Granted Patent B2

US 10,945,146 · App. 16/692,444 · Granted Mar 9, 2021

Inventors: David William Kleinbeck (Lees Summit, MO); Ronald C. Dzierwa (Baltimore, MD); Daniel Carbajal (Severna Park, MD)

Assignee: DIGITAL GLOBAL SYSTEMS, INC.

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

Abstract

Claims (58)

1. An apparatus for spectrum data management for a radio frequency (RF) 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 RF environment in a learning period based on statistical learning techniques, thereby creating learning data including power level measurements of the RF environment;

wherein the apparatus is operable to index the power level measurements for each frequency interval in a spectrum section in the learning period;

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

wherein the apparatus is operable to scrub a real-time 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 detect at least one signal in the RF environment based on matched positive and negative gradients;

wherein the apparatus is operable to average the real-time spectral sweep, remove areas identified by the matched positive and negative gradients, connect points between removed areas to determine a baseline, and subtract the baseline from the real-time spectral sweep to reveal the at least one signal, thereby creating signal data;

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

wherein the ASD module is configured to extract meta data and detect at least one anomaly based on the processed data;

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 generate at least one report for the RF environment.

2. The apparatus of claim 1 , wherein the apparatus is fixed.

3. The apparatus of claim 1 , wherein the apparatus is mobile, and wherein the apparatus is installed on a drone, a vehicle, and/or a convoy.

4. The apparatus 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 In-Phase and Quadrature (I/Q) data for at least one target bandwidth based on the learning and conflict detection engine, and wherein the secondary receiver is configured to perform a fast Fourier transform (FFT) based on a wideband sweeping of the RF environment.

5. The apparatus of claim 4 , 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.

6. The apparatus of claim 4 , 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.

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

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

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

10. The apparatus of claim 1 , wherein the ASD module is operable to detect a narrowband 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.

11. The apparatus of claim 1 , wherein the apparatus is operable to detect intermodulation.

12. The apparatus of claim 1 , wherein the apparatus is operable for audio recognition.

13. The apparatus of claim 1 , wherein the at least one report includes power usage, an RF survey, variance, interference detection, intermodulation detection, uncorrelated licenses, and/or open space identification.

14. An apparatus for spectrum data management for a radio frequency (RF) 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 RF environment to a settled percent in a learning period based on statistical learning techniques, thereby creating learning data including power level measurements of the RF environment;

wherein the apparatus is operable to index the power level measurements for each frequency interval in a spectrum section in the learning period;

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

wherein the apparatus is operable to scrub a real-time 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 detect at least one signal in the RF environment based on matched positive and negative gradients;

wherein the apparatus is operable to average the real-time spectral sweep, remove areas identified by the matched positive and negative gradients, connect points between removed areas to determine a baseline, and subtract the baseline from the real-time spectral sweep to reveal the at least one signal, thereby creating signal data;

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

wherein the ASD module is configured to extract meta data and detect at least one anomaly based on the processed data;

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 generate at least one report for the RF environment.

15. A method of spectrum data management for a radio-frequency (RF) environment, comprising:

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

the node device learning the RF environment in a learning period based on statistical learning techniques, thereby creating learning data including power level measurements of the RF environment;

the node device indexing the power level measurements for each frequency interval in a spectrum section in the learning period;

the node device forming a knowledge map of the RF environment based on the power level measurements of the RF environment;

the node device scrubbing a real-time spectral sweep against the knowledge map;

the node device calculating a first derivative of the power level measurements and a second derivative of the power level measurements;

the node device selecting most prominent derivatives of the first derivative and the second derivative;

the node device detecting at least one signal in the RF environment based on matched positive and negative gradients;

the node device averaging the real-time spectral sweep, removing areas identified by the matched positive and negative gradients, connecting points between removed areas to determine a baseline, and subtracting the baseline from the real-time spectral sweep to reveal the at least one signal, thereby creating signal data;

the node device processing the signal data using compressed data for deltas, thereby creating processed data in near-real time;

the node device detecting and identifying at least one anomaly based on the processed data; and

the node device generating at least one report for the RF environment.

16. The method of claim 15 , further comprising the node device transmitting the at least one report to a data center.

17. The method of claim 15 , further comprising the node device extracting time-frequency features of the RF environment during a learning period.

18. The method of claim 15 , wherein the ASD module is operable to detect a narrowband 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.

19. The method of claim 15 , wherein the at least one report comprises an alert, an alarm, meta data, channelized data, actionable I/Q data, and/or a correlated event report.

20. The method of claim 15 , further comprising the node device generating I/Q data for at least one target bandwidth determined by the learning and conflict detection engine; the node device distilling the I/Q data and storing actionable I/Q data; and the node device performing a fast Fourier transform (FFT) based on a wideband sweeping of the RF environment and extracting meta data based on FFT data.

Assignments (2)

Continuity (24)

Cited By (2)

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