2021
DOI: 10.1109/tvt.2021.3058778
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Auto-Calibration of Automotive Radars in Operational Mode Using Simultaneous Localisation and Mapping

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Cited by 11 publications
(14 citation statements)
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References 27 publications
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“…which describes the problem of joint localisation -x [t] , calibration -γ γ γ [t] and mapping -m. This probability can be calculated via a recursive Bayesian filter, similarly to the original SLAM problem [9], [8]. The implementation of this Bayesian filter requires the definition on the observation (measurement) and dynamic (motion) models.…”
Section: Problem Statementmentioning
confidence: 99%
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“…which describes the problem of joint localisation -x [t] , calibration -γ γ γ [t] and mapping -m. This probability can be calculated via a recursive Bayesian filter, similarly to the original SLAM problem [9], [8]. The implementation of this Bayesian filter requires the definition on the observation (measurement) and dynamic (motion) models.…”
Section: Problem Statementmentioning
confidence: 99%
“…The measurement in each transmit-receive pair depends only on two calibration coefficients in these channels and thus the submatrix G ar,γ is sparse. The derivatives can be calculated directly from ( 6), (8).…”
Section: B Observation Modelmentioning
confidence: 99%
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“…In [28], a method to calibrate ranging biases in a sensor network is proposed, however there is no cooperation between the sensors. In the recent paper [29], SLAM is applied to calibrate a uniform linear array (ULA) of an automotive radar, using targets of opportunity. The approach is limited to amplitude and phase calibration of the individual ports, other antenna response deviations cannot be corrected.…”
Section: Introductionmentioning
confidence: 99%
“…• We present a novel method to simultaneously calibrate an antenna and determine its position and orientation in a cooperative network. In contrast to [29], the method can be applied to arbitrary antenna types, e.g. phased arrays, co-located antennas or MMAs, and allows a full calibration of the antenna, including gain-phase offsets, mutual coupling and nonlinear antenna response deviations.…”
Section: Introductionmentioning
confidence: 99%