IP Library Granted Patent US 11,076,308
Granted Patent B2
US 11,076,308 · App. 16/844,537 · Granted Jul 27, 2021

Systems, methods, and devices for electronic spectrum management

Inventors: David William Kleinbeck (Lees Summit, MO); Ronald C. Dzierwa (Baltimore, MD); Gabriel R. Garcia (Severna Park, MD); Daniel Carbajal (Severna Park, MD)
Assignee: DIGITAL GLOBAL SYSTEMS, INC.
H04W24/08H04B17/20H04B17/309H04B17/318H04W4/029H04W16/14H04W24/10H04W64/006G01S5/0263H04B17/23H04B17/27H04B17/3911
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Quick Facts
Patent No.
US 11,076,308
App. No.
16/844,537
Filed
Apr 9, 2020
Granted
Jul 27, 2021
Kind
B2
Art Unit
2648
USPC
455/67.11
Abstract

Systems, methods, and devices enable spectrum management by identifying, classifying, and cataloging signals of interest based on radio frequency measurements. In an embodiment, signals and the parameters of the signals may be identified and indications of available frequencies may be presented to a user. In another embodiment, the protocols of signals may also be identified. In a further embodiment, the modulation of signals, data types carried by the signals, and estimated signal origins may be identified.

Claims (69)

1. A system for managing spectrum data for an electromagnetic environment, comprising:

at least one node device; and

at least one remote device;

wherein the at least one node device comprises at least one receiver, an automatic signal detection (ASD) module, and a learning and conflict detection engine;

wherein the at least one node device is at an edge of a communication network;

wherein the at least one node device is operable to sweep and learn the electromagnetic environment in a learning period based on statistical learning techniques, thereby creating learning data including power level measurements of the electromagnetic environment;

wherein the at least one node device is operable to form a knowledge map based on the power level measurements of the electromagnetic environment;

wherein the at least one node device is operable to scrub a spectral sweep against the knowledge map;

wherein the at least one node device is operable to calculate a first derivative of the power level measurements and a second derivative of the power level measurements;

wherein the at least one node device is operable to select most prominent derivatives of the first derivative and the second derivative;

wherein the at least one node device is operable to smooth the spectral sweep with a correction vector, wherein the correction vector is determined according to the spectral sweep;

wherein the at least one node device is operable to detect at least one signal in the electromagnetic environment based on matched positive and negative gradients;

wherein the at least one node device is operable to average the spectral sweep, remove areas identified by the matched positive and negative gradients, and connect points between removed areas to determine a baseline;

wherein the at least one node device is operable to subtract the baseline from the spectral sweep to reveal the at least one signal, thereby creating signal data;

wherein the at least one node device is operable to process the signal data, thereby generating processed data;

wherein the processed data includes In-Phase and Quadrature (I/Q) data for at least one target bandwidth;

wherein the learning and conflict detection engine is configured for conflict recognition and anomaly identification based on the processed data; and

wherein the at least one node device is operable to communicate at least one report for the electromagnetic environment to the at least one remote device.

2. The system of claim 1 , wherein one or more of the at least one node device is fixed.

3. The system of claim 1 , wherein one or more of the at least one node device is mobile, and wherein the one or more of the at least one node device is installed on a drone, a vehicle, and/or a convoy.

4. The system of claim 1 , wherein the at least one receiver comprises a primary receiver and a secondary receiver, wherein the primary receiver is configured to generate the I/Q data for the at least one target bandwidth, and wherein the secondary receiver is configured to perform a fast Fourier transform (FFT) based on a wideband sweeping of the electromagnetic environment.

5. The system of claim 1 , wherein the at least one node device further comprises an I/Q buffer, wherein the learning and conflict detection engine is operable to determine whether to keep the I/Q data in the I/Q buffer.

