Systems, methods, and devices for electronic spectrum management for identifying signal-emitting devices

• US 10,231,206 B2
• Filed: 05/16/2017
• Issued: 03/12/2019
• 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: Dzierwa, Ronald C., Carbajal, Daniel, Kleinbeck, David William
• Primary Examiner(s): Tsvey, Gennadiy
• Application Number:
  US15/596,756
• Publication Number:
  US 20170250766A1
• Time in Patent Office: 665 Days
• CPC Class Codes: H04B 17/23 Indication means, e.g. displays, alarms, audible means
• CPC Class Codes: H04B 17/26 using historical data, averaging values or statistics
• CPC Class Codes: H04B 17/318 Received signal strength
• CPC Class Codes: H04B 17/373 Predicting channel quality or other radio frequency [RF] parameters
• 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: H04L 27/0012 arrangements for identifying the type of modulation
• CPC Class Codes: H04L 27/265 Fourier transform demodulators, e.g. fast Fourier transform [FFT] or discrete Fourier transform [DFT] demodulators H04L27/26524 takes precedence
• CPC Class Codes: H04W 16/14 Spectrum sharing arrangements between different networks
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

Apparatus and methods for identifying a wireless signal-emitting device are disclosed. The apparatus is configured to sense and measure wireless communication signals from signal-emitting devices in a spectrum. The apparatus is operable to automatically detect a signal of interest from the wireless signal-emitting device and create a signal profile of the signal of interest; compare the signal profile with stored device signal profiles for identification of the wireless signal-emitting device; and calculate signal degradation data for the signal of interest based on information associated with the signal of interest in a static database including noise figure parameters of a wireless signal-emitting device outputting the signal of interest. The signal profile of the signal of interest, profile comparison result, and signal degradation data are stored in the apparatus.

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;

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;

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;

subtracting the baseline from the real-time spectral sweep to reveal the at least one signal; and

locating the at least one signal using a monitoring array comprising at least three monitoring units.

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 detected 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 a highest power level at each frequency during the learning period.

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

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

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

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

8. 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.

9. The method of claim 1, wherein locating the at least one signal using a monitoring array comprising at least three monitoring units further comprises:

the at least three monitoring units scanning independently for the at least one signal;

at least one of the at least three monitoring units acquiring and measuring the at least one signal;

the at least one of the at least three monitoring units transmitting a formatted message to other units within the monitoring array; and

the at least one of the at least three monitoring units processing measurements of the at least one signal and determining a location of a signal emitting device from which the at least one signal is emitted.

10. The method of claim 9, wherein the formatted message comprises center frequency, bandwidth, modulation schema, average power, and phase lock loop time adjustment from the at least one of the at least three monitoring units.

11. The method of claim 1, further comprising determining exact locations of the at least three monitoring units and a timing of signal processing based on GPS information received by a GPS receiver in each of the at least three monitoring units.

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

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

a monitoring array comprising at least three monitoring units;

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 power level measurements of the RF environment;

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

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 derivatives;

wherein the at least one apparatus is operable to identify 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;

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; and

wherein the at least three monitoring units determine a location of a signal emitting device from which the at least one signal is emitted.

13. The system of claim 12, 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 detected at a particular level.

14. The system of claim 12, 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.

15. The system of claim 12, wherein the learning period is a predetermined period of time or a period of time required to reach a settled percent.

16. The system of claim 12, wherein the monitoring array is deployable and/or asymmetrical.

17. The system of claim 12, wherein each of the at least three monitoring units comprise a GPS receiver for timing of signal processing and determining an exact location of the at least three monitoring units.

18. The system of claim 12, wherein:

each of the at least three monitoring units comprises an antenna;

the at least three monitoring units are operable to scan independently for the at least one signal;

each of the at least three monitoring units is operable to measure the at least one signal and transmit a formatted message to other units within the monitoring array; and

each of the at least three monitoring units is operable to process measurements of the at least one signal and determine a location of a signal emitting device from which the at least one signal is emitted.

19. The system of claim 18, wherein the formatted message comprises center frequency, bandwidth, modulation schema, average power, and phase lock loop time adjustment from at least one of the at least three monitoring units.

20. The system of claim 12, further comprising an external processor, wherein the external processor is operable to process the measurements of the at least one signal and determine the location of the signal emitting device from which the at least one signal is emitted.