IP Library › Granted Patent US 12,205,477
Granted Patent B2
US 12,205,477 · App. 18/785,936 · Granted Jan 21, 2025

Unmanned vehicle recognition and threat management

Inventors: David William Kleinbeck (Lees Summit, MO); Armando Montalvo (Winter Garden, FL)
Assignee: Digital Global Systems, Inc.
G08G5/0026G01S3/043G01S3/046G01S3/46G06N3/08G06N7/01G08G5/0069
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Quick Facts
Patent No.
US 12,205,477
App. No.
18/785,936
Granted
Jan 21, 2025
Kind
B2
Abstract

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

Claims (37)

1. A system for signal identification in a radiofrequency (RF) environment, comprising:

at least one node device including a processor and memory in communication with at least one RF receiver;

wherein the at least one RF receiver is operable to capture RF data in the RF environment and transmit the RF data to the at least one node device;

wherein the at least one node device is operable to average Fast Fourier Transform (FFT) data derived from the RF data into at least one tile;

wherein the at least one tile is graphically represented as at least one waterfall image;

wherein the at least one node device is operable to analyze the at least one waterfall image using artificial intelligence (AI) or machine learning (ML) image analysis to identify at least one signal modulation type of at least one signal based on at least one training data set comprising at least one previous tile displaying at least one previous waterfall image; and

wherein the AI or ML image analysis uses a ML frequency hopping algorithm to determine a frequency hopping pattern of the at least one signal based on the at least one waterfall image.

2. The system of claim 1 , wherein the analysis of the at least one waterfall image using AI or ML image analysis includes a comparison of the at least one waterfall image to at least one other waterfall image.

3. The system of claim 1 , wherein the at least one training data set is operable to train the artificial intelligence (AI) or machine learning (ML) using images of known signal modulation types.

4. The system of claim 1 , wherein the at least one signal modulation type includes direct sequence spread spectrum (DSSS), orthogonal frequency division multiplexing (OFDM), frequency hopping spread spectrum (FHSS), Futaba advanced spread spectrum technology (FASST).

5. The system of claim 1 , wherein the at least one signal includes a drone signal type.

6. The system of claim 1 , wherein the at least one node device is operable to transmit an alert related to the at least one signal and/or the at least one signal type.

7. The system of claim 1 , further comprising a display operable to display the at least one analyzed waterfall image.

8. The system of claim 1 , wherein the at least one signal or the at least one signal modulation type is indicated with highlighting on the at least one waterfall image.

9. The system of claim 1 , wherein the at least one RF receiver is operable to transform the RF data to the FFT data.

10. An apparatus for signal identification in a radiofrequency (RF) environment, comprising:

a node device including a processor and memory;

wherein the node device is operable to receive RF data from at least one RF receiver;

wherein the node device is operable to average Fast Fourier Transform (FFT) data derived from the RF data into at least one tile;

wherein the at least one tile is represented graphically as at least one waterfall image;

wherein the node device is operable to analyze the at least one waterfall image using artificial intelligence (AI) or machine learning (ML) image analysis to identify at least one signal modulation type of at least one signal based on at least one training data set comprising at least one previous tile displaying at least one previous waterfall image; and

wherein the AI or ML image analysis uses a ML frequency hopping algorithm to determine a frequency hopping pattern of the at least one signal based on the at least one waterfall image.

11. The apparatus of claim 10 , wherein the at least one signal or the at least one signal modulation type is indicated with highlighting on the at least one analyzed waterfall image.

12. The apparatus of claim 10 , wherein the analysis of the at least one waterfall image using AI or ML image analysis includes a comparison of the at least one waterfall image to at least one other waterfall image.

13. The apparatus of claim 12 , wherein the comparison of the at least one waterfall image to at least one other waterfall image is derived from the at least one training data set.

14. The apparatus of claim 13 , wherein the at least one training data set is represented as at least a second waterfall image.

15. The apparatus of claim 10 , further comprising a display operable to display the at least one analyzed waterfall image.

16. A method for signal analysis in a radiofrequency (RF) environment, comprising:

capturing RF data in the RF environment by at least one RF receiver, converting the RF data to Fast Fourier Transform (FFT) data, and transmitting the FFT data to at least one node device;

averaging the FFT data derived from the RF data by the at least one node device into at least one tile;

wherein the at least one tile is represented graphically as at least one waterfall image;

analyzing the at least one waterfall image by the at least one node device using artificial intelligence (AI) or machine learning (ML) image analysis to identify at least one signal modulation type of at least one signal based on at least one training data set comprising at least one previous tile displaying at least one previous waterfall image; and

determining a frequency hopping pattern of the at least one signal based on the at least one waterfall image using a ML frequency hopping algorithm.

17. The method of claim 16 , further comprising the at least one node device transmitting an alert for the at least one signal modulation type.

18. The method of claim 16 , wherein the analysis of the at least one waterfall image using AI or ML image analysis includes a comparison of the at least one waterfall image to at least one other waterfall image.

19. The method of claim 18 , further comprising updating a database including the at least one other waterfall image with the at least one analyzed waterfall image.

20. The method of claim 16 , further comprising displaying the at least one analyzed waterfall image.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 7, 2024
From: MONTALVO, ARMANDO
To: DIGITAL GLOBAL SYSTEMS, INC.
Reel/Frame 068815/0978 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 12, 2024
From: KLEINBECK, DAVID WILLIAM
To: DIGITAL GLOBAL SYSTEMS, INC.
Reel/Frame 068251/0054 →
Continuity (15)
Continuation In Part 18775710 · Jul 17, 2024
Continuation In Part 18428606 · Jan 31, 2024
Continuation 18374376 · Sep 28, 2023
Continuation 18142904 · May 3, 2023
Continuation 17991348 · Nov 21, 2022
Continuation 17735615 · May 3, 2022
Continuation 17190048 · Mar 2, 2021
Continuation 16732811 · Jan 2, 2020
Continuation 16275575 · Feb 14, 2019
Continuation In Part 16274933 · Feb 13, 2019
Continuation In Part 16180690 · Nov 5, 2018
Continuation In Part 15412982 · Jan 23, 2017
Provisional Application 62722420 · Aug 24, 2018
Provisional Application 62632276 · Feb 19, 2018
Related Publication 20240386800A1 · Nov 21, 2024
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