IEEE/CAA Journal of Automatica Sinica
Citation:  Q. Ge, Y. Cheng, H. Li, Z. Ye, Y. Zhu, and G. Yao, “A nonparametric scheme for identifying data characteristic based on curve similarity matching,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1424–1437, Jun. 2024. doi: 10.1109/JAS.2024.124359 
For accurately identifying the distribution characteristic of Gaussianlike noises in unmanned aerial vehicle (UAV) state estimation, this paper proposes a nonparametric scheme based on curve similarity matching. In the framework of the proposed scheme, a Parzen window (kernel density estimation, KDE) method on sliding window technology is applied for roughly estimating the sample probability density, a precise data probability density function (PDF) model is constructed with the least square method on Kfold cross validation, and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape, abruptness and symmetry. Some comparison simulations with classical methods and UAV flight experiment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussianlike data, which provides better reference for the design of Kalman filter (KF) in complex water environment.
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