An apparatus and a method for estimating rainfall of hail and rain using a dual-polarization weather radar improve accuracy of classification of hail and rain zones and estimation of rainfall intensity by classifying hail and rain zones using a distribution of horizontal reflectivity and differential reflectivity of radar observation values, discriminating between a convective zone and a stratiform zone depending on reflection intensity, and applying a dual-polarization-based rainfall estimating relational equation for each type in a weighted mean technique.

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 synthetic aperture radar (SAR) system generates an image of a first swath. The SAR includes at least one SAR antenna, at least one SAR processor and at least one SAR transceiver. In operation the SAR defines a first beam to illuminate the first swath and one or more second beams to illuminate area(s) of ambiguity associated with the first beam. The SAR transmits a pulse via the first beam and receives backscatter energy. The SAR generates a first signal associated with the first beam and one or more second signals associated with the second beam(s). The second signal(s) are combined with determined complex vector(s), generating ambiguity signal(s) and the ambiguity signals are combined with the first signal to generate an image associated with the first swath.

An ultra-wideband dual-polarized tightly coupled bowtie antenna array for ground-based polar ice sounding radar is described. The antenna array has a very large effective aperture to increase the directivity. At the same time, it is lightweight and low profile to minimize the payload and maximize the survey range. In an implementation, the antenna array operates between 180-620 MHz with a fractional bandwidth of 3.4:1. The broadband performance benefits from the tightly coupled antenna elements. A feature of the antenna array is the planar feeding structure without balun. The antenna array element has the microstrip feeding line integrated with one arm of the bowtie antenna. The other arm is directly fed by the microstrip line. By adding a ferrite core around the coax cable for common mode suppression, the bowtie antenna element can be fed differentially without using bulky vertical feeding structure and balun.

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.

An apparatus and method for orthogonal rotation of a radiation E-field polarization rely on a radiating element including an offset-ridge waveguide and a single-mode first ridge waveguide functionally adjacent to the offset-ridge waveguide.

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.

A method is provided for determining a road condition by using a radar system having transmitter and receiving units for transmitting and receiving radar waves having two different polarizations and providing transmit and receive signals indicating an intensity of the transmitted and received radar waves. Co-polarized backscattering coefficients and at least one cross-polarized backscattering coefficient are determined based on the transmit and receive signals. If the cross-polarized backscattering coefficient is greater than or equal to a threshold, the road condition is determined based on a ratio of the co-polarized backscattering coefficients and based on a difference of one of the co-polarized backscattering coefficients and the cross-polarized backscattering coefficient. If the cross-polarized backscattering coefficient is smaller than the threshold, the road condition is determined based on the ratio and a difference of the co-polarized backscattering coefficients.

Synthetic aperture radar transmit and receive antenna systems and methods of transmitting and receiving radar signals are disclosed. In one embodiment, a transmit and receive antenna system includes a transmit antenna array configured to transmit a plurality of radio frequency transmit signals, the transmit antenna array including a plurality of patch antenna elements mounted to a printed circuit board, each patch antenna element belonging to a subarray, and one or more power amplifiers, each power amplifier feeding a subarray of the patch antenna elements, and a reflectarray receive antenna configured to receive radio frequency signals including a plurality of reflectarray antenna elements mounted to a printed circuit board, at least one antenna feed configured to receive radio frequency signals reflected from the plurality of reflectarray antenna elements, and at least one low noise amplifier electrically connected to the at least one antenna feed.

A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing deep learning. More specifically, the system and method produce predictions of MCI conversions to Alzheimer's/dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is a deep learned model trained using transfer learning. An MCI-DAP server may then receive a request from a clinician to process predictions related to a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.