The present application provides a method, apparatus and electronic equipment for recognizing a posture of a target, a first receiving signal and a second receiving signal upon scattering of a transmitting signal from a target to be recognized are acquired, a first baseband signal is determined according to the first receiving signal and the transmitting signal, and a second baseband signal is determined according to the second receiving signal and the transmitting signal; and a category of the posture of the target to be recognized is finally determined according to the first baseband signal and the second baseband signal. The first baseband signal and the second baseband signal carry various feature values related to the posture of the target, including but not limited to transversal velocity information and radial velocity information, etc.

A distributed airborne acoustic anti-drone intelligence system (DAAADS) which senses an unmanned aerial vehicle (UAV) approaching a protected site, predicts trajectories of the UAV which intersect the protected site and identifies the type of the UAV. When at least one of the trajectories intersect the protected site, an alarm and predicted trajectories are transmitted to an air defense unit, which neutralizes the UAV. Debris generated by the neutralization is tracked and trajectories of the debris are predicted. When a trajectory of the debris is predicted to intersect with the protected site, an alert is transmitted to the protected site.

Methods, apparatus, systems and articles of manufacture are disclosed to improve Doppler velocity estimation. An example apparatus is disclosed including a transmitter to transmit a first sweep signal at a first position in a first block of time during a transmit time sequence pattern, and transmit a second sweep signal at a second position in a second block of time during the transmit time sequence pattern, the second position different than the first position. The example apparatus also includes a velocity analyzer to determine a velocity and a direction of arrival of a target object identified during the transmit time sequence pattern.

An embodiment provides a device for measuring a fluid parameter of fluid flow in a channel, including: a transmitter; at least one receiver; a processor operatively coupled to the at least one transmitter and the at least one receiver; a memory device that stores instructions executable by the processor to: transmit, using the transmitter, directed energy carrying a signal toward a surface of a fluid in a fluid channel, so as to produce one or more reflections from the fluid surface; detect, by the at least one receiver, one or more received signals associated with the one or more reflections so produced; determine, based upon a measurement beam comprising characteristics of the transmitted and received signals, one or more fluid parameters to be measured using a processor of the device; and associate, using a processor of the device, the one or more fluid parameters with a channel segment. Other embodiments are described and claimed.

Provided herein is a system and method to determine a three-dimensional heading of a target. The system includes a radar sensor that obtains a three-dimensional snapshot of radar data comprising Doppler velocities and spatial positions of a plurality of detection points of a target, one or more processors, and a memory storing instructions that, when executed by the one or more processors, causes the system to perform conducting a first estimation of a three-dimensional heading of the target based on the spatial positions; conducting a second estimation of the three-dimensional heading of the target based on the Doppler velocities; and obtaining a combined estimation of the three-dimensional heading of the target based on a weighted sum of the first estimation and the second estimation.

A computer includes a processor and a memory storing instructions executable by the processor to receive radar data including a radar pixel having a radial velocity from a radar; receive camera data including an image frame including camera pixels from a camera; map the radar pixel to the image frame; generate a region of the image frame surrounding the radar pixel; determine association scores for the respective camera pixels in the region; select a first camera pixel of the camera pixels from the region, the first camera pixel having a greatest association score of the association scores; and calculate a full velocity of the radar pixel using the radial velocity of the radar pixel and a first optical flow at the first camera pixel. The association scores indicate a likelihood that the respective camera pixels correspond to a same point in an environment as the radar pixel.

A system and method for classifying a type of interaction between a human user and a mobile communication device within a defined volume, based on multiple sensors. The method may include: determining a position of the mobile communication device relative to a frame of reference of the defined volume, based on: angle of arrival, time of flight, or received intensity of radio frequency (RF) signals transmitted by the mobile communication device and received by a phone location unit located within the defined volume configured to wirelessly communicate with the mobile communication device; obtaining at least one sensor measurement related to the mobile communication device from various non-RF sensors; repeating the obtaining, to yield a time series of sensor readings; and using a computer processor to classify the type of interaction into one of many predefined types of interactions, based on the position and the time series of sensor readings.

An estimated initial direction of movement of a newly-detected object is to be determined. The actual previous directions of movement and positions of previously-detected objects are determined and stored. When a newly-detected object is newly detected at a certain position, then the actual previous direction of movement of one of the previously-detected objects at that position is used as a basis to determine the estimated initial direction of movement of the newly-detected object at that position.

Techniques and apparatuses are described that implement motion classification using low-level detections. In particular, a radar system identifies fused detections associated with an object and determines whether the fused detections indicate that the object is moving. If it is determined to be moving or moving perpendicular to the host vehicle, a current motion counter or perpendicular motion counter is incremented, respectively. A current motion flag and/or a perpendicular motion flag are set as true if the current motion counter or the perpendicular motion counter has a value greater than a threshold value, respectively. In response to setting either flag as true, the radar system increments a historical motion counter as true. The host vehicle is then operated based on the current motion flag, the perpendicular motion flag, and the historical motion counter. In this way, the radar system introduces hysteresis to improve the reliability and stability of motion classification.

In an apparatus for detecting a target using a radar according to one aspect of the present invention, a first radar and a second radar, which are multi-channel radars each including a plurality of transmitting antennas and a plurality of receiving antennas, are installed to be spaced apart from each other, and position information of a target and velocity vector information of the target are calculated from first position information and first velocity information of the target acquired from the first radar and second position information and second velocity information of the target acquired from the second radar and then are used to detect and track the target.