Estimating Morphological Parameters of Insects in Nonhorizontal Flight Attitudes Based on Scattering Matrix Reconstruction

Cheng Hu, Fan Zhang, Weidong Li*, Rui Wang, Jiangtao Wang, Haibo Liu

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

For vertical-looking radars (VLRs), it is typically assumed that insects maintain a steady and approximately horizontal flight attitude as they pass through the radar beam, allowing for the estimation of insect morphological parameters by measuring the radar cross section (RCS) for a ventral aspect. However, for tracking radars, which dynamically track and monitor insects, the attitude of the insect relative to the radar beam constantly changes. This dynamic change in attitude renders traditional insect morphological parameter estimation methods based on the ventral-aspect RCS ineffective. This article proposes a novel method for estimating the morphological parameters of insects in nonhorizontal flight attitudes. By determining the azimuth and pitch angles of the insect's body axis relative to the radar antenna reference coordinate system and reconstructing the scattering matrix (SM) of the insect from nonhorizontal attitudes to a horizontal attitude, we achieve the estimation of morphological parameters for insects in nonhorizontal attitudes. The effectiveness of the proposed method is validated using a fully polarimetric multiangle observation dataset of 33 insects from six species measured in a microwave anechoic chamber. The mean relative errors (MREs) in estimating the mass and length of the insects across 20 different observation angles are 20.06% and 12.85%, respectively. Compared to estimates obtained without making the correction, the accuracy of mass and body length estimation is improved by 19.80% and 13.79%, respectively.

源语言英语
文章编号5101016
期刊IEEE Transactions on Geoscience and Remote Sensing
63
DOI
出版状态已出版 - 2025

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引用此

Hu, C., Zhang, F., Li, W., Wang, R., Wang, J., & Liu, H. (2025). Estimating Morphological Parameters of Insects in Nonhorizontal Flight Attitudes Based on Scattering Matrix Reconstruction. IEEE Transactions on Geoscience and Remote Sensing, 63, 文章 5101016. https://doi.org/10.1109/TGRS.2025.3528047