SYSTEMS, METHODS, AND DEVICES FOR AUTOMATIC SIGNAL DETECTION WITH TEMPORAL FEATURE EXTRACTION WITHIN A SPECTRUM

• US 20180211179A1
• Filed: 01/23/2017
• Published: 07/26/2018
• Est. Priority Date: 01/23/2017
• Status: Active Grant

• Current Assignee: DGS Global Systems, Inc., Digital Global Systems, Inc.
• Original Assignee: DGS Global Systems, Inc.
• Inventors: Dzierwa, Ronald C.
• Granted Patent: US 10,122,479 B2
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04B 17/30 of propagation channels
• CPC Class Codes: H04B 17/391 Modelling the propagation channel

Abstract

Systems, methods and apparatus for automatic signal detection with temporal feature extraction in an RF environment are disclosed. An apparatus learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. A knowledge map is formed based on the learning data. The apparatus automatically extracts temporal features of the RF environment from the knowledge map. A real-time spectral sweep is scrubbed against the knowledge map. The apparatus is operable to detect a signal in the RF environment, which has a low power level or is a narrowband signal buried in a wideband signal, and which cannot be identified otherwise.

Claims

1. 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;

forming a knowledge map based on the learning data;

automatically extracting at least one temporal feature of the RF environment from the knowledge map;

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

detecting at least one signal in the RF environment.

2. The method of claim 1, wherein the knowledge map comprises an array of normal distributions, wherein each normal distribution corresponds to how often the power level at each frequency has been at a particular level.

3. The method of claim 1, further comprising creating a profile of the RF environment based on the knowledge map, wherein the profile comprises the highest power level at which each frequency has been seen during the predetermined period.

4. The method of claim 3, further comprising identifying areas of transition in the profile based on gradient detection.

5. The method of claim 4, further comprising detecting the at least one signal based on matched positive and negative gradients of the profile.

6. 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.

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

8. The method of claim 7, wherein the narrowband signal having a bandwidth ranging from 1 kHz to 60 kHz is inside the wideband signal across a spectrum up to about 6 GHz.

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

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

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

12. The method of claim 1, further comprising automatically recording relevant information in high definition when the at least one signal is detected.

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

14. 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 predetermined period based on statistical learning techniques, thereby creating learning data;

wherein the at least one apparatus is operable to form a knowledge map based on the learning data;

wherein the at least one apparatus is operable to automatically extract at least one temporal feature of the RF environment from the knowledge map;

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

wherein the at least one apparatus is operable to detect at least one signal in the RF environment.

15. The system of claim 14, further comprising a remote device in network-based communication with the at least one apparatus, wherein the knowledge map and detecting results are displayed on a remote device in real time.

16. An apparatus for detecting at least one signal in a radio-frequency (RF) environment, comprising:

at least one processor coupled with at least one memory, and at least one sensor;

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;

wherein the apparatus is operable to form a knowledge map based on the learning data;

wherein the apparatus is operable to automatically extract at least one temporal feature of the RF environment from the knowledge map;

wherein the apparatus is operable to scrub a real-time spectral sweep against the knowledge map; and

wherein the apparatus is operable to detect at least one signal in the RF environment.

17. The apparatus of claim 16, wherein at least one knowledge map is stored in the apparatus.

18. The apparatus of claim 16, wherein the apparatus is operable to obtain a knowledge map by communicating with another apparatus.

19. The apparatus of claim 16, wherein the apparatus is automatic and unmanned.

20. The apparatus of claim 16, wherein the apparatus is water resistant.