Unmanned Aerial Vehicle Indoor Navigation using Wi-Fi Trilateration

Marasigan, Rufo I.; Austria, Yolanda D.; Enriquez, Jennifer B.; Lacatan, Luisito Lolong; Dellosa, Rhowel M.

doi:10.1109/icsgrc49013.2020.9232623

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…Although its recent applications are mostly known in the area of navigation and localization, e.g. aerospace (Molesky & Wilhelm, 2002), indoors (Andò et al, 2021;Marasigan, 2020), and underwater (Wetter & Seto, 2022); trilateration is also found to be useful in other area such as biomedical engineering (Alshareef et. al (2020); Blaiech et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Revisiting Trilateration Method Based on Time-of-Flight Measurements for Navigation

Simamora,

Rachmach,

Rizqon

et al. 2023

J. Ris. Multidisip. Inov. Tech.

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This paper revisits trilateration in three-dimensional positioning. Specifically, range between a positioning target and the reference points through time-of-flight (ToF) measurements. In a ToF, range is yielded by multiplying the time required by a wave to travel between two points and its propagation speed. Position of the target can be then estimated once the number of references are adequate, i.e. at least three for two-dimensional positioning and four for three-dimensional one. In this paper, the positioning is considered for navigation where the target moves following a trajectory whilst the ToFs take place in a certain period. The target position at the time is computed based on the ToFs through least square estimation. Through a numerical simulation, it is shown that the trilateration can track a target’s trajectory despite the decreasing performance at the end of the course.

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Section: Introductionmentioning

confidence: 99%

Revisiting Trilateration Method Based on Time-of-Flight Measurements for Navigation

Simamora,

Rachmach,

Rizqon

et al. 2023

J. Ris. Multidisip. Inov. Tech.

View full text Add to dashboard Cite

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“…The authors in Ref. [12] used Wireless‐Fidelity (Wi‐Fi) signals and developed a trilateration algorithm to accomplish UAV navigation in indoor environments. In Ref.…”

Section: Introductionmentioning

confidence: 99%

WLS algorithm for UAV navigation in satellite‐less environments

Santos

Matos-Carvalho

Tomic

et al. 2022

IET Wireless Sensor Systems

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This work addresses one of the major challenges in the field of Unmanned Aerial Vehicles (UAVs), which is related to UAV navigation in 3‐dimensional indoor environments. This topic is highly relevant due to limited‐to‐no availability of satellite signals that are usually the main sources used to govern the movement of UAVs in outdoor environments. Instead, the algorithm proposed in this work resorts to terrestrial radio measurements between the UAV and a set of stationary reference points to accomplish accurate navigation. The radio measurements are first exploited to give origin to range information that is used to calculate intersection points between a suitable subset of spheres, which serve as a coarse estimate of the UAV's position. From the intersection points, both azimuth and elevation angle estimates are obtained at each reference point. Finally, by taking advantage of estimated bearing information and applying simple geometry, the localisation problem is formulated according to the Weighted Least Squares (WLS) criterion and solved in closed form. The authors’ simulation results show good performance of the proposed approach, matching or even outperforming the state‐of‐the‐art methods in terms of localisation accuracy with significantly lower computational complexity.

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“…Wireless fidelity (Wi-Fi) trilateration is another method that could be used [21]. Trilateration is a simple geometric approach in which one is interested in finding the intersection points of the circles centered around a reference point with radii equal to range observations extracted from a certain property of the received radio signal (e.g., TOF, RSS, etc.).…”

Section: Introductionmentioning

confidence: 99%

GTRS-Based Algorithm for UAV Navigation in Indoor Environments Employing Range Measurements and Odometry

Matos-Carvalho¹,

Santos²,

Tomic³

et al. 2021

IEEE Access

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This work addresses the problem of unmanned aerial vehicle (UAV) navigation in indoor environments. Due to unavailability of satellite signals, the proposed algorithm takes advantage of terrestrial radio measurements between the UAV and a set of stationary reference points, from which it extracts range information, as well as odometry by means of inertial sensors, such as accelerometer. On the one hand, based on maximum a posteriori (MAP) criterion, the range information and accumulated knowledge throughout the UAV's movement are employed to derive a generalized trust region sub-problem (GTRS), that is solved exactly via bisection procedure. On the other hand, by using the UAV's transform in relation to the world, another position estimation is obtained by employing odometry. Finally, the two position estimates are combined through a Kalman filter (KF) to enhance the positioning accuracy and obtain the final UAV's position estimation. The UAV is then navigated to a desired destination, by simply calculating the velocity components in the shortest path. Our results show that the proposed algorithm is robust to various model parameters for high precision (HP) UAV sensors, achieving reasonably good positioning accuracy. Besides, the results corroborate that the proposed algorithm is suitable for real-time applications, consuming (on average) only 21 ms to estimate the UAV position.INDEX TERMS Generalized trust region sub-problem (GTRS), indoor environments, Kalman filter (KF), maximum a posteriori (MAP) estimator, navigation, odometry, positioning, unmanned aerial vehicle (UAV).

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Unmanned Aerial Vehicle Indoor Navigation using Wi-Fi Trilateration

Cited by 9 publications

References 17 publications

Revisiting Trilateration Method Based on Time-of-Flight Measurements for Navigation

Revisiting Trilateration Method Based on Time-of-Flight Measurements for Navigation

WLS algorithm for UAV navigation in satellite‐less environments

GTRS-Based Algorithm for UAV Navigation in Indoor Environments Employing Range Measurements and Odometry

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