A co-prime coded DDM MIMO radar system, apparatus, architecture, and method are provided with a reference signal generator (112) that produces a transmit reference signal; a plurality of DDM transmit modules (11) that produce, condition, and transmit a plurality of transmit signals over which each have a different co-prime encoded progressive phase offset from the transmit reference signal; a receiver module (12) that receives a target return signal reflected from the plurality of transmit signals by a target and generates a digital signal from the target return signal; and a radar control processing unit (20) configured to detect Doppler spectrum peaks in the digital signal, where the radar control processing unit comprises a Doppler disambiguation module (25) that is configured with a CPC decoder to associate each detected Doppler spectrum peak with a corresponding DDM transmit module, thereby generating a plurality of transmitter-associated Doppler spectrum peak detections.

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.

Methods and systems include, in at least one aspect: determining an optical model of an object in flight using two dimensional image data obtained from a camera, determining a radar model of the object in flight using radar data obtained from a radar device, combining the radar model with the optical model to produce three dimensional location information of the object in flight in three dimensional space, comparing the three dimensional location information of the object in flight with data representing an expected ball launch, and rejecting (or verifying) the object as an actual ball launch in response to the three dimensional location information of the object in flight differing (or not differing) from the data representing the expected ball launch by a threshold amount.

An ad hoc approach denoted as devoid clutter capture and filling (DeCCaF) that addresses the nonstationarity effects that arise when input radar waveform returns exhibiting dynamic spectra variations are processed to combat dynamic RFI is disclosed. Portions of the spectra of each input waveform return of a set of input radar waveform returns processed during the CPI may be filled with clutter information borrowed from other waveform returns of the set of waveform returns. DeCCaF may combined with an appropriate filter (e.g., a matched filter, a mismatched filter) to achieve results that are nearly indistinguishable from input radar waveform returns in which no spectral variation are present.

A sensing system is provided that includes a first sub-sensing system having a first azimuth plane. The first sub-sensing system includes a Gradient-index lens, and a first plurality of antenna elements arranged adjacent to the Gradient-index lens and configured to receive a first signal emanating from a first field of view. The sensing system also includes a second sub-sensing system having a second azimuth plane oriented at an angle with respect to the first azimuth plane and a second plurality of antenna elements configured to receive a second signal emanating from a second field of view.

This disclosure describes systems and techniques for object detection. In aspects, techniques include obtaining 3D data including range data, angle data, and doppler data. The techniques further include processing a deep-learning algorithm on the 3D data to obtain processed 3D data and obtaining processed 2D data from the processed 3D data. The processed 2D data includes range data and angle data.

Example embodiments relate to techniques for detecting adverse road conditions using radar. A computing device may generate a first radar representation that represents a field of view for a radar unit coupled to a vehicle and during clear weather conditions and store the first radar representation in memory. The computing device may receive radar data from the radar unit during navigation of the vehicle on a road and determine a second radar representation based on the radar data. The computing device may also perform a comparison between the first radar representation and the second radar representation and determine a road condition for the road based on the comparison. The road condition may represent a quantity of precipitation located on the road and provide control instructions to the vehicle based on the road condition for the road.

Some radar sensors may provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor. Techniques for determining a two-or-more-dimensional velocity from one or more radar measurements associated with an object may comprise determining a data structure that comprises a yaw assumption and a set of weights to tune the influence of the yaw assumption. Determining the two-or-more-dimensional velocity may further comprise using the data structure as part of regression algorithm to determine a velocity and/or yaw rate associated with the object.

Techniques and apparatuses are described that implement a smart-device-based radar system capable of determining characteristics of objects external to a vehicle, occupants within a vehicle, and objects proximal a open-air vehicle. In particular, the system enables a smart device to perform many vehicle operations such as collision avoidance, occupant detection, and parking assistance in vehicle and open-air vehicle environments without integrated radar technology. By using a smart device to perform such actions, existing vehicles and open-air vehicles without integrated radar functionality may be able to leverage radar-based vehicle operations.

Aspects of the invention provide improvements to analyze data collected by a radar system. One of the systems includes a phased array module configured to transmit a sequence of pulses to an environment according to a pre-determined pattern. A data analysis system constructs an image based on returned signals from a single point received by the phased array module, and determines one or more characteristics of a target object in the environment based on the image constructed from the returned signals from the single point.