Embodiments are generally directed to determination of mobile display position and orientation using micropower impulse radar. An embodiment of an apparatus includes a display to present images; radar components to generate radar signal pulses and to generate distance data based on received return signals; radar antennae to transmit the radar signal pulses and to receive the return signals; and a processor to process signals and data, wherein the processor is to: process the return signals received by the radar antennae to determine a position and orientation of the display with respect to real objects in an environment and to determine a position of a vantage point of a user of the apparatus, and generate an augmented image including rendering a virtual object and superimposing the virtual object on an image including one or more real objects, the rendering of the virtual image being based at least in part on the determined position and orientation of the display and the determined vantage point of the user of the apparatus.

The disclosure herein generally relates to the field of determination of cardiopulmonary signals for multi-persons, and, more particularly, to determination of cardiopulmonary signals for multi-persons using in-body signals obtained by ultra-wide band (UWB) radar. The disclosed method determines of cardiopulmonary signals for multi-persons using in-body signals, wherein a UWB radar signals/waves reflected from inside a human body is utilized for efficient determination of cardiopulmonary signals. The disclosed method and system utilize the UWB radar signals to identify a number of persons along with several details about the persons that include a girth of the each identified person and the orientation of the identified person towards the one or more UWB radar. Further a chest wall distance, a breathing rate, a heart wall distance and a heart rate are determined for all the identified persons based on the identified girth and the identified orientation along with the UWB radar signals.

A method of designing a radar system includes implementing a supervised learning process of a neural network to determine a weight corresponding with each of a plurality of patch antennas of a radar system. Each of the plurality of patch antennas is sized in accordance with the weight.

A method of designing a radar system includes implementing a supervised learning process of a neural network to determine a weight corresponding with each of a plurality of patch antennas of a radar system. Each of the plurality of patch antennas is sized in accordance with the weight.

A radar system includes a transmitter, a receiver, and a processor. The transmitter transmits continuous wave radio signals. The receiver receives radio signals that includes the transmitted radio signal reflected from targets in an environment. The targets include a first target and a second target. The first target is closer than a first threshold distance from the vehicle, and the second target is farther than the first threshold distance from the vehicle. A processor is configured to process the received radio signals. The processor is configured to selectively process the received radio signals to detect the second target. The processor selectably adjusts operational parameters of at least one of the transmitter and the receiver to discriminate between the first target and the second target.

A radar system includes a transmitter, a receiver, and a processor. The transmitter transmits continuous wave radio signals. The receiver receives radio signals that includes the transmitted radio signal reflected from targets in an environment. The targets include a first target and a second target. The first target is closer than a first threshold distance from the vehicle, and the second target is farther than the first threshold distance from the vehicle. A processor is configured to process the received radio signals. The processor is configured to selectively process the received radio signals to detect the second target. The processor selectably adjusts operational parameters of at least one of the transmitter and the receiver to discriminate between the first target and the second target.

A precise cardiac data reconstruction method is provided, which may also be referred to herein as radar cardiography (RCG). RCG can reconstruct cardiac data, such as heart rate and/or electrocardiogram (ECG)-like heartbeat waveform signals wirelessly by using advanced radar signal processing techniques. For example, heartbeat and related characteristics can be monitored by isolating cardiovascular activity from strong respiratory interference in spatial spaces: azimuth and elevation. This results in significant improvements to pulse signal-to-noise-ratio (SNR) compared to conventional approaches, facilitating heart-rate variability (HRV) analysis.

A precise cardiac data reconstruction method is provided, which may also be referred to herein as radar cardiography (RCG). RCG can reconstruct cardiac data, such as heart rate and/or electrocardiogram (ECG)-like heartbeat waveform signals wirelessly by using advanced radar signal processing techniques. For example, heartbeat and related characteristics can be monitored by isolating cardiovascular activity from strong respiratory interference in spatial spaces: azimuth and elevation. This results in significant improvements to pulse signal-to-noise-ratio (SNR) compared to conventional approaches, facilitating heart-rate variability (HRV) analysis.

A method of designing a radar system includes implementing a supervised learning process of a neural network to determine a weight corresponding with each of a plurality of patch antennas of a radar system. Each of the plurality of patch antennas is sized in accordance with the weight.

A method of designing a radar system includes implementing a supervised learning process of a neural network to determine a weight corresponding with each of a plurality of patch antennas of a radar system. Each of the plurality of patch antennas is sized in accordance with the weight.