Fei Du

Dr. Fei Du


Research Focus & Metrics

Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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2 November 2015 in Robotica
Verified
Journal Article
Closed access
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XHXinjing Huang
Xinjing Huang
LYLi Yibo
Li Yibo
FDFei Du
Fei Du
JSJin Shijiu
Jin Shijiu
SUMMARY: A 2D path following control method for Autonomous Underwater Vehicles (AUVs) based on dynamic circle heading modification (DCHM) is presented. The method makes a dynamic auxiliary circle, whose radius depends on the cross-track error e, to intersect the desired path to get a new expected path point, and then determines a modified expected heading for the AUV. The guidance function is achieved by a direct mapping between e and the heading modification value Ψm. Several cases are tested in order to demonstrate the performance of the guidance and control method based on DCHMs for a real AUV. Results show that methods using a convex mapping function between e and Ψm based on our new idea can easily achieve a better convergence of path following, and reduce the error between the actual and desired heading angles. We can also customize a discretionary mapping between e and Ψm to get better path following performance.

18 August 2015 in Sensors
Verified
Journal Article
Open access
PDF file
FDFei Du
Fei Du
YLYibo Li
Yibo Li
SJShijiu Jin
Shijiu Jin
An accurate performance analysis on the MDL criterion for source enumeration in array processing is presented in this paper. The enumeration results of MDL can be predicted precisely by the proposed procedure via the statistical analysis of the sample eigenvalues, whose distributive properties are investigated with the consideration of their interactions. A novel approach is also developed for the performance evaluation when the source number is underestimated by a number greater than one, which is denoted as “multiple-missed detection”, and the probability of a specific underestimated source number can be estimated by ratio distribution analysis. Simulation results are included to demonstrate the superiority of the presented method over available results and confirm the ability of the proposed approach to perform multiple-missed detection analysis.