IP Library › Granted Patent US 12,493,112
Granted Patent B2
US 12,493,112 · App. 17/933,265 · Granted Dec 9, 2025

Multiple-target, simultaneous beamforming for four-dimensional radar systems

Inventors: Yujie Gu (Calabasas, CA); Xin Zhang (Agoura Hills, CA); Zhengzheng Li (Agoura Hills, CA); Yu Zhang (Thousand Oaks, CA)
Assignee: Aptiv Technologies AG
G01S13/42G01S7/023G01S13/931H01Q1/3233G01S2013/0245
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Quick Facts
Patent No.
US 12,493,112
App. No.
17/933,265
Granted
Dec 9, 2025
Kind
B2
Abstract

This document describes techniques and systems of multiple-target, simultaneous beamforming for four-dimensional (4D) radar systems for efficient angle estimation in two dimensions with a high dynamic range. For example, a processor can use electromagnetic (EM) energy received by a two-dimensional (2D) array to determine first angles in a first dimension associated with one or more objects. The processor can then determine a subspace projection matrix using the first angles without an estimate of the power of noise or interference signals in the received EM energy. Using the subspace projection matrix, the processor can determine an interference-orthogonal subspace projection-based beamformer. With the interference-orthogonal subspace projection-based beamformer, the processor can determine the desired signal output from an adaptive beamformer for the EM energy and second angles corresponding to respective first angles for the objects.

Claims (41)

1 . A radar system comprising:

an antenna configured to receive electromagnetic (EM) energy reflected by one or more objects, the antenna comprising a two-dimensional (2D) array that includes at least four antenna elements positioned in a first dimension and a second dimension and an additional one-dimensional (1D) concatenated array, including additional antenna elements not encompassed by the 2D array, and positioned in the first dimension; and

one or more processors configured to:

determine, using the EM energy received at the additional 1D concatenated array, first angles associated with the one or more objects, the first angles being in the first dimension;

determine, using the first angles, an interference-orthogonal subspace projection matrix, the interference-orthogonal subspace projection matrix being determined without an estimate of a power of noise in the EM energy or a power of interference signals in the EM energy;

estimate, using the interference-orthogonal subspace projection matrix, an interference-orthogonal subspace projection-based beamformer, the interference-orthogonal subspace projection-based beamformer being a function of the interference-orthogonal subspace projection matrix and a steering vector for the EM energy, the steering vector being a function of the first angles;

estimate, using a Hermitian of the interference orthogonal subspace projection-based beamformer, a desired signal output of the EM energy without the interference signals; and

determine, using vector information of the desired signal output in the first dimension, second angles associated with the one or more objects, each second angle of the second angles being in the second dimension and corresponding to a respective first angle of the first angles.

2 . The radar system of claim 1 , wherein each vector of the desired signal output in the first dimension is used to determine one or more respective second angles.

3 . The radar system of claim 2 , wherein the one or more processors are configured to determine the second angles associated with the one or more objects simultaneously.

4 . The radar system of claim 2 , wherein the one or more processors are configured to determine the second angles associated with the one or more objects sequentially.

5 . The radar system of claim 1 , wherein the additional 1D array is a uniform linear array.

6 . The radar system of claim 1 , wherein the one or more processors are configured to:

determine the first angles using at least one of an Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), a Multiple Signal Classification (MUSIC), or a non-linear least squares (NLS) based function; and

determine the second angles using at least one of a phase compare, fast Fourier transform (FFT), or NLS based function.

7 . The radar system of claim 1 , wherein the antenna elements of the 2D array in the first dimension are not aligned with antenna elements of the 2D array in the second dimension.

8 . The radar system of claim 1 , wherein the one or more processors are configured to determine the first angles associated with the one or more objects using the EM energy received at a subset of the antenna elements of the 2D array, the subset of the antenna elements positioned in the first dimension.

9 . The radar system of claim 1 , wherein the antenna elements of the 2D array are uniformly spaced apart by a first distance in the first dimension and a second distance in the second dimension, the first distance being different than the second distance.

10 . The radar system of claim 1 , wherein the second dimension is not orthogonal to the first dimension.

