Granted Patent B2

US 12,455,364 · App. 17/978,807 · Granted Oct 28, 2025

Inventors: Sam El-Akkad (Poway, CA); Christopher Fischer (Lake Forest, CA); Travis Whitaker (Costa Mesa, CA); Bryden Pearson (Irvine, CA); Todd Berk (Lake Forest, CA); Thao Pham (San Diego, CA); Jon Hsu (Yorba Linda, CA); Cameron Dart (Fountain Valley, CA)

Assignee: Anduril Industries, Inc.

G01S13/582G01S13/0209G01S13/524

Abstract

Claims (32)

1. A computer-implemented method for applying a machine learning model to identify one or more RF signals, the computer-implemented method comprising, by one or more hardware processors executing program instructions:

receiving raw RF signal data through two or more directional antennas corresponding to a first RF system, wherein the two or more directional antennas are configured to be selectably activated, wherein the first RF system comprises a processing module, and wherein the processing module comprises a machine learning component;

sampling, by the processing module of the first RF system, the raw RF signal data to generate sampled RF signal data;

transmitting the sampled RF signal data to the machine learning component for input into a machine learning model;

receiving, from the machine learning component, an output from the machine learning model; and

based on RF signal attributes and the output, identifying a type of an object, wherein the RF signal attributes comprise bandwidth, channel, and signal rate that are each associated with the raw RF signal data.

2. The computer-implemented method of claim 1 , further comprising, by the one or more hardware processors executing program instructions:

selecting for activation one or both of the two or more directional antennas for receiving the raw RF signal data, wherein the two or more directional antennas comprise broad-bandwidth directional antennas.

3. The computer-implemented method of claim 2 , further comprising, by the one or more hardware processors executing program instructions:

determining, based on positioning of the two or more directional antennas, a location of the object.

4. The computer-implemented method of claim 1 , further comprising, by the one or more hardware processors executing program instructions:

filtering the raw RF signal data.

5. The computer-implemented method of claim 4 , wherein the filtering includes a complete or partial suppression of one or more aspects of the raw RF signal data, the sampled raw signal data, or a subset of RF signal data.

6. The computer-implemented method of claim 4 , wherein the filtering comprises removing one or more RF signals from the raw RF signal data that correspond to: (1) RF signals associated with friendly equipment, (2) RF signals associated with equipment that has been manually or automatically flagged as friendly, or (3) a preconfigured whitelist or blacklist.

7. The computer-implemented method of claim 1 , wherein the sampling of the raw RF signal data comprises sampling the raw RF signal data at a preconfigured timestep.

8. The computer-implemented method of claim 7 , wherein the preconfigured timestep is between Oms and 15 ms.

9. The computer-implemented method of claim 7 , wherein the preconfigured timestep is further based at least in part on hardware components associated with an RF system implementing the method.

10. The computer-implemented method of claim 1 , wherein the output includes predicted classes and probabilities corresponding to a subset of RF signals from the sampled RF signal data.

11. The computer-implemented method of claim 10 , wherein identification of the type of object is further based on bandwidth, channel, and signal rate that are each associated with the subset of RF signals.

12. The computer-implemented method of claim 1 , wherein an output of the machine learning model includes predicted classes and probabilities corresponding to one or more RF signals that are identified by the machine learning model.

13. The computer-implemented method of claim 1 further comprising training the machine learning model, wherein training the machine learning model comprises:

based at least in part on annotations corresponding to a signal of interest included in a spectrogram, generating a first subset of RF signal data corresponding to the signal of interest, wherein the spectrogram is generated based on raw RF signal training data; and

inputting, into the machine learning model, the first subset of RF signal data for training the machine learning model to identify the signal of interest.

14. The computer-implemented method of claim 13 , wherein the spectrogram comprises the raw RF signal training data as a function of frequency, time, and/or intensity.

15. The computer-implemented method of claim 13 , wherein the annotations further correspond to a period of time in which the signal of interest is present on the spectrogram.

16. The computer-implemented method of claim 13 , wherein the annotations further correspond to one or more frequencies or frequency bands associated with the signal of interest.

17. The computer-implemented method of claim 13 , wherein the generating of the first subset of RF signal data includes removal of at least a portion of the raw RF signal training data that is not part of the signal of interest.

18. A system comprising:

a computer readable storage medium having program instructions embodied therewith; and

one or more processors configured to execute the program instructions to cause the system to perform the computer-implemented method of claim 1 .

19. A computer program product comprising a non-transitory computer-readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors to cause the one or more processors to perform the computer-implemented method of claim 1 .

20. The computer-implemented method of claim 1 , wherein the raw RF signal data is sampled at a rate that is based on at least one of: (1) a temperature of the one or more hardware processors, or (2) a quantity of raw RF signal data.

Assignments (2)

Continuity (3)

References Cited (106)