Systems, methods, and apparatus for estimating angle of arrival

Patent metadata

Field	Value
Publication number	US-12560669-B2
Application number	US-202218065266-A
Country	US
Kind code	B2
Filing date	Dec 13, 2022
Priority date	Dec 13, 2022
Publication date	Feb 24, 2026
Grant date	Feb 24, 2026

Abstract

Systems, methods, and apparatus for processing signals using a sensor arrays are disclosed. In one aspect, an apparatus comprising a sensor array and a computing device is provided. The computing device may comprise one or more processors configured to generate a data matrix based on one or more signals received at each of the plurality of sensor and to determine a covariance matrix based on the data matrix. The one or more processors may also be configured to decompose the covariance matrix into a matrix of eigenvalues and a matrix of eigenvectors and to determine a projected data matrix based on applying the data matrix to the eigenvector matrix. Further, the one or more processors may be configured to determine a denoised projected data matrix based on denoising the projected data matrix and to determine a denoised data matrix based on the denoised projected data matrix.

First claim

What is claimed is:

1 . An apparatus comprising:
a sensor array having a plurality of sensors, each of the sensors configured to receive signals; a computing device comprising one or more processors, the one or more processors configured to:
generate a data matrix based on one or more signals received at each of the plurality of sensors;
determine a covariance matrix based on the data matrix;
decompose the covariance matrix into a matrix of eigenvalues and a matrix of eigenvectors;
determine a projected data matrix based on applying the data matrix to the matrix of eigenvectors;
determine a denoised projected data matrix based on each column of the projected data matrix being a set of time data in a specific direction of a specific eigenvector;
determine a denoised data matrix by projecting the denoised projected data matrix;
determine an angle of arrival of the one or more signals based on the denoised data matrix;
communicate the angle of arrival to avionics of an aircraft; and
cause a location to determine an emitting source relative to the aircraft.

2 . The apparatus according to claim 1 , wherein the determination of the denoised data matrix comprises multiplying the denoised projected data matrix by an inverse of the matrix of eigenvectors.

3 . The apparatus according to claim 2 , wherein the angle of arrival is further determined based on at least one of a sensor array width, a signal wavelength, a number of sensors, or a number of samples of the one or more signals.

4 . The apparatus according to claim 1 , wherein the one or more processors are further configured to:
determine a first denoised matrix based on denoising the denoised data matrix; determine a signal to noise ratio (SNR) for the first denoised matrix; compare the SNR to a threshold; and determine an angle of arrival of the one or more signals based on the first denoised matrix.

5 . The apparatus according to claim 4 , wherein the one or more processors are further configured to determine an angle of arrival of the one or more signals based on the first denoised matrix.

6 . The apparatus according to claim 1 , wherein the data matrix has a size of M×N and the covariance matrix has a size of M×M, wherein N equals a number of antenna elements, and wherein M equals a number of samples of the one or more signals.

7 . The apparatus according to claim 1 , wherein each diagonal element of the covariance matrix indicates an energy level associated with the plurality of sensors, and wherein each non-diagonal element of the covariance matrix indicates a time lag between two sensors.

8 . The apparatus according to claim 1 , wherein the matrix of eigenvectors includes diagonal elements, each diagonal element of comprises an eigenvalue, and wherein the matrix of eigenvectors includes non-diagonal elements, each non-diagonal element corresponds to a value of zero.

9 . The apparatus according to claim 1 , wherein each non-zero eigenvalue represents power of a particular signal or noise, and wherein the one or more processors are further configured to select one or more eigenvalues from the eigenvalues based on a comparison of each non-zero eigenvalue to a threshold value.

10 . The apparatus according to claim 9 , wherein the selected one or more eigenvalues correspond to signal eigenvalues.

11 . The apparatus according to claim 1 , wherein each eigenvector represents a linear combination of spatially distinct signals, wherein each eigenvalue represents power of the spatially distinct signals of an associated eigenvector, and wherein the eigenvectors are orthonormal.

