Abstract: Multi-sensor radar microDoppler holography is described. An example of a method includes performing a 2D Fourier transform calculation for multiple sets of raw Radar sensor data to generate Doppler spatial frequency map data, the raw sensor data including at least a first set of raw sensor data associated with a first Radar sensor at a first location and a second set of raw sensor data associated with a second Radar sensor at a second, different location; performing a DWT calculation of the Doppler spatial frequency map data; applying phase compensation to generate phase compensated data transforms; adding the phase compensated data transforms to generate a sum; performing an inverse 2D Fourier transform calculation to generate a transform result; and extracting microDoppler information and Doppler information extraction from the transform result.

Abstract: A long-stroke pumping unit can be moved away from and toward the wellhead by using a single hydraulic power source via a control interface that is coupled to the hydraulic power source and configured to control both the vertical movement and the horizontal movement of the long-stroke pumping unit. The control interface can control the hydraulic power source for extension and retraction of a hydraulic cylinder for horizontal movement of the unit, and the same control interface can control the same hydraulic power source for extension and retraction of a first vertical hydraulic jack and a second vertical hydraulic jack of the long-stroke pumping unit for vertical movement of an end of the long-stroke pumping unit.

Abstract: For some embodiments of the present disclosure, systems and methods for using computer vision to guide processing of receive responses of RADAR sensors of a vehicle are described. A computer implemented method comprises receiving, with one or more receivers of RADAR sensors, environment receive RF responses, receiving region of interest (ROI) information including segments of a tentative set of ROIs with free space area boundaries from an image processor of an image processing chain for the computer vision, dynamically determining a number of polyphase filters based on a number of ROIs having dynamic scenes that are provided by the image processing chain, and adjusting parameters of the polyphase filters of a RADAR processing chain based on the ROI information.

Abstract: A radar apparatus at a vehicle may transmit a set of radar energy that is reflected off an object after which the reflected radar energy may be received at the radar apparatus. When relative motion of a radar apparatus and an object include movement in two different directions, power of the reflected radar signals may be spread out in a manner that makes data associated with the reflected radar signals difficult to interpret. This spreading out of the radar signals can make mappings of the received radar data appear to be smeared or distorted. To compensate for this smearing effect, mappings of this smeared radar data may be compared with curves from which compensation factors may be identified. These compensation factors may allow a processor to perform calculations to generate updated mappings of the radar data that may allow the processor to more accurately identify characteristics of the object.

Abstract: A passenger seat assembly includes a passenger seat and a retractable divider assembly. The passenger seat includes a first side, a second side opposite from the first side, and a forward-facing surface extending between the first side and the second side. The retractable divider assembly is attached to the passenger seat proximate to the first side of the passenger seat and includes a divider. The divider is movable between a stowed position and a deployed position. In the deployed position, the divider extends in a forward direction relative to the forward-facing surface of the passenger seat.

Abstract: Architectures and techniques for radar interference detection are provided. A radar sensor system in accordance with the present disclosure may receive, via a radio frequency (RF) receiver, radar signals including a radar signal of interest and one or more interfering radar signals. The radar sensor system may calculate a Doppler spectrum for each of the radar signals and perform a chirplet transform on the Doppler spectrum to generate various waveform parameters. A Principal Component Analysis (PCA) may be performed on the waveform parameters to extract frequency features of the radar signals. The radar sensor system may classify the frequency features using a classifier to identify interfering frequency features associated with the interfering radar signals using a classifier. The radar sensor system may further extract interfering waveform information based on the interfering frequency features of the interfering RF signals.

Abstract: Described is a passenger seat having a seat bottom, a seat back, and a privacy divider. The seat back may have a stationary portion and a moveable portion, wherein the moveable portion is substantially upright in stowed position and is substantially horizontal over the seat bottom in a deployed position. The privacy divider may be coupled to at least an upper surface of the moveable portion in the deployed position.

Abstract: The disclosed technology provides solutions for improving perception systems and in particular for improving object identification based on sensor point cloud data, such as radar point cloud data. A process of the disclosed technology can include steps for receiving point cloud data comprising a plurality of radar points, wherein each of the radar points corresponds with a first object or a second object in an environment, generating a semantic label for each of the radar points, and clustering the plurality of radar points based on the semantic label for each of the radar points, to generate a first point cloud cluster for the first object and a second point cloud cluster for the second object. Systems and machine-readable media are also provided.

Abstract: Methods and apparatus disclosed within provide a solution to problems associated with the use of either high frequency or low frequency radar signals. Methods of the present disclosure may transmit high frequency radar signals, transmit low frequency radar signals, receive reflected radar signals, and process the received radar signals using parameters respectively suited for processing high frequency and low frequency radar signals. Evaluations may be performed that allow an apparatus to adapt for limitations associated with the processing of high frequency radar data, the processing of low frequency radar data, or both. Different correlation functions may be performed that allow the apparatus to identify objects and to identify object velocities using different sets of program code instructions. These different evaluations and correlations may result in the generation of a set of “point-cloud” information that may be used by other processes of a sensing apparatus.

Abstract: Processing of a fractalet radio detection and ranging (RADAR) signal is described. A reference fractalet waveform is received. The fractalet waveform includes self-similar waveforms having lower frequency bands and frequency bands. A reflected fractalet waveform received via one or more antennae is decoded. A waveform profile of chirplet transforms of signals in the lower frequency bands within the reflected fractalet waveform are compared to the reference fractalet waveform. Time spans corresponding to the subset of lower frequency bands are determined. Signals from the higher frequency bands are extracted from the reflected fractalet waveform. Chirplet transforms for the extracted signals from the higher frequency bands are determined for the determined time spans. Spatial frequency components along azimuth direction and elevation directions are calculated for targets based on the chirplet transforms for the extracted signals from the higher frequency bands.

