A radar apparatus is mountable to a vehicle. The radar apparatus includes an observing unit, an estimating unit, a predicting unit, a matching processing unit, and a determining unit. The estimating unit calculates, regarding an initial detection target object, a plurality of velocity estimation values in which folding is presumed, using a velocity observation value calculated by the observing unit. The predicting unit calculates a prediction value from each of the plurality of velocity estimation values. The matching processing unit performs association of the velocity prediction value and the velocity observation value.

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.

A radar system comprises a transmitter and a receiver. The radar system is operable to define a near range and a far range. The radar system is operable to, during each one of a plurality of time intervals, repeatedly transmit, via the transmitter, a plurality of OFDM symbols. The transmitter is operable to select a transmit power for the transmission during the one of the time intervals based on from which of the near range and the far range reflections of the OFDM symbols are to be received during the one of the time intervals. The receiver is operable to receive reflections of the OFDM symbols, and process, in the receiver, the reflections of the OFDM symbols to detect objects within the near range and the far range.

A radar system comprises a transmitter and a receiver. The radar system is operable to define a near range and a far range. The radar system is operable to, during each one of a plurality of time intervals, repeatedly transmit, via the transmitter, a plurality of OFDM symbols. The transmitter is operable to select a transmit power for the transmission during the one of the time intervals based on from which of the near range and the far range reflections of the OFDM symbols are to be received during the one of the time intervals. The receiver is operable to receive reflections of the OFDM symbols, and process, in the receiver, the reflections of the OFDM symbols to detect objects within the near range and the far range.

A multiple input multiple output (MIMO) radar system for detecting a moving object is based on an explicit signal model. The explicit signal model accounts for waveform separation residuals by relating measurements of the virtual array to an auto-term including a Kronecker product of object-receiver signatures and transmitter-object signatures; and a cross-term including a Kronecker product of object-receiver signatures and transmitter-object residual signatures. The radar system uses a spatial MIMO object detector that is based on the explicit signal model to detect the moving object.

A multiple input multiple output (MIMO) radar system for detecting a moving object is based on an explicit signal model. The explicit signal model accounts for waveform separation residuals by relating measurements of the virtual array to an auto-term including a Kronecker product of object-receiver signatures and transmitter-object signatures; and a cross-term including a Kronecker product of object-receiver signatures and transmitter-object residual signatures. The radar system uses a spatial MIMO object detector that is based on the explicit signal model to detect the moving object.

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.

Systems and methods are provided for generating a radar model for a target object. In embodiments, a target simulation model is received that represents one or more physical aspects of a target object, an environment simulation model is received that represents one or more physical aspects of an environment object, and a target distance parameter is received that identifies a reference distance between the target object and a radar system to be simulated. A simulation model is generated based, at least in part, on the target simulation model, the environment simulation model, and the reference distance, and further based on a target aspect angle that identifies an angular position of the target object in relation to the radar system. Interaction of the radar system with the target object and the environment object is simulated using the simulation model, and results of the simulation are used to generate a range profile for the target object at the target aspect angle, wherein the range profile identifies a radar return strength for the reference distance. The target aspect angle is then incremented, and the operations are repeated until range profiles are generated for the target object at a plurality of target angles amounting to a 360 degree rotation of the target object. The range profiles at the plurality of target angles are then accumulated to generate the radar model for the target object.

A monitoring system for an aircraft can include an image sensor and a radar sensor. The system can provide noise compensation to a radar sample corresponding to a return radar signal received by the radar sensor based on information detected by the image sensor. The system can identify one or more object types in the image captured by the image sensor and then translate the identified object types to corresponding positions on a map. The system can correlate the radar sample to a position on the map and any object type located at that position can be identified. The system can then select a noise pattern that corresponds to the identified object type from the map and use the selected noise pattern to compensate the radar sample.