The Fermat–Weber location problem in single integrator dynamics using only local bearing angles

Trinh, Minh Hoang; Lee, Byunghun; Ahn, Hyo‐Sung

doi:10.1016/j.automatica.2015.06.017

Cited by 13 publications

(2 citation statements)

References 31 publications

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“…(59) The cost function ( 59) is a Fermat-Weber location problem, which can be solved by using the method in [21]. Hence, the error bound u of position estimates will also remain unchanged even if we change the original point of the global coordinate frame or translate the whole network.…”

Section: Endmentioning

confidence: 99%

3-D Distributed Localization With Mixed Local Relative Measurements

Xie

2020

IEEE Trans. Signal Process.

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This paper studies 3-D distributed network localization using mixed types of local relative measurements. Each node holds a local coordinate frame without a common orientation and can only measure one type of information (relative position, distance, relative bearing, angle, or ratio-of-distance measurements) about its neighboring nodes in its local coordinate frame. A novel rigidity-theory-based distributed localization is developed to overcome the challenge due to the absence of a global coordinate frame. The main idea is to construct displacement constraints for the positions of the nodes by using mixed local relative measurements. Then, a linear distributed localization algorithm is proposed for each free node to estimate its position by solving the displacement constraints. The algebraic condition and graph condition are obtained to guarantee the global convergence of the proposed distributed localization algorithm.

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Section: Endmentioning

confidence: 99%

3-D Distributed Localization With Mixed Local Relative Measurements

Xie

2020

IEEE Trans. Signal Process.

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“…The cost function (5.25) is a Fermat-Weber location problem, which can be solved by using the method in [137]. Hence, the error bound e b of position estimates will also remain unchanged even if we change the original point of the global coordinate frame or translate the whole network.…”

Section: Distributed Localization With Measurement Noisementioning

confidence: 99%