Migratory Insect Multifrequency Radar Cross Sections for Morphological Parameter Estimation

A - Papers appearing in refereed journals

Wang, R., Hu, C., Liu, C., Long, T., Kong, S., Lang, T., Gould, P. J. L., Lim, J. and Wu, K. 2018. Migratory Insect Multifrequency Radar Cross Sections for Morphological Parameter Estimation. IEEE Transactions on Geoscience and Remote Sensing. 57 (6), pp. 3450-3461.

AuthorsWang, R., Hu, C., Liu, C., Long, T., Kong, S., Lang, T., Gould, P. J. L., Lim, J. and Wu, K.
Abstract

Insect migration provides major ecosystem services, and sometimes, migratory pests cause serious crop damage and yield loss. Species identification is critically important in studies of insect migration, for both entomologists and pest managers. Radar is an effective means of detecting insect migrants. Current entomological radars usually operate at X-band, and signal amplitude information is used to estimate body mass and wing-beat frequency, which can then be used to categorize migratory insects into broad taxon classes. To improve the identification performance, this paper presents a novel radar method of measuring insect mass and body length. The multifrequency radar cross sections (RCS) of insects at X-band and Ku-/K-band are fully investigated, and the comprehensive relationship between RCS and insect morphological parameters provides an improvement in the estimation of insect mass. More importantly, estimations of body length can also be realized with an accuracy of 84% based on experimental data acquired by a vector network analyzer in a microwave anechoic chamber. If multifrequency RCS measurements can be obtained by radar in the future, then highly accurate estimations of insect mass and body length will be possible, although it is currently still a challenge to build a radar capable of making the required measurements over such a wide frequency range.

KeywordsFeature extraction; Migratory insect; Multifrequency; Radar cross section (RCS)
Year of Publication2018
JournalIEEE Transactions on Geoscience and Remote Sensing
Journal citation57 (6), pp. 3450-3461
Digital Object Identifier (DOI)doi:10.1109/TGRS.2018.2884926
Web address (URL)https://ieeexplore.ieee.org/document/8585127
Open accessPublished as non-open access
FunderNational Natural Science Foundation of China
Publisher's version
File Access Level
Restricted
Output statusPublished
Publication dates
OnlineDec 2018
PublisherInstitute of Electrical and Electronics Engineers Inc (IEEE)
ISSN0196-2892

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