A method is disclosed for suppressing sidelobes due to artifacts introduced by FFT operations during automotive radar signal processing. Sidelobes of a stronger target from the FFT operations may bury the response from a weaker target when there are multiple targets. The method estimates the sidelobes of an identified target from a measured FFT response and subtracts the estimated sidelobes from the measured FFT response. The identified target may be the strongest target from the measured FFT response. The method estimates the sidelobes to suppress the sidelobes with respect to the peak signal of the identified target. After the estimated sidelobes of the identified target are removed, the updated FFT response may reveal other targets that had been buried. The method may identify additional targets to estimate their sidelobes and may iteratively remove the estimated sidelobes of additional targets from the FFT until a desired sidelobe residual level is achieved.

Systems and methods for weather radar processing, comprising a processor in communication with a first database and a second database and computer system code executed by the processor. The computer system code causes the processor to ingest radar data from the first database and ingest numerical weather prediction data from the second database. The processor further processes the radar data and the numerical weather prediction data to generate weather products on defined tiles. The processor further processes the weather products on the defined tiles to generate full domain (stitch-tile) data and generates time aggregation data based on the stitch-tile data. The processor further generates a final model using the time aggregation data.

Systems and methods for weather radar processing, comprising a processor in communication with a first database and a second database and computer system code executed by the processor. The computer system code causes the processor to ingest radar data from the first database and ingest numerical weather prediction data from the second database. The processor further processes the radar data and the numerical weather prediction data to generate weather products on defined tiles. The processor further processes the weather products on the defined tiles to generate full domain (stitch-tile) data and generates time aggregation data based on the stitch-tile data. The processor further generates a final model using the time aggregation data.

Methods for training a model for use in monitoring a health parameter in a person are disclosed. In an embodiment, a method involves monitoring a blood pressure of a person using a control blood pressure monitoring system, receiving control data that corresponds to the monitoring using the control blood pressure monitoring system, receiving stepped frequency scanning data that corresponds to radio waves that have reflected from blood in a blood vessel of the person, wherein the stepped frequency scanning data is collected through multiple receive antennas over a range of frequencies, generating training data by combining the control data with the stepped frequency scanning data in a time synchronous manner, and training a model using the training data to produce a trained model, wherein the trained model correlates stepped frequency scanning data to values that are indicative of a blood pressure of a person.

Methods for training a model for use in monitoring a health parameter in a person are disclosed. In an embodiment, a method involves monitoring a blood pressure of a person using a control blood pressure monitoring system, receiving control data that corresponds to the monitoring using the control blood pressure monitoring system, receiving stepped frequency scanning data that corresponds to radio waves that have reflected from blood in a blood vessel of the person, wherein the stepped frequency scanning data is collected through multiple receive antennas over a range of frequencies, generating training data by combining the control data with the stepped frequency scanning data in a time synchronous manner, and training a model using the training data to produce a trained model, wherein the trained model correlates stepped frequency scanning data to values that are indicative of a blood pressure of a person.

A signal cutting-out unit cuts out a signal reflected from a target by a specific length while the cut-out signals are overlapped by a set length. A power spectrum calculating unit calculates a plurality of power spectra from output signals from the signal cutting-out unit. A power spectrum reconstructing unit performs power spectrum reconstruction by changing a ratio or contribution rate of power spectrum components in a set region, using the plurality of power spectra. A spectral moment calculating unit calculates a spectral moment from a power spectrum reconstructed by the power spectrum reconstructing unit.

In an azimuth estimation device, a center generation unit configured to generate, for each peak bin extracted by the extraction unit, a center matrix which is a correlation matrix obtained using values of the same peak bin collected from all of transmitting/receiving channels. A surrounding generation unit is configured to generate, for each of one or more surrounding bins of each of the peak bins, a surrounding matrix which is a correlation matrix obtained using values of the same surrounding bin collected from all of the transmitting/receiving channels. An integration unit is configured to generate, for each peak bin, an integrated matrix which is a correlation matrix obtained by weighting and adding the center matrix and the one or more surrounding matrices. An estimation unit is configured to execute an azimuth estimation calculation using the integrated matrix generated by the integration unit.

The invention relates to a method for determining at least one parameter of an object, wherein the method comprises the following steps: a. provision of a range-Doppler matrix, b. transfer of at least one part of the range-Doppler matrix to a neural network and c. identification of the at least one parameter by the neural network.

A high-throughput communications channel is encoded using transmit waveforms which satisfy a variety of technical constraints deemed desirable for effective radar operations and signal processing. This enables new cooperative spectrum sharing modalities for radar and communications systems.

A high-throughput communications channel is encoded using transmit waveforms which satisfy a variety of technical constraints deemed desirable for effective radar operations and signal processing. This enables new cooperative spectrum sharing modalities for radar and communications systems.