Systems and methods for detecting and monitoring human breathing, respiration, and heart rate using statistics about the wireless channel between two or more connected devices. A user is monitored for identifying patterns in the user's behavior that may allow the system to alert a caregiver to deviations in the user behavior that may be indicative of a potential issue, such as depression. A presence may further detected in a sensing area through the detection of spectral components in the breathing frequency range of comprises user includes transforming phase difference between spatial streams and amplitude of the samples representing frequency response of the channel for any frequency value into frequency domain to perform frequency analysis. Statistical analysis may be performed on the frequency space provided by the transformation. Micro motions may also be detected by detecting presence in a sensing area through the detection of spectral components in the micro motion frequency range.

Remote recovery of acoustic signals from passive sources is provided. Wideband radars, such as ultra-wideband (UWB) radars can detect minute surface displacements for vibrometry applications. Embodiments described herein remotely sense sound and recover acoustic signals from vibrating sources using radars. Early research in this domain only demonstrated single sound source recovery using narrowband millimeter wave radars in direct line-of-sight scenarios. Instead, by using wideband radars (e.g., X band UWB radars), multiple sources separated in ranges are observed and their signals isolated and recovered. Additionally, the see-through ability of microwave signals is leveraged to extend this technology to surveillance of targets obstructed by barriers. Blind surveillance is achieved by reconstructing audio from a passive object which is merely in proximity of the sound source using clever radar and audio processing techniques.

An electronic device includes a transmission antenna that transmits a transmission wave, a reception antenna that receives a reflected wave that is the transmission wave having been reflected, and a control unit that detects an object that reflects the transmission wave, based on a transmission signal transmitted as the transmission wave and a reception signal received as the reflected wave. The control unit performs control to detect, as a target, an object having a motion characteristic of a motion of an arm of a person, among objects located around the electronic device.

A polarimetric radar system for object classification and road condition estimation includes a radar transmitter unit for transmitting radar waves of different polarizations, a radar receiving unit for receiving radar waves of different polarizations, a radar signal generating unit for generating and providing the radar waves to be transmitted, a signal processing circuitry for processing the generated and received radar waves, and a signal evaluation unit. The signal evaluation unit receives processed signals from the signal processing circuitry, estimates values for a set of predetermined object parameters on the basis of the received processed signals, and selects an object class from a plurality of predetermined object classes upon detecting a match of the estimated values with one out of a plurality of predetermined sets of object parameters. The signal evaluation unit is configured to provide information that is indicative of the at least one classified object.

In an embodiment, a method includes: receiving reflected radar signals with a millimeter-wave radar; performing a range discrete Fourier Transform (DFT) based on the reflected radar signals to generate in-phase (I) and quadrature (Q) signals for each range bin of a plurality of range bins; for each range bin of the plurality of range bins, determining a respective strength value based on changes of respective I and Q signals over time; performing a peak search across the plurality of range bins based on the respective strength values of each of the plurality of range bins to identify a peak range bin; and associating a target to the identified peak range bin.

An electronic device for gesture recognition, includes a processor operably connected to a transceiver. The transceiver is configured to transmit and receive signals for measuring range and speed. The processor is configured to transmit the signals, via the transceiver. in response to a determination that a triggering event occurred, the processor is configured to track movement of an object relative to the electronic device within a region of interest based on reflections of the signals received by the transceiver to identify range measurements and speed measurements associated with the object. The processor is also configured to identify features from the reflected signals, based on at least one of the range measurements and the speed measurements. The processor is further configured to identify a gesture based in part on the features from the reflected signals. Additionally, the processor is configured to perform an action indicated by the gesture.

Training a machine learning neural network (MLNN) in radiowave based monitoring of fall characteristics in diagnosing injury. The method comprises receiving, in a first set of input layers of the MLNN, from a millimeter wave (mmWave) radar sensing device, a set of mmWave radar point cloud data representing fall attributes associated with a subject, each of the first set associated with a respective fall attribute; receiving, at a second set of input layers of the MLNN, a set of personal attributes of the subject, training a MLNN classifier based on supervised training that establishes a correlation between an injury condition of the subject as generated at the output layer, the mmWave point cloud data, and personal attributes; and adjusting an initial matrix of weights by backpropagation to increase correlation between the injury condition, the mmWave point cloud data, and the personal attributes.

Trapped or confined individuals may be located and rescued by detecting their movement using reflected, radio frequency signals over a range of multiple antenna polarities.

A method with grid map generation includes: determining position information of a moving object corresponding to a first time step based on a position sensor of the moving object; determining detection information of nearby objects present around the moving object corresponding to the first time step based on a radio detection and ranging (radar) sensor of the moving object; selecting a still object in a moving range of the moving object from among the nearby objects, based on the position information and the detection information; updating a point cloud determined based on the radar sensor in a previous time step of the first time step, based on the position information and on detection information of the still object comprised in the detection information of the nearby objects; and generating a grid map based on an occupancy probability for each grid of the updated point cloud.

Techniques and apparatuses are described that implement a distributed radar system. The distributed radar system includes two or more radar front-end circuits and at least one processor. The radar front-end circuits are distributed within a device at different positions. By partitioning antennas and transceivers across multiple radar front-end circuits instead of consolidating into a single integrated circuit, individual radar front-end circuits can have a smaller footprint than the single integrated circuit. This smaller footprint enables the radar front-end circuits to be integrated within space-constrained devices. The smaller footprint also provides additional flexibility in positioning the radar front-end circuits away from other components within the device that can cause interference. This can reduce the amount of interference seen by the distributed radar system.