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Position estimation in indoor localization with trilateration
Triangulateration uses geometric distance to estimate the location of user by employing techniques like received signal strength (RSS), time-of-arrival (TOA), time-difference-of-arrival (TDOA) and image processing.
Radio frequency (RF) signal positioning using TOA or TDOA techniques generally requires timing synchronization of the anchors and/or the anchors and targets. If the desired position accuracy is high and coverage area is large, timing synchronization will be an extremely challenging issue. The first part of this dissertation focuses on improving the performance or deployability of indoor localization systems. Specifically, we propose a synchronization-free positioning network architecture that eliminates the need of timing synchronization. Another problem that remains unsolved in RF based localization is the non-line-of-sight (NLOS) problem, which greatly degrades the positioning performance. We propose a semidefinite programing (SDP) with a soft-minimal method and an NLOS link identification method with bias deduction to mitigate the NLOS error TOA systems. For TDOA systems, NLOS mitigation is more difficult since a reference should be fixed first. To overcome this problem, we propose a method to transform the TDOA architecture into a TOA one, and then form a SDP problem with new constraints.
To avoid the special problems and difficulties in RF signal positioning, such as the synchronization and NLOS problems, in the second part of the dissertation, we propose an image-tag based localization using image processing and convolutional neural network (CNN). In the proposed method, after the segmentation of the tag from the image, information such as the tag ID, the distance, and the angle with reference to the camera are retrieved through CNNs. The camera position is finally reliably and accurately estimated from such retrieved information. The proposed method simplifies the system and provide good accuracy compare to RF based system. In addition, the proposed method effectively resolve those issues that exist in the traditional image-based localization, like the high cost, blind spot problems and unreliable and not scalable for in changing environments.Keywords: Convolutional neural network (CNN), Wi-Fi, Image, UWB, Algorithm, Localizatio
Cooperative and Distributed Localization for Wireless Sensor Networks in Multipath Environments
We consider the problem of sensor localization in a wireless network in a
multipath environment, where time and angle of arrival information are
available at each sensor. We propose a distributed algorithm based on belief
propagation, which allows sensors to cooperatively self-localize with respect
to one single anchor in a multihop network. The algorithm has low overhead and
is scalable. Simulations show that although the network is loopy, the proposed
algorithm converges, and achieves good localization accuracy
Soft-connected Rigid Body Localization: State-of-the-Art and Research Directions for 6G
This white paper describes a proposed article that will aim to provide a
thorough study of the evolution of the typical paradigm of wireless
localization (WL), which is based on a single point model of each target,
towards wireless rigid body localization (W-RBL). We also look beyond the
concept of RBL itself, whereby each target is modeled as an independent
multi-point three-dimensional (3D), with shape enforced via a set of
conformation constraints, as a step towards a more general approach we refer to
as soft-connected RBL, whereby an ensemble of several objects embedded in a
given environment, is modeled as a set of soft-connected 3D objects, with rigid
and soft conformation constraints enforced within each object and among them,
respectively. A first intended contribution of the full version of this article
is a compact but comprehensive survey on mechanisms to evolve WL algorithms in
W-RBL schemes, considering their peculiarities in terms of the type of
information, mathematical approach, and features the build on or offer. A
subsequent contribution is a discussion of mechanisms to extend W-RBL
techniques to soft-connected rigid body localization (SCW-RBL) algorithms
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