11 . The radar system of claim 1 , wherein the radar system is configured to be installed on an automobile.

12 . At least one non-transitory computer-readable storage medium comprising computer-executable instructions that, when executed, cause a processor of a radar system to:

receive, by an antenna of the radar system, electromagnetic (EM) energy reflected by one or more objects, the antenna comprising a two-dimensional (2D) array that includes at least four antenna elements positioned in a first dimension and a second dimension and an additional one-dimensional (1D) concatenated array, including additional antenna elements not encompassed by the 2D array, and positioned in the first dimension;

determine, using the EM energy received at the additional 1D concatenated array, first angles associated with the one or more objects, the first angles being in the first dimension;

determine, using the first angles, an interference-orthogonal subspace projection matrix, the interference-orthogonal subspace projection matrix being determined without an estimate of a power of noise in the EM energy or a power of interference signals in the EM energy;

estimate, using the interference-orthogonal subspace projection matrix, an interference-orthogonal subspace projection-based beamformer, the interference-orthogonal subspace projection-based beamformer being a function of the interference-orthogonal subspace projection matrix and a steering vector for the EM energy, the steering vector being a function of the first angles;

estimate, using a Hermitian of the interference orthogonal subspace projection-based beamformer, a desired signal output of the EM energy without the interference signals; and

determine, using vector information of the desired signal output in the first dimension, second angles associated with the one or more objects, each second angle of the second angles being in the second dimension and corresponding to a respective first angle of the first angles.

13 . The at least one non-transitory computer-readable storage medium of claim 12 , wherein each vector of the desired signal output in the first dimension is used to determine one or more respective second angles.

14 . The at least one non-transitory computer-readable storage medium of claim 13 , wherein the at least one non-transitory computer-readable storage medium comprises further computer-executable instructions that, when executed, cause the processor of the radar system to determine the second angles associated with the one or more objects simultaneously.

15 . The at least one non-transitory computer-readable storage medium of claim 12 , wherein the at least one non-transitory computer-readable storage medium comprises further computer-executable instructions that, when executed, cause the processor of the radar system to:

determine the first angles using at least one of an Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), a Multiple Signal Classification (MUSIC), or a non-linear least squares (NLS) based function; and

determine the second angles using at least one of a phase compare, fast Fourier transform (FFT), or NLS based function.

16 . The at least one non-transitory computer-readable storage medium of claim 12 , wherein the at least one non-transitory computer-readable storage medium comprises further computer-executable instructions that, when executed, cause the processor of the radar system to determine the first angles associated with the one or more objects using the EM energy received at a subset of the antenna elements of the 2D array, the subset of the antenna elements positioned in the first dimension.

17 . The at least one non-transitory computer-readable storage medium of claim 12 , wherein the radar system is configured to be installed on an automobile.

18 . A method comprising:

receiving, by an antenna of a radar system, electromagnetic (EM) energy reflected by one or more objects, the antenna comprising a two-dimensional (2D) array that includes at least four antenna elements positioned in a first dimension and a second dimension and an additional one-dimensional (1D) concatenated array, including additional antenna elements not encompassed by the 2D array, and positioned in the first dimension;

determining, using the EM energy received at the additional 1D concatenated array, first angles associated with the one or more objects, the first angles being in the first dimension;

determining, using the first angles, an interference-orthogonal subspace projection matrix, the interference-orthogonal subspace projection matrix being determined without an estimate of a power of noise in the EM energy or a power of interference signals in the EM energy;

estimating, using the interference-orthogonal subspace projection matrix, an interference-orthogonal subspace projection-based beamformer, the interference-orthogonal subspace projection-based beamformer being a function of the interference-orthogonal subspace projection matrix and a steering vector for the EM energy, the steering vector being a function of the first angles;

estimating, using a Hermitian of the interference-orthogonal subspace projection-based beamformer, a desired signal output of the EM energy without the interference signals; and

determining, using vector information of the desired signal output in the first dimension, second angles associated with the one or more objects, each second angle of the second angles being in the second dimension and corresponding to a respective first angle of the first angles.

