A method and apparatus with radar signal processing are included. A method includes transmitting, through transmission antenna elements, a radar signal at a transmission time interval corresponding to a time division multiplexing (TDM) latency, receiving a reflected signal of the radar signal through reception antenna elements, determining directions of arrival (DOAs) respectively corresponding to the transmission antenna elements by classifying radar data corresponding to the reflected signal, wherein the classifying is based on the transmission time interval, determining an unambiguous element of a phase error element by applying an ambiguous Doppler velocity that is based on the radar data to the phase error element of the individual DOA data, and determining integrated DOA data corresponding to the transmission antenna elements by integrating the individual DOA data by suppressing an ambiguous element of the phase error element.

Surface penetrating radar interrogates a region adjacent a pathway of the vehicle in response to activation by a user. An object detection system which is responsive to the radar transceiver is configured to recognize one or more spatial signatures of one or more detected objects in the region. A controller coupled to the radar transceiver and the object detection system is configured to (i) compare a respective spatial signature of at least one of the detected objects to a plurality of predetermined target signatures to detect an infrastructure asset, (ii) assess a perimeter around the detected infrastructure asset to estimate a severity of an obstruction blocking the infrastructure asset, and (iii) convey an alert message to the user when the estimated severity is greater than a threshold.

An apparatus and a method for estimating rainfall of hail and rain using a dual-polarization weather radar improve accuracy of classification of hail and rain zones and estimation of rainfall intensity by classifying hail and rain zones using a distribution of horizontal reflectivity and differential reflectivity of radar observation values, discriminating between a convective zone and a stratiform zone depending on reflection intensity, and applying a dual-polarization-based rainfall estimating relational equation for each type in a weighted mean technique.

An electronic device, a method, and computer readable medium are disclosed. The method includes transmitting radar signals via a radar transceiver. The method also includes identifying signals of interest that represent biometric information of a user based on reflections of the radar signals received by the radar transceiver. The method further includes generating an input based on the signals of interest that include the biometric information. The method additionally includes extracting a feature vector based on the input. The method also includes authenticating the user based on comparison of the feature vector to a threshold of similarity with preregistered user data.

Radar based HR and BR measurements by simultaneous decoding is a technical problem due to presence of intermodulation of BR and HR harmonics, which degrades simultaneous decoding. Embodiments herein provide a method and system for unobtrusive liveliness detection and monitoring of a subject using a Dual Frequency Radar (DFR) in an IOT network. The system has the capability to completely process the captured raw signals onboard to by applying required signal conditioning and extraction of relevant information using unique signal processing techniques for determining the HR and the BR of the subject accurately. The intermodulation of BR and HR harmonics is eliminated by the system by performing frequency spectrum averaging of both radars signals, which improves the accuracy. Further, the system is configured with a light MQTT protocol and encoding modules for any data to be shared for off board processing, ensuring data security and privacy compliance.

An example method can include generating, via the first sensor, a first group of output tracks associated with a motion of a first target object; generating, via the second sensor, a second group of output tracks associated with the motion of a second target object; analyzing, via a track analysis module, the first group of output tracks and the second group of output tracks to determine whether the first target object and the second target object are a same object to yield a determination; and, when the determination indicates that the first target object and the second target object are the same object, presenting a graphical user interface on a computing device that enables a user to select whether to display on the graphical user interface: (1) a single track from the first group of output tracks or the second group of output tracks and (2) a fused group of tracks selected from the first group of output tracks or the second group of output tracks.

An example method can include generating, via the first sensor, a first group of output tracks associated with a motion of a first target object; generating, via the second sensor, a second group of output tracks associated with the motion of a second target object; analyzing, via a track analysis module, the first group of output tracks and the second group of output tracks to determine whether the first target object and the second target object are a same object to yield a determination; and, when the determination indicates that the first target object and the second target object are the same object, presenting a graphical user interface on a computing device that enables a user to select whether to display on the graphical user interface: (1) a single track from the first group of output tracks or the second group of output tracks and (2) a fused group of tracks selected from the first group of output tracks or the second group of output tracks.

A method of processing radar responses of a radar system characterized by a plurality of beam patterns, to determine if a given response corresponds to a reflective object. The method including, for a first beam pattern, identifying a response at an identified angle relative to a center of the first beam pattern. Then, for a second beam pattern overlapping the first beam pattern, determining if a measured response in the second beam pattern at an angle relative to the center of the second beam pattern that corresponds to the identified angle relative to the center of the first beam pattern is within a predetermined error threshold of an anticipated response calculated using the second beam pattern and the first beam pattern. If the measured response in the second beam pattern is not within the predetermined error threshold, the first response is eliminated from display and/or further tracking.

Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for processing sensor data. In one aspect, a method includes obtaining image data representing a camera sensor measurement of a scene; obtaining radar data representing a radar sensor measurement of the scene; generating a feature representation of the image data; generating a respective initial depth estimate for each of a subset of the plurality of pixels; generating a feature representation of the radar data; for each of the subset of the plurality of pixels, generating a respective adjusted depth estimate for the pixel using the initial depth estimate for the pixel and the radar feature vectors for a corresponding subset of the plurality of radar reflection points; generating a fused point cloud that includes a plurality of three-dimensional data points; and processing the fused point cloud to generate an output that characterizes the scene.

Various embodiments wirelessly detect micro gestures using multiple antenna of a gesture sensor device. At times, the gesture sensor device transmits multiple outgoing radio frequency (RF) signals, each outgoing RF signal transmitted via a respective antenna of the gesture sensor device. The outgoing RF signals are configured to help capture information that can be used to identify micro-gestures performed by a hand. The gesture sensor device captures incoming RF signals generated by the outgoing RF signals reflecting off of the hand, and then analyzes the incoming RF signals to identify the micro-gesture.