ArticleSensors (Basel, Switzerland)2019
3D Tdoa Problem Solution with Four Receiving Nodes.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- 3D TDOA Emitter Localization Using Conic Approximation.Sensors (Basel, Switzerland) · 2023Article
- Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models.Entropy (Basel, Switzerland) · 2021Article
- Memetic Chains for Improving the Local Wireless Sensor Networks Localization in Urban Scenarios.Sensors (Basel, Switzerland) · 2021Article
- A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2021Article
- Angle-Awareness Based Joint Cooperative Positioning and Warning for Intelligent Transportation Systems.Sensors (Basel, Switzerland) · 2020Article
- Hybrid Memetic Algorithm for the Node Location Problem in Local Positioning Systems.Sensors (Basel, Switzerland) · 2020Article
- Method for Remote Determination of Object Coordinates in Space Based on Exact Analytical Solution of Hyperbolic Equations.Sensors (Basel, Switzerland) · 2020Article
- Particle Filtering for Three-Dimensional TDoA-Based Positioning Using Four Anchor Nodes.Sensors (Basel, Switzerland) · 2020Article
- Local Wireless Sensor Networks Positioning Reliability Under Sensor Failure.Sensors (Basel, Switzerland) · 2020Article
- Refinement of TOA Localization with Sensor Position Uncertainty in Closed-Form.Sensors (Basel, Switzerland) · 2020Article
- Genetic Algorithm Approach to the 3D Node Localization in TDOA Systems.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Time difference of arrival (TDOA) positioning methods have experienced growing importance over the last few years due to their multiple applications in local positioning systems (LPSs). While five sensors are needed to determine an unequivocal three-dimensional position, systems with four nodes present two different solutions that cannot be discarded according to mathematical standards. In this paper, a new methodology to solve the 3D TDOA problems in a sensor network with four beacons is proposed. A confidence interval, which is defined in this paper as a sphere, is defined to use positioning algorithms with four different nodes. It is proven that the separation between solutions in the four-beacon TDOA problem allows the transformation of the problem into an analogous one in which more receivers are implied due to the geometric properties of the intersection of hyperboloids. The achievement of the distance between solutions needs the application of genetic algorithms in order to find an optimized sensor distribution. Results show that positioning algorithms can be used 96.7% of the time with total security in cases where vehicles travel at less than 25 m/s.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.