Assignments (4)
MERGER Recorded Feb 11, 2024
From: APTIV TECHNOLOGIES (2) S.À R.L.
To: APTIV MANUFACTURING MANAGEMENT SERVICES S.À R.L.
Reel/Frame 066566/0173 →
ENTITY CONVERSION Recorded Feb 11, 2024
From: APTIV TECHNOLOGIES LIMITED
To: APTIV TECHNOLOGIES (2) S.À R.L.
Reel/Frame 066746/0001 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 11, 2024
From: APTIV MANUFACTURING MANAGEMENT SERVICES S.À R.L.
To: APTIV TECHNOLOGIES AG
Reel/Frame 066551/0219 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 19, 2022
From: GU, YUJIE; ZHANG, XIN; LI, ZHENGZHENG; ZHANG, YU
To: APTIV TECHNOLOGIES LIMITED
Reel/Frame 061137/0450 →
Continuity (1)
Related Publication 20240103151A1 · Mar 28, 2024
References Cited (81)
US 5657027A · Guymon · 1997 [cited by applicant]
US 7474262B2 · Alland · 2009 [cited by applicant]
US 7639171B2 · Alland et al. · 2009 [cited by applicant]
US 9395727B1 · Smith et al. · 2016 [cited by applicant]
US 9869762B1 · Alland et al. · 2018 [cited by applicant]
US 10416680B2 · Li et al. · 2019 [cited by applicant]
US 10446923B2 · Watson · 2019 [cited by applicant]
US 10809737B2 · Li et al. · 2020 [cited by applicant]
US 10866304B1 · Hassibi et al. · 2020 [cited by applicant]
US 11619705B2 · Zhang et al. · 2023 [cited by applicant]
US 11635506B2 · Wasa et al. · 2023 [cited by applicant]
US 20120242535A1 · Kanamoto · 2012 [cited by examiner]
US 20170029107A1 · Emami et al. · 2017 [cited by applicant]
US 20170149147A1 · Minami et al. · 2017 [cited by applicant]
US 20180149736A1 · Alland et al. · 2018 [cited by applicant]
US 20190285738A1 · Iwasa et al. · 2019 [cited by applicant]
US 20190324133A1 · Hong et al. · 2019 [cited by applicant]
US 20200004262A1 · Li et al. · 2020 [cited by applicant]
US 20200256947A1 · Motoda · 2020 [cited by applicant]
US 20200292690A1 · Kim et al. · 2020 [cited by applicant]
US 20200309899A1 · Jonas et al. · 2020 [cited by applicant]
US 20200355816A1 · Ishikawa · 2020 [cited by applicant]
US 20210373144A1 · Amani et al. · 2021 [cited by applicant]
US 20220163623A1 · Kishigami et al. · 2022 [cited by applicant]
US 20220236370A1 · Li et al. · 2022 [cited by applicant]
US 20230152436A1 · Sharma · 2023 [cited by examiner]
CN 106772224A · 2017 [cited by applicant]
CN 111239678A · 2020 [cited by applicant]
EP 2662699A1 · 2013 [cited by applicant]
EP 3757607A1 · 2020 [cited by applicant]
EP 4036600A1 · 2022 [cited by applicant]
EP 4043919A1 · 2022 [cited by applicant]
JP 6523350B2 · 2019 [cited by applicant]
JP 2020186972A · 2020 [cited by applicant]
WO 2021096889A1 · 2021 [cited by applicant]
“Extended European Search Report”, EP Application No. 23165460.9, Sep. 15, 2023, 16 pages. [cited by applicant]
“Extended European Search Report”, EP Application No. 22200994.6, Aug. 11, 2023, 15 pages. [cited by applicant]
“Extended European Search Report”, EP Application No. 23158330.3, Aug. 25, 2023, 17 pages. [cited by applicant]
Wu, et al., “A Low Complexity Adaptive Algorithm for Eigenspace-Based Two-Dimensional Direction of Arrival Tracking”, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E92-A, … [cited by applicant]
“Extended European Search Report”, EP Application No. 21216318.2, May 30, 2022, 10 pages. [cited by applicant]
Capon, “High-Resolution Frequency-Wavenumber Spectrum Analysis”, Proceedings of the IEEE, vol. 57, No. 8, Aug. 1969, pp. 1408-1418. [cited by applicant]
