High-Resolution and Low Blind Range Waveform for Migratory Insects’ Taking-Off and Landing Behavior Observation

Abstract

The observation of taking-off and landing behaviors of migratory insects is important for pest early monitoring and forecasting. Entomological radar, which can measure the ascent or descent rates remotely, has been proved to be the most effective way to observe the above behaviors. For the current entomological radars, the highest range resolution of 7.5 m and smallest blind range of 150 m make it difficult to distinguish individual insects in a swarm and observe the migratory behaviors at low altitudes. In this paper, based on the outfield data acquired from a high-resolution radar of 0.2 m, the spatial spacing distribution of migratory insects is presented for the first time and waveform design requirements are analyzed, proving the necessity of a higher resolution. Secondly, for the high-resolution and low blind range observation of insects, the stepped frequency train of LFM pulses (also called frequency-jumped burst, FJB) waveform is discussed as the optimal choice. In order to resolve the high grating-lobe problem in the FJB waveform with the low blind range, the precise spectrum model and its approximation of the LFM subpulse are first derived in detail. Then, according to the obtained spectrum characteristics, the high-resolution and low blind range FJB waveform design methods based on spectrum fluctuation period and Fresnel integral windowing are proposed to reduce the grating-lobe number to more than 50% and suppress the highest grating lobe level by at least 4 dB. Finally, based on the high-resolution and low blind range VLR adopting the proposed waveform, several typical taking-off and landing behavior observation results are presented, which proves that, in their migratory pattern, insects usually take off around sunset.

Monitoring aerial insect biodiversity: a radar perspective

“…Radars can deliver unique information on insect EBVs that complements other approaches, and their use is expanding with promising current developments-from developments in radar technology that measure more features of aerial objects, machine learning and neural network algorithms that use these novel features to improve taxonomic classification, harmonization of data across radar systems and integration with other data sources, to efforts in providing data infrastructure for improved availability and access. While small-scale radars keep on being developed further [92,93], developments in 'frequency-modulated continuous wave' radars hold much promise for general insect biodiversity and biomass monitoring as they increase capabilities of taxonomic differentiation and extend the measurement range to much closer to the ground.…”

“…While small-scale radars keep on being developed further [ 92 , 93 ], developments in ‘frequency-modulated continuous wave' radars hold much promise for general insect biodiversity and biomass monitoring as they increase capabilities of taxonomic differentiation and extend the measurement range to much closer to the ground.…”