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Volume 11 Issue 6
Jun.  2024

IEEE/CAA Journal of Automatica Sinica

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Q. Ge, Y. Cheng, H. Li, Z. Ye, Y. Zhu, and  G. Yao,  “A non-parametric 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
Citation: Q. Ge, Y. Cheng, H. Li, Z. Ye, Y. Zhu, and  G. Yao,  “A non-parametric 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

A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching

doi: 10.1109/JAS.2024.124359
Funds:  This work was supported by the National Natural Science Foundation of China (62033010) and Qing Lan Project of Jiangsu Province (R2023Q07)
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  • For accurately identifying the distribution characteristic of Gaussian-like noises in unmanned aerial vehicle (UAV) state estimation, this paper proposes a non-parametric 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 K-fold 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 Gaussian-like data, which provides better reference for the design of Kalman filter (KF) in complex water environment.

     

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    Highlights

    • Probability Density Function (PDF) curve estimation: On the condition of unknown data distribution, a Parzen window method on the sliding window technology is adopted for the data PDF rough estimation. The sliding window technology ensure real-time monitoring of data characteristic
    • Data Characteristic Analysis: With the shape difference between a Gaussian-like PDF curve and a Gaussian PDF curve, the intelligent optimization algorithm adjusts mean and variance to analyse the difference between two curve shapes (slope, kullback-leibler divergence). In addition, two sample statistics (simplified skewness-kurtosis, simplified quantile) are used in the abruptness and symmetry analysis, which is modified by Monte-Carlo method
    • Characteristic Evaluation Method: Based on the four calculated indices of slope, kullback-leibler divergence, simplified skewness-kurtosis, simplified quantile, multi-index evaluation based on the method of weighted mean is proposed to improve the correct recognition rate between Gaussian-like PDF curve and standard Gaussian PDF curve

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