Delphi Technologies, Inc.

United States of America As Represented by the Secretary of the Air Force

The University of Arizona

Alphacore, Inc.

Aptiv Technologies Limited

Siyang Cao

Stephen William Alland

Joseph Stork Smith

Hao Xin

James F Searcy

Min Liang

Alebel Hassen Arage

Robert L Ewing

David Brendel

Yuan Zheng

Collision avoidance is an important issue for unmanned autonomous vehicles (UAVs). As such, UAVs can be outfitted with a simple and inexpensive sensor for use in collision avoidance. The sensor can be attached to a gimbal and can include a RADAR transmit antenna, a RADAR receive antenna, and an optical camera. The RADAR transmit antenna and RADAR receive antenna are part of a RADAR system. The optical camera and the RADAR system are bore sighted to one another by aligning their fields of view. The optical camera captures an image of a target when the RADAR system indicates the target is in the field of view. The RADAR system and image data can be used to determine a target trajectory. The target trajectory can be used to avoid a collision with the target.

Systems and methods for obstacle avoidance for unmanned autonomous vehicles

The present disclosure addresses a novel feedback design methodology to meet the emerging frontiers of beamforming radio frequency (RF) technology in the areas of machine learning and surveillance. The feasibility of developing adaptive waveform modulation schemes for spectrum management in radars via orthogonal wavelet concepts. With the increasing prevalence of RF spectrum bandwidth limitations, this approach of adaptive feedback waveforms addresses advanced signal processing beamforming technique for phase array RF improving overall sensing performance. The adaptive illumination waveform algorithms for enhancing detection, discrimination, and tracking is motivated from the analogy drawn between the cellular wireless communication systems and the general multi-static radar automotive systems. The present innovation has developed signal processing schemes of adaptive illumination waveforms for enhancing RF detection performance and have developed a unified system architecture of the adaptive radar waveform design for various scenarios including multi-static radars and multiple targets.

Robust feedback design methodology for transform sensing using radio frequency (RF) phase array detection

A high performance, low-cost automotive radar is designed by mounting receivers around a 3D printed Luneburg lens. With this configuration, the antenna radiation pattern is maintained for all angles, (which means no beam deformation). Further, the present radar is capable of performing detection at all azimuth and elevation angles with high angle resolution and broadband operation. The radar adaptively adjusts its spatial sensing pattern, sweeping frequency band, pulse repetition frequency and coherent processing interval according to the environment. This is accomplished by initially performing a rough scan, which updates sensing results via a narrow bandwidth waveform and wide beam scanning. When interested objects are identified, a high-resolution detailed scan is performed in a specific region of interest. In this way, a much more effective detection can be obtained. Moreover, a method of mitigating interference of the 3D printed Luneburg lens based radar and a method of improving the angle resolution using a lens based MIMO approach is disclosed.

Automotive radar using 3D printed luneburg lens

A Pseudo-Random Phase Modulation (PRPM) multiple-input-multiple-output (MIMO) radar system suitable for use on an automated vehicle includes a first transmit-antenna that transmits a first transmit-signal generated by a first PRPM-code, a second transmit-antenna that transmits a second transmit-signal generated by a second PRPM-code, a receive-antenna used to detect a first reflected-signal arising from the first transmit-signal and a second reflected-signal arising from the second transmit-signal, and a controller. The controller is in communication with the receive-antenna and is operable to generate the first PRPM-code and the second PRPM-code. The controller is configured to generate a first sub-channel-output based on a down-converted-signal from the receive-antenna and the first PRPM-code, generate a second sub-channel-output based on the down-converted-signal from the receive-antenna and the second PRPM-code, determine a first residue-signal based on the second sub-channel-output, and determine a first residue-removed-signal by subtracting the first residue-signal from the first sub-channel-output.

Residue cancellation for automated vehicle MIMO radar

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Tucson, AZ, United States of America

Siyang Cao