Chan, et al., “A parameter estimation approach to estimation of frequencies of sinusoids”, Apr. 1981, pp. 214-219, 6 pages. [cited by applicant]
Feger, et al., “A 77-GHz FMCW MIMO Radar Based on an SiGe Single-Chip Transceiver”, IEEE Transactions on Microwave Theory and Techniques, vol. 57, No. 5, May 2009, pp. 1020-1035. [cited by applicant]
Mcglaning, “Multipath Propagation”, Wireless Receiver Design for Digital Communications—Chapter 3., Jan. 2012, pp. 190-206. [cited by applicant]
Qian, et al., “Enhanced PUMA for direction-of-arrival estimation and its performance analysis”, Aug. 15, 2016, pp. 4127-4137, 11 pages. [cited by applicant]
Razavi-Ghods, “Characterisation of MIMO Radio Propagation Channels”, Durham theses, Durham University. Available at Durham E-Theses Online: http://etheses.dur.ac.uk/2526/ (Year: 2007), 349 pages. [cited by applicant]
Scheiner, et al., “Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar”, Dec. 2019, pp. 2068-2077. [cited by applicant]
Schmidt, “Multiple Emitter Location and Signal Parameter Estimation”, IEEE Transactions on Antennas and Propagation, Mar. 1986, pp. 276-280. [cited by applicant]
Shi, et al., “Sparsity-Based Two-Dimensional DOA Estimation for Coprime Array: From Sum-Difference Coarray Viewpoint”, IEEE Transactions on Signal Processing, vol. 65, No. 21, Nov. 1, 2017, pp. 5591-5604. [cited by applicant]
Vaidyanathan, et al., “Theory of Sparse Coprime Sensing in Multiple Dimensions”, IEEE Transactions on Signal Processing, vol. 59, No. 8, Aug. 2011, pp. 3592-3608. [cited by applicant]
Yu, et al., “MIMO Adaptive Beamforming for Nonseparable Multipath Clutter Mitigation”, IEEE Transactions on Aerospace and Electronic Systems, vol. 50, No. 4, Oct. 2014, pp. 2604-2618. [cited by applicant]
“Extended European Search Report”, EP Application No. 21196393.9, Feb. 28, 2022, 11 pages. [cited by applicant]
“Extended European Search Report”, EP Application No. 21196394.7, Mar. 4, 2022, 11 pages. [cited by applicant]
“Extended European Search Report”, EP Application No. 21215410.8, Jul. 12, 2022, 9 pages. [cited by applicant]
“Extended European Search Report”, EP Application No. 21216322.4, Jun. 3, 2022, 9 pages. [cited by applicant]
Amin, et al., “Sparse Arrays and Sampling for Interference Mitigation and DOA Estimation in GNSS” Proceedings of the IEEE, vol. 104, No. 6, Jun. 2016, pp. 1302-1317. [cited by applicant]
Chen, et al., “A new method for joint DOD and DOA estimation in bistatic MIMO radar”, Feb. 2010, pp. 714-718. [cited by applicant]
Engels, et al., “Automotive MIMO Radar Angle Estimation in the Presence of Multipath”, Oct. 2017, 5 pages. [cited by applicant]
Gu, et al., “Adaptive Beamforming via Sparsity-Based Reconstruction of Covariance Matrix”, Compressed Sensing in Radar Signal Processing, 2019, 33 pages. [cited by applicant]
Gu, et al., “Joint SVD of Two Cross-Correlation Matrices to Achieve Automatic Pairing in 2-D Angle Estimation Problems”, IEEE Antennas and Wireless Propagation Letters, vol. 6, pp. 553-556, Feb. 2007, 4 pages. [cited by applicant]
Gu, et al., “Robust Adaptive Beamforming Based on Interference Covariance Matrix Reconstruction and Steering Vector Estimation”, IEEE Transactions on Signal Processing, vol. 60, No. 7, Jul. 2012, pp. 3881-3885. [cited by applicant]
