio frequency (RF)-based techniques are widely
adopted for indoor localization despite the challenges in extracting
sufficient information from measurements. Soft range
information (SRI) offers a promising alternative for highly
accurate localization that gives all probable range values rather
than a single estimate of distance. We propose a deep learning
approach to generate accurate SRI from RF measurements. In
particular, the proposed approach is implemented by a network
with two neural modules and conducts the generation directly
from raw data. Extensive experiments on a case study with
two public datasets are conducted to quantify the efficiency
in different indoor localization tasks. The results show that
the proposed approach can generate highly accurate SRI, and
significantly outperforms conventional techniques in both nonline-of-sight (NLOS) detection and ranging error mitigation.Ramon y Cajal Grant RYC-2016-1938
Nonlinear Deterministic Observer for Inertial Navigation using Ultra-wideband and IMU Sensor Fusion
Navigation in Global Positioning Systems (GPS)-denied environments requires
robust estimators reliant on fusion of inertial sensors able to estimate
rigid-body's orientation, position, and linear velocity. Ultra-wideband (UWB)
and Inertial Measurement Unit (IMU) represent low-cost measurement technology
that can be utilized for successful Inertial Navigation. This paper presents a
nonlinear deterministic navigation observer in a continuous form that directly
employs UWB and IMU measurements. The estimator is developed on the extended
Special Euclidean Group and ensures exponential
convergence of the closed loop error signals starting from almost any initial
condition. The discrete version of the proposed observer is tested using a
publicly available real-world dataset of a drone flight. Keywords:
Ultra-wideband, Inertial measurement unit, Sensor Fusion, Positioning system,
GPS-denied navigation.Comment: 2023 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS
Detection of UWB ranging measurement quality for collaborative indoor positioning
Wireless communication signals have become popular alternatives for indoor positioning and navigation due to lack of navigation satellite signals in such environments. The signal characteristics determine the method used for positioning as well as the positioning accuracy. Ultra-wideband (UWB) signals, with a typical bandwidth of over 1 GHz, overcome multipath problems in complicated environments. Hence, potentially achieves centimetre-level ranging accuracy in open areas. However, signals can be disrupted when placed in environments with obstructions and cause large ranging errors. This paper proposes a ranging measurement quality indicator (RQI) which detects the UWB measurement quality based on the received signal strength pattern. With a detection validity of more than 83%, the RQI is then implemented in a ranging-based collaborative positioning system. The relative constraint of the collaborative network is adjusted adaptively according to the detected RQI. The proposed detection and positioning algorithm improves positioning accuracy by 80% compared to non-adaptive collaborative positioning
Novel Fine-Tuned Attribute Weighted Na\"ive Bayes NLoS Classifier for UWB Positioning
In this paper, we propose a novel Fine-Tuned attribute Weighted Na\"ive Bayes
(FT-WNB) classifier to identify the Line-of-Sight (LoS) and Non-Line-of-Sight
(NLoS) for UltraWide Bandwidth (UWB) signals in an Indoor Positioning System
(IPS). The FT-WNB classifier assigns each signal feature a specific weight and
fine-tunes its probabilities to address the mismatch between the predicted and
actual class. The performance of the FT-WNB classifier is compared with the
state-of-the-art Machine Learning (ML) classifiers such as minimum Redundancy
Maximum Relevance (mRMR)- -Nearest Neighbour (KNN), Support Vector Machine
(SVM), Decision Tree (DT), Na\"ive Bayes (NB), and Neural Network (NN). It is
demonstrated that the proposed classifier outperforms other algorithms by
achieving a high NLoS classification accuracy of with imbalanced data
and with balanced data. The experimental results indicate that our
proposed FT-WNB classifier significantly outperforms the existing
state-of-the-art ML methods for LoS and NLoS signals in IPS in the considered
scenario
Ultra-low-power Range Error Mitigation for Ultra-wideband Precise Localization
Precise and accurate localization in outdoor and indoor environments is a
challenging problem that currently constitutes a significant limitation for
several practical applications. Ultra-wideband (UWB) localization technology
represents a valuable low-cost solution to the problem. However,
non-line-of-sight (NLOS) conditions and complexity of the specific radio
environment can easily introduce a positive bias in the ranging measurement,
resulting in highly inaccurate and unsatisfactory position estimation. In the
light of this, we leverage the latest advancement in deep neural network
optimization techniques and their implementation on ultra-low-power
microcontrollers to introduce an effective range error mitigation solution that
provides corrections in either NLOS or LOS conditions with a few mW of power.
Our extensive experimentation endorses the advantages and improvements of our
low-cost and power-efficient methodology
A Variational Learning Approach for Concurrent Distance Estimation and Environmental Identification
Wireless propagated signals encapsulate rich information
for high-accuracy localization and environment sensing.
However, the full exploitation of positional and environmental
features as well as their correlation remains challenging in
complex propagation environments. In this paper, we propose
a methodology of variational inference over deep neural networks
for concurrent distance estimation and environmental
identification. The proposed approach, namely inter-instance
variational auto-encoders (IIns-VAEs), conducts inference with
latent variables that encapsulate information about both distance
and environmental labels. A deep learning network with instance
normalization is designed to approximate the inference concurrently
via deep learning. We conduct extensive experiments on
real-world datasets and the results show the superiority of the
proposed IIns-VAE in both distance estimation and environmental
identification compared to conventional approaches
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