IP Library › Granted Patent US 12,003,990

Granted Patent B2

US 12,003,990 · App. 18/086,196 · Granted Jun 4, 2024

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.

H04W24/08 H04B17/20 H04B17/309 H04B17/318 H04W4/029 H04W16/14 H04W24/10 H04W64/006 H04B17/23 H04B17/27 H04B17/3911

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Quick Facts

Patent No.: US 12,003,990
App. No.: 18/086,196
Filed: Dec 21, 2022
Granted: Jun 4, 2024
Kind: B2
Examiner: TSVEY, GENNADIY
Art Unit: 2648
USPC: 455/67.11

External Links

USPTO Patent Center↗ Full Patent PDF↗

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 (77)

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

at least one node 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 operable to sweep and learn the electromagnetic environment, 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 knowledge map comprises an array of normal distributions, wherein each normal distribution corresponds to how often a power level at each frequency has been at a particular level;

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 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 at least one node device is operable to process the signal data using compressed data for deltas, thereby generating the processed data in near-real time;

wherein the at least one node device is operable to calculate and store signal degradation data for the at least one signal based at least in part on noise figure parameters, hardware parameters, and environmental parameters;

wherein the processed data includes In-Phase and Quadrature (I/Q) data;

wherein the at least one node device is operable to determine a signal's modulation type and protocol using the I/Q data;

wherein the I/Q data is equalized to remove multipath, fading, white noise, and/or interference from at least one other signaling system by a fast parallel adaptive filter; and

wherein one or more of the at least one node device is mobile.

2. The system of claim 1 , wherein the learning and conflict detection engine is configured for conflict recognition and anomaly identification based on the processed data.

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

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

10. The system of claim 1 , wherein the at least one node device is operable to communicate at least one report for the electromagnetic environment to at least one remote device.

11. The system of claim 10 , 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.

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

13. 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 operable to sweep and learn the electromagnetic environment, 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 knowledge map comprises an array of normal distributions, wherein each normal distribution corresponds to how often a power level at each frequency has been at a particular level;

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 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 apparatus is operable to process the signal data using compressed data for deltas, thereby generating the processed data in near-real time;

wherein the apparatus is operable to calculate and store signal degradation data for the at least one signal based at least in part on noise figure parameters, hardware parameters, and environmental parameters;

wherein the processed data includes In-Phase and Quadrature (I/Q) data;

wherein the apparatus is operable to generate a fast convolution periodogram using the I/Q data;

wherein the apparatus is operable to use the fast convolution periodogram to compute spectral density;

wherein the I/Q data is equalized to remove multipath, fading, white noise, and/or interference from at least one other signaling system by a fast parallel adaptive filter; and

wherein the apparatus is mobile.

14. The apparatus of claim 13 , 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 13 , 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 13 , 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 13 , 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 13 , wherein the ASD module is configured to extract meta data and detect anomalies based on the processed data.

19. The apparatus of claim 13 , wherein the apparatus is installed on a drone, a vehicle, and/or a convoy.

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 operable to sweep and learn the electromagnetic environment based on statistical learning techniques, thereby creating learning data including power level measurements of the electromagnetic environment;