990 research outputs found
Improving Accuracy in Ultra-Wideband Indoor Position Tracking through Noise Modeling and Augmentation
The goal of this research is to improve the precision in tracking of an ultra-wideband (UWB) based Local Positioning System (LPS). This work is motivated by the approach taken to improve the accuracies in the Global Positioning System (GPS), through noise modeling and augmentation. Since UWB indoor position tracking is accomplished using methods similar to that of the GPS, the same two general approaches can be used to improve accuracy. Trilateration calculations are affected by errors in distance measurements from the set of fixed points to the object of interest. When these errors are systemic, each distinct set of fixed points can be said to exhibit a unique set noise. For UWB indoor position tracking, the set of fixed points is a set of sensors measuring the distance to a tracked tag. In this work we develop a noise model for this sensor set noise, along with a particle filter that uses our set noise model. To the author\u27s knowledge, this noise has not been identified and modeled for an LPS. We test our methods on a commercially available UWB system in a real world setting. From the results we observe approximately 15% improvement in accuracy over raw UWB measurements. The UWB system is an example of an aided sensor since it requires a person to carry a device which continuously broadcasts its identity to determine its location. Therefore the location of each user is uniquely known even when there are multiple users present. However, it suffers from limited precision as compared to some unaided sensors such as a camera which typically are placed line of sight (LOS). An unaided system does not require active participation from people. Therefore it has more difficulty in uniquely identifying the location of each person when there are a large number of people present in the tracking area. Therefore we develop a generalized fusion framework to combine measurements from aided and unaided systems to improve the tracking precision of the aided system and solve data association issues in the unaided system. The framework uses a Kalman filter to fuse measurements from multiple sensors. We test our approach on two unaided sensor systems: Light Detection And Ranging (LADAR) and a camera system. Our study investigates the impact of increasing the number of people in an indoor environment on the accuracies using a proposed fusion framework. From the results we observed that depending on the type of unaided sensor system used for augmentation, the improvement in precision ranged from 6-25% for up to 3 people
3-D Motion Capture of an Unmodified Drone with Single-chip Millimeter Wave Radar
Accurate motion capture of aerial robots in 3-D is a key enabler for
autonomous operation in indoor environments such as warehouses or factories, as
well as driving forward research in these areas. The most commonly used
solutions at present are optical motion capture (e.g. VICON) and Ultrawideband
(UWB), but these are costly and cumbersome to deploy, due to their requirement
of multiple cameras/sensors spaced around the tracking area. They also require
the drone to be modified to carry an active or passive marker. In this work, we
present an inexpensive system that can be rapidly installed, based on
single-chip millimeter wave (mmWave) radar. Importantly, the drone does not
need to be modified or equipped with any markers, as we exploit the Doppler
signals from the rotating propellers. Furthermore, 3-D tracking is possible
from a single point, greatly simplifying deployment. We develop a novel deep
neural network and demonstrate decimeter level 3-D tracking at 10Hz, achieving
better performance than classical baselines. Our hope is that this low-cost
system will act to catalyse inexpensive drone research and increased autonomy.Comment: Submitted to The 2021 International Conference on Robotics and
Automation (ICRA 2021
A Meta-Review of Indoor Positioning Systems
An accurate and reliable Indoor Positioning System (IPS) applicable to most indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of indoor positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys
Soft information for localization-of-things
Location awareness is vital for emerging Internetof-
Things applications and opens a new era for Localizationof-
Things. This paper first reviews the classical localization
techniques based on single-value metrics, such as range and
angle estimates, and on fixed measurement models, such as
Gaussian distributions with mean equal to the true value of the
metric. Then, it presents a new localization approach based
on soft information (SI) extracted from intra- and inter-node
measurements, as well as from contextual data. In particular,
efficient techniques for learning and fusing different kinds of SI
are described. Case studies are presented for two scenarios in
which sensing measurements are based on: 1) noisy features
and non-line-of-sight detector outputs and 2) IEEE 802.15.4a
standard. The results show that SI-based localization is highly
efficient, can significantly outperform classical techniques, and
provides robustness to harsh propagation conditions.RYC-2016-1938
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