Systems, methods, and devices having databases for electronic spectrum management

• US 10,257,729 B2
• Filed: 06/14/2017
• Issued: 04/09/2019
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: DGS Global Systems, Inc., Digital Global Systems, Inc.
• Original Assignee: DGS Global Systems, Inc.
• Inventors: Carbajal, Daniel, Dzierwa, Ronald C.
• Primary Examiner(s): Tsvey, Gennadiy
• Application Number:
  US15/622,173
• Publication Number:
  US 20170290075A1
• Time in Patent Office: 664 Days
• CPC Class Codes: H04L 27/00 Modulated-carrier systems
• CPC Class Codes: H04L 27/0006 Assessment of spectral gaps suitable for allocating digitally modulated signals, e.g. for carrier allocation in cognitive radio for spectrum sharing between different networks H04W16/14
• CPC Class Codes: H04W 16/14 Spectrum sharing arrangements between different networks
• CPC Class Codes: H04W 24/02 Arrangements for optimising operational condition
• CPC Class Codes: H04W 24/08 Testing, supervising or monitoring using real traffic
• CPC Class Codes: H04W 64/006 with additional information processing, e.g. for direction or speed determination
• CPC Class Codes: H04W 72/0453 Resources in frequency domain, e.g. a carrier in FDMA
• CPC Class Codes: H04W 76/11 Allocation or use of connection identifiers

Abstract

Systems, methods, and apparatus are provided for automated identification of baseline data and changes in state in a wireless communications spectrum, by identifying sources of signal emission in the spectrum by automatically detecting signals, analyzing signals, comparing signal data to historical and reference data, creating corresponding signal profiles, and determining information about the baseline data and changes in state based upon the measured and analyzed data in near real time, which is stored on each apparatus or device and/or on a remote server computer that aggregates data from each apparatus or device.

Claims

1. A method for automatic signal detection in a radio-frequency (RF) environment, comprising:

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;

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

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

creating a profile of the RF environment based on the knowledge map, wherein the profile comprises a highest power level for each frequency detected during the learning period;

scrubbing a real-time spectral sweep against the profile;

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

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

performing a squaring function on the most prominent derivatives;

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

averaging the real-time spectral sweep, removing areas identified by the matched positive and negative gradients, and 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.

2. The method of claim 1, 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.

3. The method of claim 1, wherein the at least one signal is transmitted from a remote signal emitting device and has a low power level.

4. The method of claim 1, wherein the at least one signal is a narrowband signal hidden in a wideband signal.

5. The method of claim 1, further comprising displaying the knowledge map and/or detecting results in real time on a remote device.

6. The method of claim 1, further comprising automatically fine-tuning a threshold of power level on a segmented basis while extracting at least one temporal feature from the knowledge map.

7. The method of claim 1, wherein frequency resolution of the knowledge map is based on a Fast Fourier Transform (FFT) size setting.

8. The method of claim 1, further comprising periodically reevaluating the RF environment and updating the knowledge map.

9. The method of claim 1, wherein the learning period is a predetermined period of time or a period of time required to reach a settled percent.

10. The method of claim 1, further comprising sending a notification and/or an alarm to an operator after detecting the at least one signal.

11. A system for automatic signal detection in a radio-frequency (RF) environment, comprising:

at least one apparatus for detecting signals in the RF environment;

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

wherein the at least one apparatus is operable to form a knowledge map based on the learning data power level measurements of the RF environment;

wherein the at least one apparatus is operable to create a profile of the RF environment based on the knowledge map, wherein the profile comprises a highest power level for each frequency detected during the learning period;

wherein the at least one apparatus is operable to scrub a real-time spectral sweep against the profile;

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

wherein the at least one apparatus is operable to perform a squaring function on the most prominent derivates;

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

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

wherein the at least one apparatus is operable to subtract the baseline from the real-time spectral sweep to reveal the at least one signal.

12. The system of claim 11, 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.

13. The system of claim 11, wherein the at least one signal is transmitted from a remote signal emitting device and has a low power level.

14. The system of claim 11, wherein the at least one signal is a narrowband signal hidden in a wideband signal.

15. The system of claim 11, further comprising a remote device in network-based communication with the at least one apparatus, wherein the knowledge map and/or detecting results are displayed on a remote device in real time.

16. The system of claim 11, wherein the at least one apparatus automatically fine-tunes a threshold of power level on a segmented basis while extracting at least one temporal feature from the knowledge map.

17. The system of claim 11, wherein frequency resolution of the knowledge map is based on a Fast Fourier Transform (FFT) size setting.

18. The system of claim 11, wherein the at least one apparatus periodically reevaluates the RF environment and updates the knowledge map.

19. The system of claim 11, wherein the learning period is a predetermined period of time or a period of time required to reach a settled percent.

20. The system of claim 11, wherein the at least one apparatus is operable to send a notification and/or an alarm to an operator after detecting the at least one signal.