Data Processing Methods of Flow Field Based on Artificial Lateral Line Pressure Sensors

The estimation of the type and parameter of flow field is important for robotic fish. Recent estimation methods cannot meet the requirements of the robotic fish due to the lack of prior knowledge or the under-fitting of the model. A processing method including data preprocessing, feature extraction, feature selection, flow type classification and flow field parameters estimation, is proposed based on the data of the pressure sensors in an artificial lateral line. Probabilistic Neural Network (PNN) is used to classify the flow field type and the Generalized Regressive Neural Network (GRNN) is the best choice for estimating the flow field parameters. Also, a few filtering methods for data preprocessing, three methods for feature selection and nine parameters estimation methods are analysis for choosing better method. The proposed method is verified by the experiments with both simulation and real data.

Paper Link: springer

Recommended citation:

@article{sun2022data,
  title={Data Processing Methods of Flow Field Based on Artificial Lateral Line Pressure Sensors},
  author={Sun, Bing and Xu, Yi and Xie, Shuhang and Xu, Dong and Liang, Yupu},
  journal={Journal of Bionic Engineering},
  volume={19},
  number={6},
  pages={1797--1815},
  year={2022},
  publisher={Springer}
}