Estimating Morphological Parameters of Insects in Nonhorizontal Flight Attitudes Based on Scattering Matrix Reconstruction
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 paper proposes a novel method for estimating the morphological parameters of insects in non-horizontal 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 non-horizontal attitudes to a horizontal attitude, we achieve the estimation of morphological parameters for insects in non-horizontal attitudes. The effectiveness of the proposed method is validated using a fully-polarimetric multi-angle observation dataset of 33 insects from 6 species measured in a microwave anechoic chamber. The mean relative errors in estimating the mass and length of the insects across 20 different observation angles are 20.06% and 12.85%. 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.
Authors
- J. Wang (ORCID: https://orcid.org/0009-0004-2555-6205)
- Cheng Hu (ORCID: https://orcid.org/0000-0001-7582-5291)
- Fan Zhang (ORCID: https://orcid.org/0000-0002-0018-8782)
- Weidong Li (ORCID: https://orcid.org/0000-0002-1774-7800)
- Rui Wang (ORCID: https://orcid.org/0000-0002-3510-7356)
- Haibo Liu (ORCID: https://orcid.org/0000-0002-4213-2883)
Institutions
- Beijing Institute of Technology (CN)
Publication Details
- Journal
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tgrs.2025.3528047
- Citations
- 1
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 1.75
Funders
- National Defense Basic Scientific Research Program of China