@article{Chen_Te_Estimation_2018,
 author={Chen, Te and Chen, Long and Cai, Yingfeng and Xu, Xing},
 howpublished={online},
 year={2018},
 abstract={<p>
 This paper presents a novel sideslip angle estimator based on the 
 pseudo-multi-sensor fusion method. The
 </p>
 <p>
 kinematics-based and dynamics-based sideslip angle estimators are 
 designed for sideslip angle estimation.
 </p>
 <p>
 Also, considering the influence of ill-conditioned matrix and model 
 uncertainty, a novel sideslip angle estimator
 </p>
 <p>
 is proposed based on the wheel speed coupling relationship using a 
 modified recursive least squares
 </p>
 <p>
 algorithm. In order to integrate the advantages of above three sideslip 
 angle estimators, drawing lessons
 </p>
 <p>
 from the multisensory information fusion technology, a novel thinking of 
 sideslip angle estimator design is
 </p>
 <p>
 presented through information fusion of pseudo-multi-sensors. 
 Simulations and experiments were carried
 </p>
 <p>
 out, and effectiveness of the proposed estimation method was verified.
 </p>},
 type={Artykuły / Articles},
 title={Estimation of vehicle sideslip angle via pseudo-multisensor information fusion method},
 volume={vol. 25},
 number={No 3},
 pages={499-516},
 journal={Metrology and Measurement Systems},
 publisher={Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation},
 doi={10.24425/123902},
 keywords={vehicle state estimation, sideslip angle, recursive least squares, multi-sensor information fusion, pseudo-measurements},
}