12 . The apparatus according to claim 1 , wherein the one or more processors are configured to determine each eigenvector based on a transformation of a linear combination of eigenvectors associated with different spatial locations.

13 . The apparatus according to claim 1 , wherein the one or more processors are further configured to:
determine a first covariance matrix based on the denoised data matrix; decompose the first covariance matrix into a matrix of first eigenvalues and a matrix of first eigenvectors; determine a first projected data matrix based on applying the denoised data matrix to one or more of the first eigenvectors; determine a first denoised projected data matrix based on denoising the first projected data matrix; determine a first denoised matrix based on the first denoised projected data matrix; and determine an angle of arrival of the one or more signals based on the first denoised matrix.

14 . The apparatus according to claim 1 , wherein the one or more processors are configured to:
determine a new denoised matrix when a signal to noise ratio for the denoised matrix does not exceed a threshold.

15 . A method comprising:
receiving one or more signals at a sensor apparatus, the sensor apparatus having a plurality of sensors, wherein each of the plurality of sensors is configured to receive one or more signals; generating, by one or more processors, a data matrix based on one or more signals received at each of the plurality of sensors; determining, by the one or more processors, a covariance matrix based on the data matrix; decomposing, by the one or more processors, the covariance matrix into a matrix of eigenvalues and a matrix of eigenvectors; determining, by the one or more processors, a projected data matrix based on applying the data matrix to one or more of the eigenvectors; determining, by the one or more processors, a denoised projected data matrix based on each column of the projected data matrix being a set of time data in a specific direction of a specific eigenvector; determining, by the one or more processors, a denoised matrix by projecting the denoised projected data matrix; determining, by the one or more processors, an angle of arrival of the one or more signals based on the denoised matrix; communicating, by the one or more processors, the angle of arrival to an avionics unit of an aircraft; and causing, by the one or more processors, a location to determine an emitting source relative to the aircraft.

16 . The method according to claim 15 , further comprising:
determining a first denoised matrix based on denoising the denoised matrix; determining a signal to noise ratio (SNR) of the eigenvalues; comparing the SNR of each of the eigenvalues to a threshold; and determining an angle of arrival of the one or more signals based on the first denoised matrix.

17 . The method according to claim 15 , wherein each eigenvector, in the matrix of eigenvectors, is determined based on a transformation of a linear combination of eigenvectors associated with different spatial locations.

18 . The method according to claim 15 , further comprising:
determining a first covariance matrix based on the denoised matrix; decomposing the first covariance matrix into a matrix of first eigenvalues and a matrix of first eigenvectors; determining a first projected data matrix based on applying the denoised matrix to one or more of the eigenvectors; determining a first denoised projected data matrix based on denoising the first projected data matrix; determining a first denoised matrix based on the first denoised projected data matrix; and determining an angle of arrival of the one or more signals based on the first denoised matrix.

19 . The method according to claim 15 , wherein each non-zero eigenvalue represents power of a particular signal or noise, and
the method further comprising:
selecting one or more

Assignees

• Boeing Co

Inventors

• Petre Peter
• Wittenberg Peter S
• Smith Andrew D

Classifications

• G01S3/74

  Multi-channel systems specially adapted for direction-finding, i.e. having a single antenna system capable of giving simultaneous indications of the directions of different signals (systems in which the directions of different signals are determined sequentially and displayed simultaneously G01S3/04, G01S3/14) · CPC title

• G01S3/72

  Diversity systems specially adapted for direction-finding · CPC title

• G01S3/10

  Means for reducing or compensating for quadrantal, site, or like errors · CPC title

• H01Q3/26

  varying the relative phase or relative amplitude of energisation between two or more active radiating elements; varying the distribution of energy across a radiating aperture ({H01Q3/12,} H01Q3/22, H01Q3/24 take precedence) · CPC title

• G01S3/46 Primary

  using antennas spaced apart and measuring phase or time difference between signals therefrom, i.e. path-difference systems · CPC title