The present disclosure discloses a system for echo intensity calibration based on continuous wave weather radar data. The system includes a communication module used for establishing a network protocol or local protocol-based communication link between a radar receiver and a radar terminal computer; a main control module which is in communication connection with the communication module to receive radar data of the radar receiver or a control signal of the radar terminal computer and execute an echo intensity calibration strategy; and a storage module which temporarily stores continuous wave weather radar data, and aircraft echo power values that are identified by the main control module within a period of time.

A reflectivity correction method using a double polarization variable-based bright band detection result includes a preprocessing operation for correcting a double polarization variable observation error and calculating a depolarization ratio; a fuzzy classifier generation operation for calculating a weighting and a membership function of each characteristic variable using a bright band height extracted from a quasi-vertical profile generated from specific elevation angle data, a bright band detection operation for detecting a bright band using a depolarization ratio and a fuzzy classifier for each elevation angle, and a reflectivity correction operation for correcting reflectivity over-observation for a detected bright band region on the basis of a correction factor calculated using an apparent profile of reflectivity generated by averaging reflectivity data for the bright band region for each elevation angle. Thus, it is possible to improve the accuracy of precipitation estimation by using the corrected reflectivity.

A reflectivity correction method using a double polarization variable-based bright band detection result includes a preprocessing operation for correcting a double polarization variable observation error and calculating a depolarization ratio; a fuzzy classifier generation operation for calculating a weighting and a membership function of each characteristic variable using a bright band height extracted from a quasi-vertical profile generated from specific elevation angle data, a bright band detection operation for detecting a bright band using a depolarization ratio and a fuzzy classifier for each elevation angle, and a reflectivity correction operation for correcting reflectivity over-observation for a detected bright band region on the basis of a correction factor calculated using an apparent profile of reflectivity generated by averaging reflectivity data for the bright band region for each elevation angle. Thus, it is possible to improve the accuracy of precipitation estimation by using the corrected reflectivity.

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.

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.

Methods and systems for generating a composite image in remote sensing applications are described. In an example, a device can receive an image having a plurality of points specified in an image space. The device can extract a portion of the image and transform points among the extracted portion from the image space to a parameter space defined by a distance parameter and an orientation parameter. The device can identify a set of intersection points in the parameter space that indicate at least one occurrence of a geometry feature in the extracted portion of the image. The device can augment the portion of the image with a plurality of new pixels based on the identified set of intersection points. The device can generate a composite image using the augmented image, where the composite image can include a plurality of augmented images corresponding to other portions of the image.

A radar apparatus with a transmission antenna array that outputs a high aspect ratio frequency modulation continuous wave (FMCW) transmission beam that illuminates a large field of regard in elevation and may be both electronically and mechanically scanned in azimuth. The weather radar apparatus includes a receive array and receive electronics that may receive the reflected return radar signals and digitally form a plurality of receive beams that may be used to determine characteristics of the area in the field of regard. The receive beams may be used to determine reflectivity of weather systems and provide a coherent weather picture. The weather radar apparatus may simultaneously process the receive signals into monopulse beams that may be used for accurate navigation as well as collision avoidance.

The learning device includes an acquisition unit, a learning data generation unit, and a learning processing unit. The acquisition unit acquires operation data generated during an operation of a radar device and the operation history data indicating operations performed by an operator on the radar device from the radar device. The learning data generation unit generates the learning data using the operation data and the operation history data. The learning processing unit learns an operation determination model that determines an operation to be performed on the radar device based on the operation data, using the learning data.

The learning device includes an acquisition unit, a learning data generation unit, and a learning processing unit. The acquisition unit acquires operation data generated during an operation of a radar device and the operation history data indicating operations performed by an operator on the radar device from the radar device. The learning data generation unit generates the learning data using the operation data and the operation history data. The learning processing unit learns an operation determination model that determines an operation to be performed on the radar device based on the operation data, using the learning data.

The present disclosure relates to spherical dual-polarization phased array weather radar. The spherical dual-polarization phased array weather radar comprises a spherical crown phased array antenna module, a digital transceiver module and a signal processing module, wherein the spherical crown phased array antenna module comprises a spherical support frame and a plurality of dual-polarization micro-strip radiation units; the dual-polarized micro-strip radiation units are tightly arranged on the spherical support frame; the spherical crown phased array antenna module is used for detecting weather; wireless transmission is carried out between the digital transceiver module and the spherical crown phased array antenna module; the digital transceiver module is used for generating a frequency modulation signal or a phase coding signal required for detecting meteorological targets and receiving an echo signal reflected by the target; and the signal processing module is connected with the digital transceiver module.