NDLI: Adaptive Fading Extended Kalman Filtering for Mobile Robot Localization Using a Doppler–Azimuth Radar

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Adaptive Fading Extended Kalman Filtering for Mobile Robot Localization Using a Doppler–Azimuth Radar

Content Provider	MDPI
Author	Li, Bin Lu, Yanyang Karimi, Hamid Reza
Copyright Year	2021
Abstract	In this paper, the localization problem of a mobile robot equipped with a Doppler–azimuth radar (D–AR) is investigated in the environment with multiple landmarks. For the type (2,0) robot kinematic model, the unknown modeling errors are generally aroused by the inaccurate odometer measurement. Meanwhile, the inaccurate odometer measurement can also give rise to a type of unknown bias for the D–AR measurement. For reducing the influence induced by modeling errors on the localization performance and enhancing the practicability of the developed robot localization algorithm, an adaptive fading extended Kalman filter (AFEKF)-based robot localization scheme is proposed. First, the robot kinematic model and the D–AR measurement model are modified by considering the impact caused by the inaccurate odometer measurement. Subsequently, in the frame of adaptive fading extended Kalman filtering, the way to the addressed robot localization problem with unknown biases is sought out and the stability of the developed AFEKF-based localization algorithm is also discussed. Finally, in order to testify the feasibility of the AFEKF-based localization scheme, three different kinds of modeling errors are considered and the comparative simulations are conducted with the conventional EKF. From the comparative simulation results, it can be seen that the average localization error under the developed AFEKF-based localization scheme is ${[0.0245 m 0.0224 m 0.0039 rad]}^{T}$ and the average localization errors using the conventional EKF are ${[1.0405 m 2.2700 m 0.1782 rad]}^{T}$ , ${[0.4963 m 0.3482 m 0.0254 rad]}^{T}$ and ${[0.2774 m 0.3897 m 0.0353 rad]}^{T}$ , respectively, under the three cases of the constant bias, the white Gaussian stochastic bias and the bounded uncertainty bias.
Starting Page	2544
e-ISSN	20799292
DOI	10.3390/electronics10202544
Journal	Electronics
Issue Number	20
Volume Number	10
Language	English
Publisher	MDPI
Publisher Date	2021-10-18
Access Restriction	Open
Subject Keyword	Electronics Imaging Science Localization Mobile Robot Adaptive Fading Extended Kalman Filter Modeling Errors Unknown Measurement Bias Doppler-azimuth Radar
Content Type	Text
Resource Type	Article

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