Systems, methods, and devices for automatic signal detection with temporal feature extraction within a spectrum

• US 10,271,233 B2
• Filed: 08/21/2017
• Issued: 04/23/2019
• 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: Kleinbeck, David William, Dzierwa, Ronald C., Carbajal, Daniel
• Primary Examiner(s): Tsvey, Gennadiy
• Application Number:
  US15/681,558
• Publication Number:
  US 20180014217A1
• Time in Patent Office: 610 Days
• CPC Class Codes: H04B 17/20 of receivers
• CPC Class Codes: H04B 17/23 Indication means, e.g. displays, alarms, audible means
• CPC Class Codes: H04B 17/24 with feedback of measurements to the transmitter
• CPC Class Codes: H04B 17/26 using historical data, averaging values or statistics
• CPC Class Codes: H04B 17/27 for locating or positioning the transmitter
• CPC Class Codes: H04B 17/29 Performance testing
• CPC Class Codes: H04B 17/309 Measuring or estimating channel quality parameters
• CPC Class Codes: H04B 17/318 Received signal strength
• CPC Class Codes: H04W 24/04 Arrangements for maintaining operational condition
• CPC Class Codes: H04W 24/08 Testing, supervising or monitoring using real traffic
• CPC Class Codes: H04W 24/10 Scheduling measurement reports ; Arrangements for measurement reports

Abstract

Systems, methods and apparatus are disclosed for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.

Claims

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

at least one receiver and at least one processor coupled with at least one memory;

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

wherein the apparatus is operable to sweep and learn the RF environment in a predetermined 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 predetermined period of time;

wherein the apparatus is operable to form a knowledge map of the RF environment 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 perform a squaring function on the most prominent derivative;

wherein the apparatus is operable to create impressions on the RF environment based on a machine learning algorithm;

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, and connect points between removed areas to determine a baseline; and

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

2. The apparatus of claim 1, further comprising a multiplicity of receivers, wherein the multiplicity of receivers is operable to sweep multiple bandwidths and perform time-frequency analyses at the same time.

3. The apparatus of claim 1, wherein the machine learning algorithm is an artificial neural network algorithm.

4. The apparatus of claim 1, wherein the apparatus is operable to detect Global Positioning System (GPS) coordinates of the apparatus.

5. The apparatus of claim 1, wherein the apparatus is mobile and linked to a GPS system for time and location.

6. The apparatus of claim 1, wherein the apparatus is fixed, and wherein the apparatus is operable to obtain time from its own clock or a GPS system.

7. The apparatus of claim 1, wherein the apparatus is operable to estimate a location of a signal emitting device from which the at least one signal is emitted based on in-phase and quadrature (I/Q) data generated from spectral sweep in the RF environment.

8. The apparatus of claim 1, wherein the apparatus is operable to evaluate a performance of a radio transmitter based on frequency deterioration.

9. The apparatus of claim 1, wherein the at least one signal is a narrowband signal hidden in a wideband signal, and wherein the narrowband signal having a bandwidth ranging from 1 kHz to 60 kHz is inside the wideband signal with a bandwidth up to 100 MHz.

10. The apparatus of claim 1, wherein the at least one signal is a second wideband signal within a first wideband signal.

11. The apparatus of claim 10, wherein the first wideband signal is an aggregation of the second wideband signal and a third wideband signal, and wherein the first, second and third wideband signals are 4G or 5G wideband signals.

12. The apparatus of claim 1, wherein the apparatus is operable to generate at least one report automatically for the RF environment.

13. The apparatus of claim 1, wherein the apparatus is water resistant.

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

learning the RF environment in a predetermined 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 predetermined period;

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

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

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;

creating impressions on the RF environment based on a machine learning algorithm;

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.

15. The method of claim 14, further comprising extracting at least one time-frequency feature from the knowledge map.

16. The method of claim 14, wherein the at least one signal is a narrowband signal hidden in a wideband signal, and wherein the narrowband signal having a bandwidth ranging from 1 kHz to 60 kHz is inside the wideband signal with a bandwidth up to 100 MHz.

17. The method of claim 14, wherein the at least one signal is a second wideband signal within a first wideband signal, and wherein the first wideband signal is an aggregation of the second wideband signal and a third wideband signal, and wherein the first, second and third wideband signals are 4G or 5G wideband signals.

18. The method of claim 14, 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.

19. The method of claim 14, further comprising creating a profile of the RF environment based on the knowledge map, wherein the profile comprises a highest power level at each frequency during the predetermined period.

20. The method of claim 14, further comprising sending a notification and/or an alarm to an operator after detecting the at least one signal.