Analysis of reliable deployment of TDOA local positioning architectures

Díez-González, Javier; Álvarez, Rubén; Verde, Paula; Ferrero-Guillén, Rubén; Pérez, Hilde

doi:10.1016/j.neucom.2021.12.074

Cited by 21 publications

(9 citation statements)

References 50 publications

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“…In UWB localization systems, the time difference of arrival (TDOA) is usually used as it does not directly use the arrival time of the signal, but uses the time difference of the signals received by multiple base stations to determine the location of the mobile station. Compared with the time of arrival (TOA) algorithm, significantly fewer communications are required to complete a localization, and the localization accuracy is also improved [ 15 – 17 ].…”

Section: Related Workmentioning

confidence: 99%

Firefly swarm intelligence based cooperative localization and automatic clustering for indoor FANETs

et al. 2023

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At present, the applications of multiple unmanned aerial vehicles (UAVs) are becoming more and more widespread, covering many civil and military fields. When performing tasks, UAVs will form a flying ad hoc network (FANET) to communicate to each other. However, subject to high mobility, dynamic topology, and limited energy of FANETs, maintaining stable communication performance is a challenging task. As a potential solution, the clustering routing algorithm divides the entire network into multiple clusters to achieve strong network performance. Meanwhile, the accurate localization of UAV is also strongly required when FANETs are applied in the indoor scenario. In this paper, we propose a firefly swarm intelligence based cooperative localization (FSICL) and automatic clustering (FSIAC) for FANETs. Firstly, we combine the firefly algorithm (FA) and Chan algorithm to better cooperative locate the UAVs. Secondly, we propose the fitness function consisting of link survival probability, node degree-difference, average distance, and residual energy, and take it as the light intensity of the firefly. Thirdly, the FA is put forward for cluster-head (CH) selection and cluster formation. Simulation results indicate that the proposed FSICL algorithm achieves the higher localization accuracy faster, and the FSIAC algorithm achieves the higher stability of clusters, longer link expiration time (LET), and longer node lifetime, all of which improve the communication performance for indoor FANETs.

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Section: Related Workmentioning

confidence: 99%

Firefly swarm intelligence based cooperative localization and automatic clustering for indoor FANETs

et al. 2023

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“…Due to the faux-visible range (nondirect path) in indoor spaces, where the reflected or scattered signals can cause significant positioning errors, the current AOA positioning technology is not suitable for indoor positioning systems, but rather for suburban areas where the influence of multipath is small [ 13 ]. A slight variation in the base station’s placement angle when the mobile station is far from it will result in a significant mistake in the positioning distance and have an impact on positioning accuracy [ 14 ]. TDOA positioning science and technology, which is more effective in the error environment but also has a greater reliance on the time reference, requires full time synchronization between all the base stations involved in positioning, but does not need to know the time of transmitting from the mobile station, nor does it require synchronization between the mobile station and the base station.…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization

Cao

Chen

et al. 2023

Biomimetics

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A TDOA/AOA hybrid location algorithm based on the crow search algorithm optimized by particle swarm optimization is proposed to address the challenge of solving the nonlinear equation of time of arrival (TDOA/AOA) location in the non-line-of-sight (NLoS) environment. This algorithm keeps its optimization mechanism on the basis of enhancing the performance of the original algorithm. To obtain a better fitness value throughout the optimization process and increase the algorithm’s optimization accuracy, the fitness function based on maximum likelihood estimation is modified. In order to speed up algorithm convergence and decrease needless global search without compromising population diversity, an initial solution is simultaneously added to the starting population location. Simulation findings demonstrate that the suggested method outperforms the TDOA/AOA algorithm and other comparable algorithms, including Taylor, Chan, PSO, CPSO, and basic CSA algorithms. The approach performs well in terms of robustness, convergence speed, and node positioning accuracy.

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“…Distance-based UWB positioning systems generally use the Time Difference of Arrival (TDOA) [14][15][16] and Two-Way Ranging (TWR) [17,18] in the ranging process, and both methods utilize device timeline data. Distance-based positioning has advantages such as no major constraints on the infrastructure configuration, high accuracy, and no severe interference from multiple paths, but it has a fatal disadvantage in that the location estimation error is greatly affected by distance.…”

Section: Introductionmentioning

confidence: 99%

Vehicular Localization Framework with UWB and DAG-Based Distributed Ledger for Ensuring Positioning Accuracy and Security

Kim,

Lee,

Yoo

et al. 2023

Electronics

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In performing various missions using various types of vehicles or other moving objects, the positioning of each agent within the swarm is essential. In particular, for missions that require precise location estimation, the case of malicious attacks through data forgery cannot be excluded. In this paper, we propose a highly secure and accurate localization framework utilizing a Directed-Acyclic-Graph (DAG)-based distributed ledger as an intelligent vehicle network for an Ultra-Wideband (UWB) positioning system. When performing UWB positioning, the data generated from each node are used to calculate the Time of Flight (ToF), and if any of them are tampered with, the overall positioning performance is greatly reduced. We prevented positioning performance degradation by ensuring the safety and integrity of the data by applying a chain-based logical network between each node utilized for UWB positioning. The experimental results indicated that the proposed framework was effective at providing system stability and security without affecting the UWB positioning performance. In addition, the performance of the framework was verified by presenting defense indicators for various attack scenarios.

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Analysis of reliable deployment of TDOA local positioning architectures

Cited by 21 publications

References 50 publications

Firefly swarm intelligence based cooperative localization and automatic clustering for indoor FANETs

Firefly swarm intelligence based cooperative localization and automatic clustering for indoor FANETs

Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization

Vehicular Localization Framework with UWB and DAG-Based Distributed Ledger for Ensuring Positioning Accuracy and Security

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