Insect Density Statistics Method Based on Nearest Neighbor Association and Gaussian Beam Volume

Abstract

The invention discloses an insect density statistical method based on the nearest neighbor method association and Gaussian beam volume, and provides an effective means for accurately counting the density of migrating insects at each altitude. Compared with the existing insect radar density statistics method, this method associates the same insect appearing at different scanning wave positions through the nearest neighbor method, which effectively prevents multiple statistics of the same insect and reduces the false alarm rate of detection ; The radar detection volume is calculated by the Gaussian beam, and the radar detectable area corresponding to different RCS insects is considered to be different, so the volume calculation is more reasonable.

Description

technical field [0001] The invention belongs to the technical field of insect radar, and relates to an insect density statistics method based on the association of the nearest neighbor method and Gaussian beam volume. Background technique [0002] Insect migration is a very important biological phenomenon in entomology. Research on insect migration can help humans improve their understanding of nature, and enhance early warning and control of pests and diseases. Obtaining characteristic parameters such as time, species, direction, height and density of insect migration is of great significance for the study of insect migration. [0003] During insect migration, stratification occurs. In biology, we often want to know the distribution density of insects at various altitudes, so as to understand the migratory habits and even living habits of insects. As a powerful tool for detecting high-altitude insect migration, insect radar can quickly and efficiently complete the work o...

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Problems solved by technology

[0005] However, there are two problems with this method: first, in the scanning radar detection, the calculation of the detectable volume of the radar is very complicated. For the targets of different RCS, the farthest detection distance of the radar is different, so the difference in the detectable volume is very large

The traditional method only uses the average RCS to uniformly calculate the detectable volume, and the error is very large. Second, the number of insects is counted: because the RCS of large insects is relatively large, the detection distance of the radar is relatively long, so the farthest detection range of the radar is relatively large. The distance corresponds to a larger

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