Granted Patent B2

US 12,560,669 · App. 18/065,266 · Granted Feb 24, 2026

Inventors: Peter Petre (Chicago, IL); Peter S. Wittenberg (Chicago, IL); Andrew D. Smith (Chicago, IL)

Assignee: The Boeing Company

G01S3/46G01S3/72G01S3/10G01S3/74

Abstract

Claims (74)

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 eigenvalues from the eigenvalues based on a comparison of each eigenvalue, of the eigenvalues, to a threshold value.

20 . A non-transitory computer-readable medium having stored thereon instruction code, wherein the instruction code is executable by one or more processors to perform operations comprising:

receiving one or more signals;

generating a data matrix based on the one or more signals;

determining a covariance matrix based on the data matrix;

decomposing the covariance matrix into a matrix of eigenvalues and a matrix of eigenvectors;

determining a projected data matrix based on applying the data matrix to one or more of the eigenvectors;

determining a denoised projected data matrix based on denoising the projected data matrix;

determining 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.

Assignments (1)

Continuity (1)

References Cited (16)