6. The system of claim 1 , wherein the at least one node device further comprises a demodulator configured to distill the I/Q data and store actionable I/Q data, wherein the actionable I/Q data comprises signal metrics, protocol data, radio identification (ID), network ID and layer 3 data.

7. The system of claim 1 , wherein the learning and conflict detection engine is operable to tune the ASD module automatically.

8. The system of claim 1 , wherein the at least one report comprises a correlated event report, an alert, an alarm, meta data, channelized data, actionable I/Q data, coverage, capacity, or conflict analysis.

9. The system of claim 1 , wherein the ASD module is configured to extract meta data and detect anomalies based on the processed data.

10. The system of claim 1 , wherein the ASD module is operable for signal recognition based on temporal feature extraction.

11. The system of claim 1 , wherein the ASD module is operable to detect a narrow band signal with a bandwidth from 1 kHz to 60 kHz inside a wideband signal with a bandwidth up to 100 MHz across a 6 GHz spectrum.

12. The system of claim 1 , wherein the ASD module is operable to detect a second wideband signal within a first wideband signal, wherein the first wideband signal is an aggregation of the second wideband signal and a third wideband signal.

13. The system of claim 1 , wherein the at least one node device is operable for audio recognition.

14. An apparatus for spectrum data management for an electromagnetic environment, comprising:

at least one receiver, an automatic signal detection (ASD) module, and a learning and conflict detection engine;

wherein the apparatus is at an edge of a communication network;

wherein the apparatus is operable to sweep and learn the electromagnetic environment in a learning period based on statistical learning techniques, thereby creating learning data including power level measurements of the electromagnetic environment;

wherein the apparatus is operable to form a knowledge map based on the power level measurements of the electromagnetic environment;

wherein the apparatus is operable to scrub a spectral sweep against the knowledge map;

wherein the apparatus is operable to calculate a first derivative of the power level measurements and a second derivative of the power level measurements;

wherein the apparatus is operable to select most prominent derivatives of the first derivative and the second derivative;

wherein the apparatus is operable to smooth the spectral sweep with a correction vector, wherein the correction vector is determined according to the spectral sweep;

wherein the apparatus is operable to detect at least one signal in the electromagnetic environment based on matched positive and negative gradients;

wherein the apparatus is operable to average the spectral sweep, remove areas identified by the matched positive and negative gradients, and connect points between removed areas to determine a baseline;

wherein the apparatus is operable to subtract the baseline from the spectral sweep to reveal the at least one signal, thereby creating signal data;

wherein the apparatus is operable to process the signal data, thereby generating processed data;

wherein the processed data includes In-Phase and Quadrature (I/Q) data for at least one target bandwidth;

wherein the learning and conflict detection engine is configured for conflict recognition and anomaly identification based on the processed data; and

wherein the apparatus is operable to communicate at least one report for the electromagnetic environment to at least one remote device.

15. The apparatus of claim 14 , wherein the at least one receiver comprises a primary receiver and a secondary receiver, wherein the primary receiver is configured to generate the I/Q data for at least one target bandwidth, and wherein the secondary receiver is configured to perform a fast Fourier transform (FFT) based on a wideband sweeping of the electromagnetic environment.

16. The apparatus of claim 14 , further comprising an I/Q buffer, wherein the learning and conflict detection engine is operable to determine whether to keep the I/Q data in the I/Q buffer.

17. The apparatus of claim 14 , further comprising a demodulator configured to distill the I/Q data and store actionable I/Q data, wherein the actionable I/Q data comprises signal metrics, protocol data, radio identification (ID), network ID and layer 3 data.

18. The apparatus of claim 14 , wherein the ASD module is configured to extract meta data and detect anomalies based on the processed data.

19. The apparatus of claim 14 , wherein the at least one report comprises a correlated event report, an alert, an alarm, meta data, channelized data, actionable I/Q data, coverage, capacity, or conflict analysis.

