Precipitation Particle Classification via Multi-Radar Fuzzy Integration
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Solution Overview
Problem
Existing precipitation particle classification methods using dual polarization radar devices only provide classification results from a single radar device and do not effectively integrate data from multiple radar devices, leading to incomplete and inaccurate classification in overlapping scanning areas.
Innovation Solution
A precipitation particle classification apparatus and method that utilize a processing circuitry to receive signals from multiple radar devices, calculate polarization parameters, and apply fuzzy inference to determine the degree of attribution for each type of precipitation particle, converting polar coordinate distribution evaluation values to Cartesian coordinates, integrating these values to produce a composite evaluation for accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classification results are integrated from multiple radar devices, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces a coordinate conversion unit as an intermediary component that transforms polar coordinate distribution evaluation values from multiple radar devices into Cartesian coordinate system. This mediator enables the integration of data from different radar devices by unifying their coordinate systems, thereby improving measurement precision while managing system complexity through a dedicated conversion mechanism
Solution Approach 2:
The patent merges the distribution evaluation values from multiple radar devices by converting them to a common Cartesian coordinate system and integrating them. This combining approach allows the system to leverage data from multiple radar devices to improve classification accuracy in overlapping scanning areas while maintaining a unified processing framework
2Manufacturing precision
If fuzzy inference is applied to calculate distribution evaluation values, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The patent applies fuzzy inference to calculate polar coordinate distribution evaluation values as a preliminary step before coordinate conversion and integration. By performing the fuzzy inference calculation upfront for each radar device's data, the system prepares refined evaluation values that can be efficiently converted and merged, improving overall classification precision while managing processing time through staged computation
Data Source
AI summary
To provide a precipitation particle classification apparatus for obtaining a proper classification result of precipitation particles based on information from a plurality of radar devices. The precipitation particle classification apparatus includes a data processing part, a fuzzy processing part, a coordinate conversion part, an interpolation part, and a classification part. The data processing part acquires polarization parameters obtained by reflection on the precipitation particles from each of the plurality of radar devices which are arranged at different positions and have a part of a scanning area overlapping with each other. The fuzzy processing part obtains a polar coordinate distribution evaluation value indicating the distribution in polar coordinates of an evaluation value indicating the degree of attribution to each type of precipitation particles from polarization parameters by using a fuzzy inference. The coordinate conversion part converts the polar coordinate distribution evaluation value into the Cartesian coordinate distribution evaluation value. The interpolation part integrates the Cartesian coordinate distribution evaluation values whose positions on the coordinates are substantially equal among the Cartesian coordinate distribution evaluation values obtained for each of the plurality of radar devices to obtain a composite evaluation value. The classification part classifies precipitation particle species based on the composite evaluation value.