Embodiments of the disclosure include a Radar system for detecting presence of a living thing. The radar system includes a transmitter/receiver module configured to emit radio signals to an environment surrounding the Radar system and to detect returned radio signals from the environment, and a large-movement detection sub-module configured to determine a large movement of the living thing based on the returned radio signals. The radar system also includes a micro-movement detection sub-module configured to determine a micro movement of the living thing based on the returned radio signals, and a breath and heartbeat detection sub-module configured to determine a breath or heartbeat of the living thing based on the returned radio signals. The radar system further includes a presence detection sub-module configured to determine the presence of the living thing based on determinations received from the corresponding detection sub-modules.

In an embodiment, a method includes: receiving first radar data from a millimeter-wave radar sensor; receiving a set of hyperparameters with a radar processing chain; generating a first radar processing output using the radar processing chain based on the first radar data and the set of hyperparameters; updating the set of hyperparameters based on the first radar processing output using a hyperparameter selection neural network; receiving second radar data from the millimeter-wave radar sensor; and generating a second radar processing output using the radar processing chain based on the second radar data and the updated set of hyperparameters.

Provided is an object recognition device including a prediction processing unit, a temporary setting unit, and a association processing unit. The prediction processing unit predicts, as a prediction position on an object model obtained by modeling a tracking target, a position of a movement destination of the tracking target based on a trajectory formed by movement of at least one object of a plurality of objects as the tracking target. The temporary setting unit sets, based on specifications of a sensor that has detected the tracking target, a position of at least one candidate point on the object model. The association processing unit sets, based on the position of the candidate point and the prediction position, a reference position on the object model. The association processing unit determines whether the position of the detection point and the prediction position associate with each other based on a positional relationship between a association range which is set so that the association range has a reference position on the object model as a reference and a detection point at a time when the sensor has detected the at least one object of the plurality of objects.

The invention relates to a method for detecting at least one road user on a traffic route by means of a radar sensor and an optical detector, wherein with said method radar radiation is emitted by at least one radar transmitter of the radar sensor and reflected by the at least one road user, the reflected radar radiation is detected by means of at least one radar receiver of the radar sensor, the detected radar radiation is evaluated in such a way that at least one distance and one radial velocity of the at least one road user relative to the radar sensor is determined, an optical image of the at least one road user is detected by means of the optical detector, and the optical image is evaluation,
wherein at least one parameter of the at least one road user is determined both from the detected radar radiation and the optical image.

A method for exposure level estimation, includes transmitting radar signals for object detection and communication signals for wireless communication operations. The method also includes identifying a location of an object relative to the electronic device within a first time duration based on the radar signals, the first time duration including a previous time until a current time. The method further includes determining a radio frequency (RF) exposure measurement associated with the object based on the location of the object over the first time duration. Additionally, the method includes determining a power density budget over a second time duration based on a comparison of the RF exposure measurement to an RF exposure threshold, the second time duration including the current time until a future time. The method also includes modifying the wireless communication operations for the second time duration based on the power density budget.

A video recording and communication device includes a camera, a processor, a radar sensor, and memory. The processor executes instructions from memory to cause the device to operate the sensor in a first operational mode in which the camera is maintained in a low-power mode. The processor further detects, using the sensor in the first mode, possible motion of an object within a threshold distance from the sensor. Responsive to the detecting, the processor transitions the sensor from the first mode to a second operational mode. The second mode consumes more power than the first mode. The processor determines, using the radar sensor in the second mode, that the possible motion of the object occurred in a region of interest, in response to which, it transitions the sensor from the second mode to a third operational mode. The third mode consumes more power mode than the second mode.

A method for detecting a living being on a seat of a vehicle, further relating to a detection arrangement and to a vehicle. The method may include emitting electromagnetic waves at predetermined frequency or at a predetermined frequency band towards the seat by an electromagnetic radiator, receiving electromagnetic waves reflected on a surface by a sensor, detecting an object on the seat from a transit time of the emitted and the reflected electromagnetic waves between the radiator, the surface and the sensor by a detection device, detecting movements of the object from the reflected electromagnetic waves by the detection device if an object has been detected, determining from the detected movements of the object whether the detected object is a living being, and outputting a detection signal by way of the detection device if it has been determined that the detected object is a living being.

This document describes techniques for radio frequency (RF) based micro-motion tracking. These techniques enable even millimeter-scale hand motions to be tracked. To do so, radar signals are used from radar systems that, with conventional techniques, would only permit resolutions of a centimeter or more.

A sensor includes a transmit antenna, a receive antenna, circuitry, and a memory. The transmit antenna includes N transmit antenna elements each transmitting a transmit signal. The receive antenna includes M receive antenna elements each receiving N receive signals including reflection signals reflected by an organism. The circuitry extracts a second matrix corresponding to a predetermined frequency range from an N×M first matrix representing propagation characteristics between each transmit antenna element and each receive antenna element calculated from the receive signals. The circuitry estimates the position of the organism by using the second matrix, and calculates a radar cross-section value with respect to the organism, based on the estimated position and the positions of the transmit antenna and the receive antenna. The circuitry then estimates the posture of the organism by using the calculated radar cross-section value and information indicating associations between radar cross-section values and postures of the organism.

Methods, devices and instruction-carrying storage operate to track a target object over time and space. The tracking techniques involve obtaining a point cloud of reflection points at time n, a target from time n−1, state information including previous location information for the target and previous group distribution for previous reflection points associated with the target at time n−1; predicting a location of the target at time n based on the state information; determining a gate around the target and which of the multiple reflection points are within the gate; determining, for each of the multiple reflection points determined to be within the gate, a likelihood that the corresponding reflection point is associated with the target; determining current group distribution for the reflection points determined to likely be associated with the target; and outputting the determined current group distribution and current location information of the target.