Radar Measurements of Morphological Parameters and Species Identification Analysis of Migratory Insects

Abstract

Migratory insect identification has been concerning entomology and pest managers for a long time. Their nocturnal behavior, as well as very small radar cross-section (RCS), makes individual detection challenging for any radar network. Typical entomological radars work at the X-band (9.4 GHz) with a vertical pencil beam. The measured RCS can be used to estimate insect mass and wingbeat frequency, and then migratory insects can be categorized into broad taxon classes using the estimated parameters. However, current entomological radars cannot achieve species identification with any higher precision or confidence. The limited frequency range of current insect radars have precluded the acquisition of more information useful for the identification of individual insects. In this paper, we report an improved measurement method of insect mass and body length using a radar with many more measurement frequencies than current entomological radars. The insect mass and body length can be extracted from the multi-frequency RCSs with uncertainties of 16.31% and 10.74%, respectively. The estimation of the thorax width and aspect ratio can also be achieved with uncertainties of 13.37% and 7.99%, respectively. Furthermore, by analyzing the statistical data of 5532 insects representing 23 species in East China, we found that the correct identification probabilities exceed 0.5 for all of the 23 species and are higher than 0.8 for 15 of the 23 species under the achievable measurement precision of the proposed technique. These findings provide promising improvements of individual parameter measurement for entomological radars and imply a possibility of species identification with higher precision.

Monitoring aerial insect biodiversity: a radar perspective

“…Individual mass is the most important single feature differentiating insect radar signals [ 69 ], which has been used for echo selection [ 63 ] and to infer species identity in combination with aerial trapping [ 70 ]. Parameters related to body shape have been used to select echoes from morphologically distinct taxa such as ladybird beetles and hoverflies, particularly in systems where the composition of aerial taxa has been well-described.…”

“…Machine learning approaches to differentiate insects based on a limited set of morphological parameters are rapidly improving and, e.g. allowed identifying 23 Lepidoptera and Odonata species in an aerial community in East China with a probability greater than 0.5 [ 69 ]. The importance of various characteristics in differentiating taxa depends on type and range of taxa being classified.…”

Design of Insect Target Tracking Algorithm in Clutter Based on the Multidimensional Feature Fusion Strategy

“…A key problem of multitarget tracking in cluttered environments is the uncertainty of measurement-to-track data associations, which means it is difficult to correctly determine the sources of measurements by only relying on kinematic information (target position and velocity). According to the previous literature [24,25], it can be concluded that an insect's polarization pattern and scattering matrix (SM) are closely related to the insect's geometry and composition, which can be measured using a fully polarimetric radar. This type of radar can measure all four linear-polarization receive-transmit (HH, HV, VH, VV) backscattering combinations.…”