20. An apparatus for spectrum data management for an electromagnetic environment, comprising:

at least one receiver, an automatic signal detection (ASD) module, and a learning and conflict detection engine;

wherein the apparatus is at an edge of a communication network;

wherein the apparatus is mobile;

wherein the apparatus is installed on a drone, a vehicle, and/or a convoy;

wherein the apparatus is operable to sweep and learn the electromagnetic environment in a learning period based on statistical learning techniques, thereby creating learning data including power level measurements of the electromagnetic environment;

wherein the apparatus is operable to form a knowledge map based on the power level measurements of the electromagnetic environment;

wherein the apparatus is operable to scrub a spectral sweep against the knowledge map;

wherein the apparatus is operable to calculate a first derivative of the power level measurements and a second derivative of the power level measurements;

wherein the apparatus is operable to select most prominent derivatives of the first derivative and the second derivative;

wherein the apparatus is operable to smooth the spectral sweep with a correction vector, wherein the correction vector is determined according to the spectral sweep;

wherein the apparatus is operable to detect at least one signal in the electromagnetic environment based on matched positive and negative gradients;

wherein the apparatus is operable to average the spectral sweep, remove areas identified by the matched positive and negative gradients, and connect points between removed areas to determine a baseline;

wherein the apparatus is operable to subtract the baseline from the spectral sweep to reveal the at least one signal, thereby creating signal data;

wherein the apparatus is operable to process the signal data, thereby generating processed data;

wherein the processed data includes In-Phase and Quadrature (I/Q) data for at least one target bandwidth;

wherein the learning and conflict detection engine is configured for conflict recognition and anomaly identification based on the processed data; and

wherein the apparatus is operable to communicate at least one report for the electromagnetic environment to at least one remote device.

Assignments (4)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 13, 2021
From: DZIERWA, RONALD C.
To: DIGITAL GLOBAL SYSTEMS, INC.
Reel/Frame 056233/0724 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 13, 2021
From: CARBAJAL, DANIEL
To: DIGITAL GLOBAL SYSTEMS, INC.
Reel/Frame 056233/0775 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 13, 2021
From: GARCIA, GABRIEL R.
To: DIGITAL GLOBAL SYSTEMS, INC.
Reel/Frame 056233/0782 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 15, 2021
From: KLEINBECK, DAVID WILLIAM
To: DIGITAL GLOBAL SYSTEMS, INC.
Reel/Frame 055261/0115 →
Continuity (28)
Continuation 16415549 · May 17, 2019
Continuation 15686655 · Aug 25, 2017
Continuation In Part 15681540 · Aug 21, 2017
Continuation In Part 15496660 · Apr 25, 2017
Continuation In Part 15412982 · Jan 23, 2017
Continuation In Part 14983678 · Dec 30, 2015
Continuation 14504802 · Oct 2, 2014
Continuation 14329835 · Jul 11, 2014
Continuation 14087441 · Nov 22, 2013
Continuation In Part 14082873 · Nov 18, 2013
Continuation 13912683 · Jun 7, 2013
Continuation In Part 14082916 · Nov 18, 2013
Continuation 13912893 · Jun 7, 2013
Continuation In Part 14082930 · Nov 18, 2013
Continuation 13913013 · Jun 7, 2013
Continuation In Part 15478916 · Apr 4, 2017
Continuation In Part 14934808 · Nov 6, 2015
Continuation 14504836 · Oct 2, 2014
Continuation 14331706 · Jul 15, 2014
Continuation In Part 14086875 · Nov 21, 2013
Continuation In Part 14082873 · Nov 18, 2013
Continuation In Part 14082916 · Nov 18, 2013
Continuation In Part 14082930 · Nov 18, 2013
Continuation In Part 14593202 · Jan 9, 2015
Continuation 14325044 · Jul 7, 2014
Continuation 14082916 · Nov 18, 2013
Provisional Application 61789758 · Mar 15, 2013
Related Publication 20200245167A1 · Jul 30, 2020