A personal proximity warning device for warning a user of a rear approach of a person includes a housing that is removably attachable to an item worn by the user. A camera and a sensor attached to the housing capture an image of and sense motion within an area proximate to the user. A battery, a transceiver, and a microprocessor are positioned in the housing. The microprocessor selectively actuates the camera and the transceiver to capture and transmit the image of the area, respectively, and actuates the transceiver to transmit an alert. Programming code, which is selectively positionable on an electronic device of the user, enables the electronic device to receive the alert and the image, to override audio to earbuds being worn by the user, and to broadcast the alert to the user via the earbuds.

Examples disclosed herein relate to radar systems to coordinate detection of objects external to the vehicle and distractions within the vehicle. A method of environmental detection with a radar system includes detecting an object in an external environment of a vehicle with the radar system positioned on the vehicle. The method includes determining a distraction metric from measurements of user activity obtained within the vehicle with the radar system. The method includes adjusting one or more detection parameters of the radar system based at least on the detected object and the distraction metric. Other examples disclosed herein relate to a radar sensing unit for a vehicle that includes an internal distraction sensor, an external object detection sensor, a coordination sensor and a central controller for internal and external environmental detection.

A method for classifying tracks in radar detections of a scene acquired by a stationary radar unit, comprises: acquiring radar detections of the scene using the static radar unit; feeding at least a portion of the radar detections into a tracker module for producing track-specific feature data indicating a specific track in the scene, feeding at least a portion of the radar detections into a scene model comprising information about scene-specific features aggregated over time, and information indicating areas in the scene with expected ghost target detections and areas with expected real target detections, wherein at least a subset of the scene-specific features is determined from the radar detections; classifying the specific track as belonging to a real target or to a ghost target by relating the specific track to a position in the scene model.

A radar detection system can be calibrated by a method which includes selecting location indicators with corresponding locations. A transmitter of a sensor emits a radar signal to the location of each of the location indicators. The radar signal is reflected off of a target at the location of each of the location indicators. The radar signal which has been reflected off of the target is received with a receiver of the sensor. The location of the target at each of the location indicators is communicated between the sensor and a controller. Locations which define a protected region are selected with the controller. The controller designates the protected region, thereby calibrating the radar detection system such that the radar detection system is capable of detecting an object in the protected region. The calibrated radar detection system can detect targets in an operational mode.

The method serves to determine an operational geographical zone (ZO) relative to a sensor (S) configured to observe and measure the radial speed of an object traveling with a non-zero minimum speed “VT” in a region of interest (ROI). The method comprises: a step of simulating the position of said sensor (s); a step of determining a first zone (Al) of the region of interest constituted by points at each of which said object at that point traveling at a speed greater than or equal to said speed VT and in a given direction “DT”, would be seen by said sensor (S) as having a radial speed greater than a threshold speed defined for that point; and the operational geographical zone (ZO) being defined by taking account of the intersection of the first zone (Al) and of a coverage zone (A2) of the sensor.

Embodiments relate to using an active reflected wave detector to classify the state of a person in an environment and optionally respond accordingly. In one embodiment there is provided a computer implemented method of determining a state of a person comprising: receiving an output of an active reflected wave detector; classifying a state of the person as being in a safe supported state based on the output using measurements of reflections associated with the person, wherein said classifying is based at least on: a height metric associated with at least one reflection from the person conveyed in the output of the active reflected wave detector; and a plurality of velocity magnitude measurements of the person corresponding to different times, each of said velocity magnitude measurements determined using the reflections associated with the person conveyed in the output of the active reflected wave detector.

A vehicle mounted liveness detection system, liveness detection method, apparatus, computer device and storage medium are disclosed. The system includes a Doppler microwave sensor, a camera and an alarm controller. The Doppler microwave sensor is configured to perform a liveness search on an in-vehicle space, obtain a liveness search data, and transmit the liveness search data to the alarm controller. The camera is configured to capture an in-vehicle image of the in-vehicle space and transmit the in-vehicle image to the alarm controller. The alarm controller is configured to perform a liveness detection on the liveness search data, perform a liveness detection on the in-vehicle image and perform an in-vehicle liveness alarm operation when a presence of liveness in the in-vehicle space is detected based on at least one of the liveness search data and the in-vehicle image.

A millimeter-wave real-time imaging based safety inspection system and safety inspection method. The safety inspection system includes a conveying device (10), a millimeter wave transceiver module (11), an antenna array (17, 18), a switch array (16a, 16b), a switch control unit (15a, 15b), a quadrature demodulation and data acquisition module (12), and an image display unit (13). By using an Inverse Synthetic Aperture Radar (ISAR) imaging principle, the millimeter-wave real-time imaging based safety inspection system performs real-time imaging on an object to be inspected when the object moves, so that not only the imaging speed is improved, but also the field of view is enlarged. A safety inspector can determine whether an inspected person carries dangerous goods by observing a three-dimensional diagram of the inspected person, thereby eliminating the inconvenience caused by back-and-forth movement of a safety inspection device used by the safety inspector around the inspected person.

Disclosed are systems, methods, and non-transitory media for sensing radio frequency signals. For instance, radio frequency data can be received by an apparatus and from at least one wireless device in an environment. Based on the radio frequency data received from the at least one wireless device, the apparatus can determine sensing coverage of the at least one wireless device. The apparatus can further provide the determined sensing coverage and a position of at least one device to a user device.

A system and method for detection of an aerial drone in an environment includes a baseline of geo-mapped sensor data in a temporal and location indexed database formed by (i) using at least one sensor to receive signals from the environment and converting into digital signals for further processing; (ii) deriving time delays, object signatures, Doppler shifts, reflectivity, and/or optical characteristics from the received signals; (iii) geo-mapping the environment using GNSS and the sensor data; and (iv) logging sensor data over a time interval, for example 24 hours to 7 days. Live sensor data can be then be monitored and signature data can be derived by computing at least one parameter such as direction and signal strength. The live data is continuously or periodically compared to the baseline data to identify a variance, if any, which may be indicative of a detection event.