Single radar echo quality control method and system for removing signal interference clutter, and terminal

Abstract

The invention belongs to the technical field of radar echo quality control, and discloses a single radar echo quality control method and system for removing signal interference clutters and a terminal, and the method comprises the steps: carrying out the recognition and marking of a streaming cloud echo and a streaming cloud edge echo, and carrying out the backup storage of all observation polarization amounts at corresponding positions; constructing a signal interference clutter identification model based on polarization characteristics according to manual observation summarization, and performing suspected signal interference clutter identification; the method comprises the following steps of: identifying ground clutters and signal interference clutters mixed with the ground clutters by utilizing polarization quantity characteristic analysis on the premise of ensuring maximum protection of flow cloud echoes, and removing the clutters marked as the ground clutters and suspected signal interference clutters. According to the method, signal interference clutters can be accurately identified and processed, under the condition that clutters and severe convection exist in a mixed mode, accurate identification can still be conducted through the technology, the single radar echo data quality can be greatly improved, and basic technical support is provided for radar networking.

Description

technical field [0001] The invention belongs to the technical field of radar echo quality control, and in particular relates to a single radar echo quality control method, system and terminal for removing signal interference clutter. Background technique [0002] At present, the traditional radar echo quality control methods usually include: reference section algorithm, fuzzy logic method, neural network algorithm, etc. These methods are suitable for removing general ground clutter, sea clutter, super-refraction clutter and many more. The step-by-step super-refractive ground object echo recognition method based on fuzzy logic (Liu Liping, 2007) can better identify the ground object echo and precipitation echo, but because the method uses radial velocity and spectral width values ​​( The effective range of numerical values ​​is generally less than 150km), which leads to the quality control range being smaller than the effective reflectance detection range (230km); and this m...

AI Technical Summary

Problems solved by technology

Signal interference clutter is somewhat similar to general solar ray clutter in terms of reflectivity, most of which present local ray-like features, but the obvious differences are: 1) Although its overall intensity is not too strong, the local The intensity will be stronger, close to 40dBZ, which is close to the edge intensity of strong convective echo; when it is mixed with real rainfall cloud echo, especially when mixed with strong convective cloud, it is extremely difficult to accurately identify of

2) The time and location of its occurrence are affected by signal interference sources (from a business point of view, many fixed signal interference sources around radars will exist for a long time due to some objective factors and cannot be completely removed), and there are extremely large randomness

3) When the signal interference clutter exists, its clutter shape and local intensity characteristics will also show irregular changes over time. Therefore, this further increases the difficulty of building a clutter identification model

[0005] (1) The traditional radar echo quality control method has great uncertainty in the removal of signal interference clutter

[0006] (2) When signal interference clutter and real rainfall cloud echoes are mixed, especially when mixed with strong convective clouds, accurate identification is extremely difficult

[0007] (3) The time and location of the signal interference clutter are affected by the signal interference source, and there is great randomness; the signal interference clutter exists for a period of time, and its clutter shape and local intensity characteristics will also appear over time. irregular change features, which increase the difficulty of constructing the clutter recognition model

[0008] The difficulty in solving the above problems and defects is: the traditional method has great uncertainty in the ability to remove signal interference clutter

The signal interference clutter is somewhat similar to the general solar ray clutter in terms of reflectivity, most of which present local ray-like features, but the obvious differences are: 1) Although its overall intensity is not too strong, but The local intensity will be stronger, close to 40dBZ, which is close to the edge intensity of strong convective echo; when it is mixed with real rainfall cloud echo, especially when mixed with strong convective cloud, accurate identification is extremely important difficult

2) The time and location of its occurrence are all affected by signal interference sources (from a business point of view, some fixed signal interference sources around radars will exist for a long time due to some objective factors and cannot be completely removed), and there are extremely large randomness

3) When the signal interference clutter exists, its clutter shape and local intensity characteristics will also show irregular changes over time. Therefore, this further increases the difficulty of building a clutter identification model

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