Abstract: Techniques and architectures for managing radar sensor processing chains. A first high-frequency radio signal is received with a first RF receiver in the plurality of RF sensor suites on a host platform. The received high-frequency radio signal is converted to a lower second frequency range. A chirplet transform is performed on the signal in the second frequency range. Stored relative location information for a second RF receiver in the plurality of RF sensor suites is retrieved. Radar waveform information corresponding to the second RF receiver in a processing stream corresponding to the first RF receiver is extracted by utilizing the retrieved information and results from the chirplet transform. A point cloud is generated based on the converted signal in the second frequency range and the extracted radar waveform information.

Abstract: Architectures and techniques for radar signaling in emergency scenarios. A high-frequency radio signal in a first frequency range from a remote device with a local radio frequency (RF) receiver. The received radio signal in the first frequency range is converted to a corresponding signal in a second and lower frequency range. Signal phase information in the lower frequency signal is modified to generate a modified signal in the lower frequency range. The modified signal in the lower frequency range is converted to the first frequency range. The modified signal in the first frequency range is transmitted to the remote device with an RF transmitter.

Abstract: Multi-frequency microDoppler fusion is described. An example of a method includes performing a 2D Fourier transform of raw Radar sensor data and raw LiDAR sensor data to generate Radar range-Doppler map data and LiDAR range-Doppler map data; performing a Stationary Wavelet Transform (SWT) calculation of the Radar and LiDAR sensor range-Doppler map data to generate a Radar SWT product and a LiDAR SWT product; performing an SWT product thresholding operation to extract signatures that are common to the Radar SWT product and the LiDAR SWT product; and performing a microDoppler calculation on the extracted common signatures to generate Radar-LIDAR joint fused micro-Doppler signatures.

Abstract: Systems and techniques of the present disclosure may allow traffic control indicators, such as road signs or signals along a roadway to send information to a radar device at an autonomous vehicle (AV). A traffic control indicator may receive a radio signal sent from the radar device at the AV. A radar apparatus at the traffic control indicator may add information that simulates movement of the traffic control indicator and message data to an updated radar signal. The updated radar signal may then be sent from the traffic control indicator radar apparatus to the AV radar device. The AV radar device may then extract the message data from the updated radar signal based on updated radar signal including the movement information. A controller that controls the AV may then control the AV to drive according to the message data extracted from the updated radar signal.

Abstract: The present disclosure is directed to a plurality of different software models allowing a processor to perform calculations associated with different sets of criteria using data associated with variables that have a strong correlation with one or more of the different criteria sets. Methods consistent with the present disclosure may use several different sets of software that include instructions associated with the collection and evaluation of data associated with a vehicle and with an automated driving system. Different criteria sets may be associated with a range of operating modes, spectral content of received signals, phases of radar signals, and an angular coverage/field of view of the radar apparatus. Results generated by each of the software models may allow a processor to assign weights to the results generated by the different models to generate a combined result that in turn is used to update an operational mode of the radar apparatus.

Abstract: A phone holder device is provided. The device includes a lower part having a plurality of fasteners and a closure with plurality of balls placed in the closure; a spinning part rotatably attached to the center of the lower part, the spinning part is configured to rotate around the center of the spinning part; and a top plate attached to the spinning part.

Abstract: A storage box includes a compartment having a base and opposed sidewalls extending from the base. Each sidewall of the opposed sidewalls includes two first retaining surfaces extend away from the base and along the sidewall where the first retaining surfaces are a first distance away from each other. Each sidewall includes two parallel second retaining surfaces extending away from the first retaining surfaces and along the sidewall. The second retaining surfaces are a second distance away from each other and the second distance is greater than the first distance. The storage box includes a rod, and support surfaces associated with each sidewall of the first pair of opposed sidewalls are configured to support the rod.

Abstract: Disclosed are systems and methods for scintillation-based neural network for RADAR target classification. In some aspects, a method includes generating a point cloud from radio frequency (RF) scene responses received from a radio detection and ranging (RADAR) sensor for a scanned scene; populating a rolling buffer with frame data from the point cloud, the frame data including radar cross section (RCS) values, RCS scintillation measurements, and velocity values for objects in the scanned scene; inputting the RCS scintillation measurements and velocity values for an object of the objects to a convolutional neural network (CNN); and receiving a classification of the object from the CNN, wherein the CNN is to utilize a probability density function (PDF) estimate of the RCS scintillation measurements and the velocity values to determine fits with one or more reference PDFs based on a Neyman-Pearson evaluation, and wherein the fits are assessed to classify the object.

Abstract: Curvelet-based low level fusion of camera and RADAR sensor information is disclosed and includes processing RADAR data corresponding to a scene to generate a RADAR point cloud and processing camera image data corresponding to the scene using a curvelet transform to identify a target of interest in the scene and generate for the target of interest a target type, (x,y) coordinate values, and a curvelet magnitude per decomposition level. If discrepancies exist between (x,y) coordinate values of the RADAR point cloud and the target type, (x,y) coordinate values, and curvelet magnitude per composition level of the target of interest, a portion of the RADAR data processing is repeated to regenerate the RADAR point cloud; otherwise, the RADAR point cloud to a perception stack of a vehicle.