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Multiobject Localization Using Magnetic Tensor Gradiometer Array and Improved iForest
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AI
DOI：10.1109/LGRS.2021.3122457.png)
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Abstract
En 中文
The current magnetic localization technology based on magnetic tensor gradient (MTG) information mostly focuses on single-object localization using a tensor gradiometer, which cannot be applied to more common scenarios where there are multiple objects in the test area and their number changes over time. The difficulty is that magnetic detection is a blind experiment. The lack of prior information will lead to poor accuracy, slow inversion speed, or even failure. This letter presents a novel method to estimate the information of multiple objects, including their number, trajectory, and magnetic moment. Specifically, an improved isolation forest algorithm is used to predetermine the best possible number, center coordinates, and magnetic moments of the objects from the rough solution set obtained by the magnetic tensor gradiometer array. Given the above prior conditions, we can safely get the accurate estimation of the objects by formulating a suitable cost function between the measured magnetic data and those theoretically calculated based on the extended multiple magnetic dipole mathematical model. The results of the simulation experiment show that the proposed method provides a useful way for multiobject localization.
Keywords:
Location awareness
Magnetometers
Tensors
Magnetic moments
Magnetic field measurement
Detectors
Trajectory
Improved isolation forest
magnetic tensor gradiometer array
multiobject
priori information prediction
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