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.

Abstract: Disclosed are systems and methods for detecting weather conditions at a LiDAR sensor level. In some aspects, a method includes calculating a reference probability mass function (PMF) of at least one field of a point cloud generated from reference scene responses received from a light detection and ranging (LiDAR) sensor; calculating a current PMF for the at least one field of the point cloud generated from a current scene response received from the LiDAR sensor; determining a statistical difference between the reference PMF and the current PMF using a Kullbeck-Leibler (KL) divergence calculation; and responsive to the statistical difference satisfying a threshold for the at least one field, flagging an environmental change in the current scene response.

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: The present technology determines noise in an image of a representation of an environment around an autonomous vehicle (AV). A sensor system receives a first image that represents an environment around the AV including at least one object that may be obscured by environmental conditions. The first image is analyzed to determine if pixels in the first image represent noise from the environmental conditions in comparison to other pixels in the first image that represents a higher degree of image detail. A second image can be generated using the pixels in the first image that represent the higher degree of image detail to result in an output that is less affected by noise from environmental conditions and may result in better performance by downstream systems relying of date from the sensor system.

Abstract: High-resolution point cloud formation based on the use of a point spread function kernel is disclosed. The proposed disclosure provides the means to generate high angular resolution point clouds by performing a matching process of the point spread function (PSF) to a series of adaptive hypotheses to accurately identify the target signatures using the maximum amount of available information. The proposed method comprises a two-stage processing chain. In the first step, the spatial and the Doppler spectrum of the target responses is calculated and decomposed in terms of the sensor point spread function (PSF). In the second step, the target PSF representation is then processed using a support vector machine to determine the presence of closely located targets that would not be possible to detect using typical automotive RADAR signal processing chains.

Abstract: Adaptive differential quantization for ranging sensors and sensor data encoding is enclosed. The proposed disclosure comprises a differential quantization algorithm that extracts the raw signal data gradients to maximize the amount of effective information (using information theory approaches) transferred to a sensor processing data unit. This approach results in an efficient use of the data links between the sensor suite and a central sensor processing data engine, which can allow the use of cost-effective physical links. The proposed disclosure takes advantage of the gradual environment changes observed in typical vehicular platform trajectories.

Abstract: A radio detection and ranging (RADAR) system calibration and compensation approach is described. receive aa reference signal is received from a signal generation architecture. A pre-selected number of cycles of the reference signal are stored. A spectrum calculation is performed on the stored reference signal to generate a spectrum representation. Component analysis operations are performed on the spectrum representation to generate a set of coefficients. Non-linear classification is performed on the set of coefficients to determine collection cycle parameters. Noise parameters are estimated utilizing the coefficients and the collection cycle parameters. The noise parameters are phase noise parameter estimations and/or system characteristic colored noise parameter estimations. Point clouds can be generated using the noise estimation parameters.

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: Methods and apparatus disclosed within provide a solution to problems associated with objects located in a blind spot of a radar apparatus of a first vehicle. An identifier included a set of received radar signals may be extracted from the received radar signals to identify a radar device of a second vehicle. Information may then be received that identifies a location of the second vehicle. This received information may also include operational characteristics of the received radar signals. Evaluations may then be performed on the received radar signals using the received information such that a location of an object located in the blind spot of the radar apparatus may be identified. Data associated with the object may then be stored in a set of radar detection data that that also includes locations of other objects that were detected by direct observations made by the radar apparatus of the first vehicle.

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: A radio detection and ranging (RADAR) sensor system signal generator is disclosed. The signal generator includes a lower frequency oscillator circuit to generate a first reference signal having a first frequency, a higher frequency oscillator circuit to generate a second reference signal having a second frequency that is higher than the first frequency, a set of frequency multipliers and signal mixers coupled to receive the first reference signal and the second reference signal, the set of frequency multipliers and signal mixers to generate a plurality of baseline signals, voltage-to-frequency converters to generate radar frequency waveforms at a plurality of specified frequencies according to chirp parameters for a plurality of frequency bands, and modulating radio frequency mixers to mix the plurality of baseline signals with the radar frequency waveforms at the plurality of specified frequencies to generate a multifrequency wavelet RADAR waveform.

Abstract: Architectures and techniques for near field beamforming are disclosed. RADAR waveform data is received from a radio frequency front end. Range and movement information for one or more objects is generated from the received RADAR waveform data. A spatial frequency representation of the received RADAR waveform data is calculated. The spatial frequency representation of the received RADAR waveform data is migrated to a spatial space representation using a mapping function and interpolation. Signal processing operations are performed on the spatial space representation of the received RADAR waveform data. The spatial space representation of the received RADAR waveform data is converted to a Cartesian space representation. Information corresponding to the one or more objects in the Cartesian space representation is generated.

Abstract: Use of camera information for radio detection and ranging (RADAR) beamforming is disclosed. Camera information for a scene having a target object is received. Digital RADAR waveforms corresponding to a frame of reference including the at target object are received. Coordinates for the scene in the camera frame of reference are translated to the RADAR frame of reference. A radar cross section estimation is determined for the object based on the transformed coordinates. A kernel is selected based on the radar cross section estimation. RADAR signal processing is performed on the digital RADAR waveforms utilizing the selected kernel. A point cloud is populated based on results from the RADAR signal processing.

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: 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: 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: 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: 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: Methods and systems are provided for generating an on-demand distributed aperture by mechanical articulation. In some aspects, a process can include steps for determining a location of an autonomous vehicle, determining whether a maneuver requires long range detections or medium range detections based on the location of the autonomous vehicle, positioning at least two articulated radars based on the determining of whether the maneuver requires long range detections or medium range detections, and enabling a mode of resolution based on the positioning of the at least two articulated radars and by utilizing a static radar. Systems and machine-readable media are also provided.

Abstract: The present disclosure is directed to the transmission and reception of sets of very high frequency electromagnetic (EM) signals in ways that allow a sensing apparatus to discriminate between different sets of transmitted EM signals. Here a sensing apparatus may sequentially transmit different sets of EM signals. Each of these different sets of signals may include an encoded identifier that uniquely identifies each respective signal set of the different sets of signals. Each of these signal sets may include several pulses of a particular frequency with a same relative phase relationship followed by pulses that have a different phase relationship. These changes in phase may be used to encode the unique identifiers into the different sets of transmitted EM energy and these identifiers may be used by a sensing apparatus to associate specific received sets of EM energy with specific sets of transmitted EM energy.