Methods, apparatus and systems for periodic or transient motion detection, e.g. fall event detection, based on wireless signals are described. In one example, a described system comprises: a transmitter configured for transmitting a first wireless signal through a wireless multipath channel of a venue; a receiver configured for receiving a second wireless signal through the wireless multipath channel; and a processor. The second wireless signal differs from the first wireless signal due to the wireless multipath channel that is impacted by a target motion of an object in the venue. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, computing a time series of spatial-temporal information (STI) of the object based on the TSCI, and detecting the target motion of the object based on the time series of STI (TSSTI).

A target recognition device includes a target recognition unit, a tracking unit, a target registration unit, a condition determination unit, and a registration requirement setting unit. The condition determination unit determines whether a target satisfies predetermined conditions J1 to J3. The registration requirement setting unit sets a requirement for registering a target by the target registration unit to be stricter for the target determined to satisfy all of the conditions J1 to J3 than for the target determined to satisfy not all of the conditions J1 to J3.

An occupant detection device includes: a radio wave sensor located above a seat surface of a seat that is disposed in a vehicle cabin of a vehicle, in a vertical direction of the vehicle, and including a transmission unit configured to transmit a transmission wave to the vehicle cabin and a reception unit configured to receive a reflected wave generated by the transmission wave being reflected by an occupant on the seat; a creation unit configured to create, based on the reflected wave, three-dimensional map information in the vehicle cabin in which a position of a reflection point of the reflected wave is represented by three-dimensional coordinates; and a calculation unit configured to calculate, based on the three-dimensional map information, backbone information that is information on a backbone of the occupant on the seat.

Sensor fusion is performed for efficient deep learning processing. A camera image is received from an image sensor and supplemental sensor data is received from one or more supplemental sensors, the camera image and the supplemental sensor data including imaging of a cabin of a vehicle. Regions of interest in the camera image are determined based on one or more of the camera image or the supplemental sensor data, the regions of interest including areas of the camera image flagged for further image analysis. A machine-learning model is utilized to perform object detection on the regions of interest of the camera image to identify one or more objects in the camera image. The objects are placed into seating zones of the vehicle.

An architecture is provided for cooperative target tracking and signal propagation learning using mobile sensors. A method can comprise as a function of sensing data representative of a location of a target device at a first defined moment and model data relating to a motion model representing a probability density function, determining, by a system comprising a processor, a group of locations for the target device at a second defined time point, wherein the probability density function facilitates determining, based on the location of the target device at the first defined moment, a current location of the target device at a third defined moment; and as a function of the group of locations, generating, by the system, a data structure representing a matrix of received signal strength values; and identifying, by the system, a location of the group of locations for the target device at the third defined moment based on the data structure.

Embodiments are directed to a method for determining velocity of an object. The method includes in response to two interleaved chirp sequences being sent towards the object, processing responsive chirps of each of the two interleaved chirp sequences independently from one another to produce respective Doppler-spectrum data sets, and calculating the velocity of the object based on the respective Doppler-spectrum data sets. Each of the interleaved chirp sequences being characterized by a common time spacing between respective chirps of the respective chirp sequence, and each chirp of one of the chirp sequences being offset by an amount of time that is different than the common time spacing.

Aspects of the disclosure are directed to apparatuses, systems and methods for radar processing. As may be implemented in accordance with one or more aspects herein, an apparatus may include receiver circuitry to receive and sample radar signals reflected from a target, and processing circuitry to carry out the following. Representations of the reflections are transformed into the time-frequency domain where they are oversampled. The oversampled representations of the reflections are inversely transformed to provide resampled reflections. Positional characteristics of the target may then be ascertained by constructing a range response characterizing the target based on the resampled reflections.

A fall detection system includes a radar that generates emitting radio waves and receives reflected radio waves from a person under detection, a data generator that generates a point cloud according to the reflected radio waves, an area determining device that determines a sub-area of a detecting area in which the person under detection lies, and a classifier that determines whether the person under detection falls according to the point cloud. The classifier adaptively processes the point cloud with different methods according to sub-areas as determined by the area determining device respectively to determine whether the person under detection falls.

A system for controlling operation of a vehicle includes a millimeter-wave radar sensor, a processor, and a memory communicably coupled to the processor. The memory stores a sensor control module configured to automatically control operation of the radar sensor to perform at least one radar scan of at least a portion of an interior of the vehicle. The sensor control module is also configured to determine, using information acquired by the at least one radar scan of the vehicle interior, at least one characteristic of a child car seat positioned in the interior. The sensor control module is also configured to compare the at least one determined characteristic of the child car seat with child car seat comparison information and, responsive to the comparison of the at least one determined characteristic with the child car seat comparison information, control an operation of the vehicle.

A system and method providing a radar-based motion-sensing user interface suitable for issuing commands to a device or system as a consequence of the detection of user motion, whole-body gestures and/or hand gestures. The system and method derive a three-dimensional representation of a user within a defined space from two-dimensional data obtained from multiple reflected radar signals. The three-dimensional representation is then processed to recognize a human body, and in particular the movement and/or position of the body and/or body parts and joints. The recognized movement and/or position are then compared to a known list of gestures and movements that are associated with particular device/system commands. If one or more of the recognized movements and/or positions conforms with a command movement/gesture, the associated command is issued to the device or system being controlled.