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Insect density statistical based on nearest neighbor correlation and the Gaussian beam volume

A nearest neighbor and Gaussian beam technology, applied in the field of insect radar, can solve the problems of long radar detection distance, large gap in detectable volume, complex calculation of radar detectable volume, etc., achieve reasonable volume calculation and reduce false alarm rate.

Active Publication Date: 2018-12-18
BEIJING INSTITUTE OF TECHNOLOGYGY
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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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  • Insect density statistical based on nearest neighbor correlation and the Gaussian beam volume
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  • Insect density statistical based on nearest neighbor correlation and the Gaussian beam volume

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[0038] The present invention will be described in detail below with reference to the drawings and embodiments.

[0039] The invention uses a two-dimensional scanning insect radar to observe migrating insects, uses the nearest neighbor method to correlate radar data, counts the number of insects, and then uses Gaussian beam rotation integral to calculate the radar scan volume, and divides the number of insects by the corresponding volume to obtain the insect density.

[0040] Scanning radar generally discretizes 0°-90° elevation angles, and the radar realizes airspace scanning through continuous azimuth rotation at each elevation angle. It should be noted that the present invention calculates the density of insects in a scanning mode with a fixed elevation angle or a radar at multiple elevation angles, and the beams at each elevation angle do not overlap.

[0041] Step 1: Statistics of the number of insects

[0042] The key to quantitative statistics lies in the detection and associati...

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Abstract

The invention discloses an insect density statistical method based on the nearest neighbor correlation method and the Gaussian beam volume, which provides an effective method for accurately counting the density of each height layer of migrating insect. Compared with the existing methods of insect radar density statistics, this method correlates the same insect at different scanning wave positionsby the nearest neighbor method, which can effectively prevent the same insect from being counted for many times, and reduce the false alarm rate of detection. Gaussian beam is used to calculate the detection volume of radar, and the different radar detectable regions of different RCS are taken into account, 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 statistical method based on the nearest neighbor method correlation and Gaussian beam volume. Background technique [0002] Insect migration is a very important biological phenomenon in entomology. Research on the migration of insects can help humans improve their understanding of nature, and enhance the early warning and prevention of pests and diseases. Obtaining characteristic parameters such as the time, type, direction, height and density of insect migration is of great significance to the study of insect migration. [0003] During the migration of insects, stratification occurs. In biology, one often wants to know the distribution density of insects at various heights in order to understand their migration and life habits. Insect radar, as a powerful tool for detecting high-altitude insect migration, can quickly and efficiently complete the work of insect de...

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Application Information

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IPC IPC(8): G06M11/02G06F17/30
CPCG06M11/02
Inventor 王锐胡程蔡炯龙腾曾涛张天然
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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