A system for intelligently implementing an autonomous agent that includes an autonomous agent, a plurality of infrastructure devices, and a communication interface. A method for intelligently calibrating infrastructure (sensing) devices using onboard sensors of an autonomous agent includes identifying a state of calibration of an infrastructure device, collecting observation data from one or more data sources, identifying or selecting mutually optimal observation data, specifically localizing a subject autonomous agent based on granular mutually optimal observation data, identifying dissonance in observation data from a perspective of a subject infrastructure device, and recalibrating a subject infrastructure device.

Methods, apparatus, systems and articles of manufacture are disclosed to test RADAR integrated circuits. A radar circuit comprising a local oscillator (LO), a transmitter coupled to the LO and configured to be coupled to a transmission network, a receiver configured to be coupled to the transmission network, and a controller coupled to the LO, the transmitter, and the receiver, the controller to cause the LO to generate a frequency modulated continuous waveform (FMCW), cause the transmitter to modulate the FMCW as a modulated FMCW, cause the transmitter to transmit the modulated FMCW via the transmission network and the receiver to obtain a received FMCW from the transmission network, and in response to obtaining the received FMCW from the receiver, generate a performance characteristic of the radar circuit based on the received FMCW.

A vehicle is located on a digital map, with objects being stored in the digital map in a georeferenced manner. From a set of these objects stored in a georeferenced manner in the digital map, which are currently being detected by the environment sensor, the object most distant from the environment sensor is identified and a current sensor range of the environment sensor is determined on the basis of the distance of the environment sensor from the object.

Systems, methods, tangible non-transitory computer-readable media, and devices associated with radar validation and calibration are provided. For example, target positions for targets can be determined based on imaging devices. The targets can be located at respective predetermined positions relative to the imaging devices. Radar detections of the targets can be generated based on radar devices. The radar devices can be located at a predetermined position relative to the imaging devices. Filtered radar detections can be generated based on performance of filtering operations on the radar detections. A detection error can be determined for the radar devices based on calibration operations performed using the filtered radar detections and the target positions determined based on the one or more imaging devices. Furthermore, the radar devices can be calibrated based on the detection error.

A radar detection system can be calibrated by a method which includes selecting location indicators with corresponding locations. A transmitter of a sensor emits a radar signal to the location of each of the location indicators. The radar signal is reflected off of a target at the location of each of the location indicators. The radar signal which has been reflected off of the target is received with a receiver of the sensor. The location of the target at each of the location indicators is communicated between the sensor and a controller. Locations which define a protected region are selected with the controller. The controller designates the protected region, thereby calibrating the radar detection system such that the radar detection system is capable of detecting an object in the protected region. The calibrated radar detection system can detect targets in an operational mode.

A radar detection system can be calibrated by a method which includes selecting location indicators with corresponding locations. A transmitter of a sensor emits a radar signal to the location of each of the location indicators. The radar signal is reflected off of a target at the location of each of the location indicators. The radar signal which has been reflected off of the target is received with a receiver of the sensor. The location of the target at each of the location indicators is communicated between the sensor and a controller. Locations which define a protected region are selected with the controller. The controller designates the protected region, thereby calibrating the radar detection system such that the radar detection system is capable of detecting an object in the protected region. The calibrated radar detection system can detect targets in an operational mode.

Two-dimensional DOA estimation is challenging as the computational and hardware complexity could scale as the square as compared to that of one-dimensional problem. The proposed scheme relies on designing antenna locations and also involves a mix of subarray and digital beamforming to lower the overall system performance and cost by reducing the costly transceiver chains.

This framework proposes a two-step solution which first isolates a target to a given range doppler bin and elevation angle by linear receive subarray in the elevation direction. However, the elevation estimate is relatively coarse which is further refined along with a high-resolution estimate of azimuth angle. This is achieved by processing the received data from a 2D sparse antenna array, which are systematically chosen to maximize the resolution in both directions. The compressive sensing algorithm is applied to the 2D sparse received array data which exploits the sparse representation of the underlying signal support. The propose approach successfully pairs the correct elevation and azimuth angles for multiple targets. The methodology is effective for a case of single data snapshot and algorithm performance scale well with the availability of multiple data snapshots. It is noted that the proposed methodology allows to further increase the system resolution when data is processed with MIMO virtual array processing.

Two-dimensional DOA estimation is challenging as the computational and hardware complexity could scale as the square as compared to that of one-dimensional problem. The proposed scheme relies on designing antenna locations and also involves a mix of subarray and digital beamforming to lower the overall system performance and cost by reducing the costly transceiver chains.

This framework proposes a two-step solution which first isolates a target to a given range doppler bin and elevation angle by linear receive subarray in the elevation direction. However, the elevation estimate is relatively coarse which is further refined along with a high-resolution estimate of azimuth angle. This is achieved by processing the received data from a 2D sparse antenna array, which are systematically chosen to maximize the resolution in both directions. The compressive sensing algorithm is applied to the 2D sparse received array data which exploits the sparse representation of the underlying signal support. The propose approach successfully pairs the correct elevation and azimuth angles for multiple targets. The methodology is effective for a case of single data snapshot and algorithm performance scale well with the availability of multiple data snapshots. It is noted that the proposed methodology allows to further increase the system resolution when data is processed with MIMO virtual array processing.

This document describes techniques for fine-motion virtual-reality or augmented-reality control using radar. These techniques enable small motions and displacements to be tracked, even in the millimeter or sub-millimeter scale, for user control actions even when those actions are small, fast, or obscured due to darkness or varying light. Further, these techniques enable fine resolution and real-time control, unlike conventional RF-tracking or optical-tracking techniques.

An outside sensor unit includes an outside sensor, a main bracket, a support bracket, a rotation device, and a position adjustment device. The outside sensor detects the outside of a vehicle. The main bracket is attached to a vehicle body. The support bracket supports the outside sensor and is attached to the main bracket. The rotation device has a rotation axis line which is substantially parallel to a roll axis of the vehicle and connects the support bracket and the main bracket together rotatably around the rotation axis line. The position adjustment device is capable of adjusting the relative rotation position between the support bracket and the main bracket around the rotation axis line.