1,828 research outputs found
Power-Efficient Indoor Localization Using Adaptive Channel-aware Ultra-wideband DL-TDOA
Among the various Ultra-wideband (UWB) ranging methods, the absence of uplink
communication or centralized computation makes downlink
time-difference-of-arrival (DL-TDOA) localization the most suitable for
large-scale industrial deployments. However, temporary or permanent obstacles
in the deployment region often lead to non-line-of-sight (NLOS) channel path
and signal outage effects, which result in localization errors. Prior research
has addressed this problem by increasing the ranging frequency, which leads to
a heavy increase in the user device power consumption. It also does not
contribute to any increase in localization accuracy under line-of-sight (LOS)
conditions. In this paper, we propose and implement a novel low-power
channel-aware dynamic frequency DL-TDOA ranging algorithm. It comprises NLOS
probability predictor based on a convolutional neural network (CNN), a dynamic
ranging frequency control module, and an IMU sensor-based ranging filter. Based
on the conducted experiments, we show that the proposed algorithm achieves 50%
higher accuracy in NLOS conditions while having 46% lower power consumption in
LOS conditions compared to baseline methods from prior research
NLOS mitigation in indoor localization by marginalized Monte Carlo Gaussian smoothing
One of the main challenges in indoor time-of-arrival (TOA)-based wireless localization systems is to mitigate non-line-of-sight (NLOS) propagation conditions, which degrade the overall positioning performance. The positive skewed non-Gaussian nature of TOA observations under LOS/NLOS conditions can be modeled as a heavy-tailed skew t-distributed measurement noise. The main goal of this article is to provide a robust Bayesian inference framework to deal with target localization under NLOS conditions. A key point is to take advantage of the conditionally Gaussian formulation of the skew t-distribution, thus being able to use computationally light Gaussian filtering and smoothing methods as the core of the new approach. The unknown non-Gaussian noise latent variables are marginalized using Monte Carlo sampling. Numerical results are provided to show the performance improvement of the proposed approach
Non-line-of-sight Node Localization based on Semi-Definite Programming in Wireless Sensor Networks
An unknown-position sensor can be localized if there are three or more
anchors making time-of-arrival (TOA) measurements of a signal from it. However,
the location errors can be very large due to the fact that some of the
measurements are from non-line-of-sight (NLOS) paths. In this paper, we propose
a semi-definite programming (SDP) based node localization algorithm in NLOS
environment for ultra-wideband (UWB) wireless sensor networks. The positions of
sensors can be estimated using the distance estimates from location-aware
anchors as well as other sensors. However, in the absence of LOS paths, e.g.,
in indoor networks, the NLOS range estimates can be significantly biased. As a
result, the NLOS error can remarkably decrease the location accuracy.
And it is not easy to efficiently distinguish LOS from NLOS measurements. In
this paper, an algorithm is proposed that achieves high location accuracy
without the need of identifying NLOS and LOS measurement.Comment: submitted to IEEE ICC'1
Joint received signal strength, angle-of-arrival, and time-of-flight positioning
This paper presents a software positioning framework that is able to jointly use measured values of three parameters: the received signal strength, the angle-of-arrival, and the time-of-flight of the wireless signals. Based on experimentally determined measurement accuracies of these three parameters, results of a realistic simulation scenario are presented. It is shown that for the given configuration, angle-of-arrival and received signal strength measurements benefit from a hybrid system that combines both. Thanks to their higher accuracy, time-of-flight systems perform significantly better, and obtain less added value from a combination with the other two parameters
Statistical Characterization and Mitigation of NLOS Errors in UWB Localization Systems
In this paper some new experimental results about the statistical
characterization of the non-line-of-sight (NLOS) bias affecting time-of-arrival
(TOA) estimation in ultrawideband (UWB) wireless localization systems are
illustrated. Then, these results are exploited to assess the performance of
various maximum-likelihood (ML) based algorithms for joint TOA localization and
NLOS bias mitigation. Our numerical results evidence that the accuracy of all
the considered algorithms is appreciably influenced by the LOS/NLOS conditions
of the propagation environment
- …