Aptiv Technologies Limited

Aptiv Technologies Ag

Xin Zhang

Zhengzheng Li

Yu Zhang

Le Zheng

Yujie Gu

Shuimei Zhang

Stuart Rogers

This document describes a radar system to universally detect direct-of-arrival (DoA) and direction-of-departure (DoD) angles in direct-path and multipath reflection conditions. For example, a radar system includes a transmitter and receiver array with a first array forming a minimum redundancy array and a second array forming a sparse uniform linear array. A processor determines, using second-array measurements, estimated angles. The quantity of estimated angles is larger than the quantity of actual angles due to angular ambiguity of the second array. The processor then identifies multiple potential sets of actual angles from among the estimated angles and tests each set under a maximum likelihood criterion using first-array measurements. The DoA and DoD angles are identified as the respective set with a maximum utility function value. In this way, the processor determines actual angles with improved resolution and reduced cost without having to identify whether a direct-path or multipath reflection condition exists.

Radar system to universally detect direction-of-arrival or direction-of-departure angles in direct-path and multipath reflection conditions

Described herein is NLS using a bounded linear initial search space and a fixed grid with pre-calculated variables. Specifically, first and second signals with unknown first and second elevation angles, respectively, are received that have been reflected by an object, with the second signal also having been reflected off the ground. A line of second angles is then established as a function of first angles, a sensor height, and a range to the object. The first angles being bound by a function of the sensor height and the range and a function of the sensor height, the range, and the maximum height. A search algorithm is then used to search for an initial elevation angle pair along the line. The initial elevation angle pair may then be fed into a refinement algorithm (e.g., non-linear least squares) to determine the elevation angles associated with the first and second signals.

NLS using a bounded linear initial search space and a fixed grid with pre-calculated variables

This document describes techniques and systems of a radar system with sequential two-dimensional (2D) angle estimation. The radar system can efficiently estimate angles in two dimensions for detections. For example, a radar system includes a processor and an antenna that can receive electromagnetic energy reflected by one or more objects. The antenna includes a 2D array that includes antenna elements positioned in a first dimension and a second dimension. The processor can determine, using electromagnetic energy received by the 2D array, first angles in the first dimension associated with a detection of the one or more objects. The processor can then steer the 2D array to the first angle to generate a steered 1D array for each first angle. Using the steered 1D array, the processor can determine second angles associated with the first angle for the detection.

Radar system with sequential two-dimensional angle estimation

This document describes techniques and systems of a radar system with paired one-dimensional (1D) and two-dimensional (2D) antenna arrays. Even with fewer antenna elements than a traditional radar system, the paired arrays enable an example radar system to have a comparable angular resolution at a lower cost. For example, the 1D array includes antenna elements positioned in a first direction (e.g., azimuth direction) and spaced by a first distance and a second distance. The 2D array includes at least four other antenna elements positioned in the first direction and a second direction (e.g., elevation direction). The other antenna elements are spaced by a third distance in the second direction and by the sum of the first direction and the second direction in the first direction. A processor can associate, using shared angle estimates, angles in the first direction and the second direction for respective objects.

Radar system with paired one-dimensional and two-dimensional antenna arrays

This document describes techniques and systems of a radar system with modified orthogonal linear antenna subarrays and an angle-finding module. The described radar system includes a first one-dimensional (1D) (e.g., linear) subarray; a second 1D subarray positioned orthogonal to the first 1D subarray; and a two-dimensional (2D) subarray. Using electromagnetic energy received by the first 1D subarray and the second 2D subarray, azimuth angles and elevation angles associated with one or more objects can be determined. The radar system associates, using electromagnetic energy received by the 2D subarray, pairs of an azimuth angle and an elevation angle to the respective objects. In this way, the described systems and techniques can reduce the number of antenna elements while maintaining the angular resolution of a rectangular 2D array with similar aperture sizing.