Gu, et al., “Robust Adaptive Beamforming Based on Interference Covariance Matrix Sparse Reconstruction”, Signal Processing, vol. 96, Mar. 1, 2014, pp. 375-381. [cited by applicant]
Haardt, et al., “Unitary ESPRIT: How to Obtain Increased Estimation Accuracy with a Reduced Computational Burden”, May 1995, 1232-1242. [cited by applicant]
Jiang, et al., “Joint DOD and DOA Estimation for Bistatic MIMO Radar in Unknown Correlated Noise”, Nov. 2015, 5113-5125. [cited by applicant]
Jin, “Joint DOD and DOA estimation for bistatic MIMO radar”, Feb. 2009, pp. 244-251. [cited by applicant]
Kikuchi, et al., “Pair-Matching Method for Estimating 2-D Angle of Arrival With a Cross-Correlation Matrix”, IEEE Antennas and Wireless Propagation Letters, vol. 5, pp. 35-40, Mar. 2006, 6 pages. [cited by applicant]
Moffet, “Minimum-Redundancy Linear Arrays”, IEEE Transactions on Antennas and Propagation, vol. AP-16, No. 2., Mar. 1968, pp. 172-175. [cited by applicant]
Pursuant to MPEP § 2001.6(b) the applicant brings the following co-pending application to the Examiner's attention: U.S. Appl. No. 17/075,632. [cited by applicant]
Roy, et al., “ESPRIT-Estimation of Signal Parameters Via Rotational Invariance Techniques”, Jul. 1989, pp. 984-995. [cited by applicant]
Steinwandt, et al., “Performance Analysis of ESPRIT-Type Algorithms for Co-Array Structures”, Dec. 10, 2017, 5 pages. [cited by applicant]
Sun, et al., “MIMO Radar for Advanced Driver-Assistance Systems and Autonomous Driving: Advantages and challenges”, Jul. 2020, pp. 98-117. [cited by applicant]
Tropp, et al., “Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit”, IEEE Transactions on Information Theory, vol. 53, No. 12, Dec. 2007, pp. 4655-4666, Dec. 2007, 12 pages. [cited by applicant]
Vaidyanathan, et al., “Sparse Sensing with Co-Prime Samplers and Arrays”, IEEE Trans. Signal Process., vol. 59, No. 2, Feb. 2011, pp. 573-586. [cited by applicant]
Van Trees, “Planar Arrays and Apertures”, Essay in “Detection, Estimation, and Modulation Theory, Optimum Array Processing”, pp. 231-274. Wiley-Interscience, May 2002, 44 pages. [cited by applicant]
Visentin, et al., “Analysis of Multipath and DOA Detection Using a Fully Polarimetric Automotive Radar”, Apr. 2018, 8 pages. [cited by applicant]
Wang, et al., “Two-Dimensional Beamforming Automotive Radar with Orthogonal Linear Arrays”, 2019 IEEE Radar Conference, Boston, MA, Apr. 22-26, 2019., 6 pages. [cited by applicant]
Zhou, et al., “A Robust and Efficient Algorithm for Coprime Array Adaptive Beamforming”, IEEE Transactions on Vehicular Technology, vol. 67, No. 2, Feb. 2018, pp. 1099-1112. [cited by applicant]
Zoltowski, et al., “Closed-Form 2-D Angle Estimation with Rectangular Arrays in Element Space or Beamspace via Unitary ESPRIT”, Feb. 1996, pp. 316-328. [cited by applicant]
Zoltowski, et al., “ESPRIT-Based 2-D Direction Finding with a Sparse Uniform Array of Electromagnetic Vector Sensors”, Aug. 1, 2000, pp. 2195-2204. [cited by applicant]
“Extended European Search Report”, EP Application No. 22197753.1, Mar. 7, 2023, 17 pages. [cited by applicant]
Zhang, et al., “Flexible Array Response Control via Oblique Projection”, IEEE Transactions on Signal Processing, vol. 67, No. 12, Jun. 15, 2019, pp. 3126-3139. [cited by applicant]