Robust UKF-IMM Filter for Tracking an Off-road Ground Target

Choi, Jae Weon; Samuel, Kangwagye

doi:10.1007/s12555-018-0249-9

Cited by 12 publications

(2 citation statements)

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“…Location and status estimation systems for vehicles have extensively used this technique [30], enabling state estimation filters to adapt more flexibly to changes in the noise environment, thereby increasing the accuracy and robustness of state estimation. Choi JW et al [31] proposed an IMM-RUKF algorithm for all-terrain off-road ground target tracking. This algorithm performs better in tracking highly maneuverable targets across various scenarios than a single model.…”

Section: Introductionmentioning

confidence: 99%

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State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF

Chen,

Zhang,

et al. 2024

Applied Sciences

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The prerequisite for intelligent vehicles to achieve autonomous driving and active safety functions is acquiring accurate vehicle state parameters. Traditional Kalman filters often underperform in non-Gaussian noise environments due to their reliance on Gaussian assumptions. This paper presents the IMM-MCCKF filter, which integrates the interacting multiple model theory (IMM) and the maximum correntropy cubature Kalman filter method (MCCKF), for estimating the state parameters of intelligent vehicles. The IMM-MCCKF successfully suppresses non-Gaussian noise by optimizing a nonlinear cost function using the maximum correntropy criteria, allowing it to capture and analyze signal data outliers accurately. The filter designs various state and measurement noise submodels to adapt to the environment dynamically, thus reducing the impact of unknown noise statistical properties. Accurately measuring the velocity of a vehicle and the angle at which its center of mass drifts sideways is of utmost importance for its ability to maneuver, maintain stability, and ensure safety. These parameters are critical for implementing advanced control systems in intelligent vehicles. The study begins by constructing a nonlinear Dugoff tire model and a three-degrees-of-freedom (3DOF) vehicle model. Subsequently, utilizing low-cost vehicle sensor signals, joint simulations are conducted on the CarSim-Simulink platform to estimate vehicle state parameters. The results demonstrate that in terms of estimation accuracy and robustness in non-Gaussian noise scenarios, the proposed IMM-MCCKF filter consistently outperforms the MCCKF and CKF algorithms.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%