Radar system with modified orthogonal linear antenna subarrays

This document describes techniques and systems of a radar system with an angle-finding process for sparse uniform arrays. The described radar system includes a processor and an antenna that can receive electromagnetic energy reflected by objects in the surrounding environment. The antenna includes a one-dimensional (1D) or two-dimensional (2D) sparse array. The processor can determine, using the received electromagnetic energy, a signal subspace associated with the objects that includes an invariance equation. Using an estimated solution to the invariance equation, the processor determines a solution to the invariance equation. The solution to the invariance equation is used to determine angular phases associated with the objects. The processor can then determine, using the angular phases, angles associated with the objects. In this way, the described angle-finding process enables the radar system with a sparse array to efficiently determine angles associated with objects without blind spots.

Angle-finding process for sparse uniform arrays

This document describes techniques and systems for independent transmit and receive channel calibration for multiple-input multiple-output (MIMO) systems. Antenna responses are collected from each virtual channel of a MIMO system at an angle respective to an object. The transmit components and the receive components of the virtual channels are separated and organized into vectors (one for the transmit components and one for the receive components). Calibration values for elements of the vectors are computed and maintained in a transmit calibration matrix and a receive calibration matrix, respectively. Together, the transmit calibration matrix and the receive calibration matrix may include fewer elements than a calibration matrix for the virtual channels and, therefore, may require less memory and fewer computations to calibrate a MIMO system than using other calibration techniques. As such, described is a less expensive and less complex way to calibrate MIMO system by accurately approximating an ideal antenna array.

Independent transmit and receive channel calibration for multiple-input multiple-output (MIMO) systems

The disclosure provides systems, apparatuses, and techniques for operating automotive MIMO radars in crowded multi-path environments to obtain reliable detections by linearly predicting whether a bistatic condition occurred. To avoid saturating computing resources processing bistatic detections, the described techniques enable a radar system to quickly identify and discard from the field-of-view radar detections that are likely a result of bistatic conditions. By ignoring unusable radar returns that are likely a result of bistatic conditions, an example radar system can focus on processing radar returns from static conditions, for example, in providing radar-based detections as output to an automotive system that is driving a vehicle in an autonomous or a semi-autonomous mode. In so doing, the example radar system provides a highly accurate static object detector that is sufficiently quick in detecting bistatic conditions for use in vehicle-safety systems as well as autonomous and semi-autonomous control.

Linear prediction-based bistatic detector for automotive radar

This document describes techniques and systems to enable a radar system to detect angles in bistatic and monostatic scenarios. In some examples, an automotive radar system includes one or more processors. The processors can obtain electromagnetic (EM) energy reflected by objects and generate, based on the reflected EM energy, a two-dimensional (2D) data matrix. The 2D data matrix has a number of rows corresponding to the number of antenna elements in a transmitter array and a number of columns corresponding to the number of antenna elements in a receiver array. Using the 2D data matrix, the processors can determine DoA estimates and DoD estimates in monostatic and bistatic scenarios. By comparing the DoA estimates to the DoD estimates, the processors can determine an angle associated with the objects. In this way, the described techniques and systems can enable angle detection in monostatic and bistatic conditions with improved angular resolution and reduced cost.

Radar system to detect angles in bistatic and monostatic scenarios

This document describes techniques and components of a radar system with a sparse primary array and a dense auxiliary array. Even with fewer antenna elements than a traditional radar system, an example radar system has a comparable angular resolution at a lower cost, lower complexity level, and without aliasing. The radar system includes a processor and antenna arrays that can receive electromagnetic energy reflected by one or more objects. The antenna arrays include a primary subarray and an auxiliary subarray. The auxiliary subarray includes multiple antenna elements with a smaller spacing than the antenna elements of the primary subarray. The processor can determine, using the received electromagnetic energy, first and second potential angles associated with the one or more objects. The processor then associates, using the first and second potential angles, respective angles associated with each of the one or more objects.

Radar system with sparse primary array and dense auxiliary array

A two-step optimization method for scheduling transmissions in an MIMO (multi-input multi-output) includes determining a first phase code for each transmission according to a first equation, placing each first phase code in a set of first phase codes, and determining a cost function of the set of first phase codes, determining a second phase code for each transmission according to a second equation, determining an updated cost function corresponding to replacing each of the first phase codes with a corresponding one of the second phase codes, and determining which set of phase codes has a smaller cost function.

Growing community of inventors

Calabasas, CA, United States of America

Xin Zhang