A vehicle radar system, apparatus and method use a radar control processing unit to generate a target response signal in at least a first dimension from compressed radar data signals and to perform cell-averaging constant false alarm rate (CA-CFAR) target detection by convolving the target response signal with a weighted kernel window signal in a frequency domain using a Fast Fourier Transform hardware accelerator, an element-wise multiplier, and an Inverse Fast Fourier Transform hardware accelerator to generate an output signal having a sign that indicates a target detection decision.

Aspects of the present disclosure include methods, apparatuses, and computer readable media for identifying a plurality of radar signals including a first radar signal during a first interval and a second radar signal during a second interval, identifying a first subinterval within the first interval, identifying a second subinterval within the second interval, refraining from transmitting downlink information during the first subinterval, and transmitting the downlink information during the second subinterval.

Techniques and apparatuses are described that implement a smart-device-based radar system capable of detecting a frame-of-reference change. In particular, a radar system includes a frame-of-reference machine-learned module trained to recognize whether or not the radar system's frame of reference changes. The frame-of-reference machine-learned module analyzes complex radar data generated from at least one chirp of a reflected radar signal to analyze a relative motion of at least one object over time. By analyzing the complex radar data directly using machine learning, the radar system can operate as a motion sensor without relying on non-radar-based sensors, such as gyroscopes, inertial sensors, or accelerometers. With knowledge of whether the frame-of-reference is stationary or moving, the radar system can determine whether or not a gesture is likely to occur and, in some cases, compensate for the relative motion of the radar system itself.

A radar sensor for sensing the level of a product or the topology of the surface of a product having a continuously operated clock that closes a power supply line to the processor at a predetermined time to activate the processor. Thereupon, the processor controls the switching arrangement to activate the radar chip.

A dual sensing method of an object and a computing apparatus for object sensing are provided. In the method, a first clustering is performed on radar information including a plurality of sensing points and is for determining a first part of the sensing points to be an object. A second clustering is performed on a result of the first clustering and is for determining that the sensing points determined to be the object in the result of the first clustering are located in a region of a first density. A result of the second clustering is taken as a region of interest. According to the region of interest, object detection and/or object tracking is performed on combined information formed by combining the radar information and an image, whose respective detection region and photographing region are overlapped.

Imaging systems, including radio frequency, microwave and millimeter-wave arrangements, and related methods are described. According to one aspect, an imaging system includes an antenna array, a position capture system configured to generate position information indicative of locations of one of the antenna array and the target at the first and second moments in time, and wherein the one of the antenna array and the target move between the first and second moments in time, a transceiver configured to control the antenna array to emit electromagnetic energy towards the target and to generate an output that is indicative of the received electromagnetic energy, a data acquisition system configured to generate radar data, processing circuitry configured to process the position information and the radar data to generate image data regarding the target, and an interface configured to use the image data to generate visual images regarding the target.

A method of determining an uncertainty estimate of an estimated velocity of an object includes, determining the uncertainty with respect to a first estimated coefficient and a second estimated coefficient of the velocity profile equation of the object. The first estimated coefficient being assigned to a first spatial dimension of the estimated velocity and the second estimated coefficient being assigned to a second spatial dimension of the estimated velocity. The velocity profile equation represents the estimated velocity in dependence of the first estimated coefficient and the second estimated coefficient. The method also includes determining the uncertainty with respect to an angular velocity of the object, a first coordinate of the object in the second spatial dimension, and a second coordinate of the object in the first spatial dimension.

Techniques and apparatuses are described that implement a smart-device-based radar system capable of detecting a frame-of-reference change. In particular, a radar system includes a frame-of-reference machine-learned module trained to recognize whether or not the radar system's frame of reference changes. The frame-of-reference machine-learned module analyzes complex radar data generated from at least one chirp of a reflected radar signal to analyze a relative motion of at least one object over time. By analyzing the complex radar data directly using machine learning, the radar system can operate as a motion sensor without relying on non-radar-based sensors, such as gyroscopes, inertial sensors, or accelerometers. With knowledge of whether the frame-of-reference is stationary or moving, the radar system can determine whether or not a gesture is likely to occur and, in some cases, compensate for the relative motion of the radar system itself.

A system implemented in a vehicle for tracking and mapping of one or more objects to identify free space is disclosed. The system has an input unit having lidar sensors and radar sensors that sense objects in a region surrounding the vehicle, and a processing unit that: receives data from lidar sensors and radar sensors and maps the data in corresponding grid maps of corresponding sensors; tracks objects in regions corresponding to the sensors and performs estimation for objects not sensed by any of the sensors; fuses the grid maps by converting them from sensor frame to vehicle frame to generate a fused grid map; and integrates the fused grid map with any or a combination of track management and scan matching to perform classification of the one or more objects into static objects or dynamic objects and identification of free space in the fused grid map.

A device is disclosed. The device may include an antenna, which antenna may receive a ranging signal encoding timing information for one or more of positioning, navigation, and timing. The ranging signal may include a first pulse of a pulse group, a second pulse of the pulse group, and an inter-pulse interval between a start of the first pulse and a start of the second pulse. The device may include a processor, which processor may identify a transmitter of the ranging signal at least partially responsive to the inter-pulse interval.