Granted Patent B2

US 11,445,378 · App. 17/461,267 · Granted Sep 13, 2022

Inventors: Armando Montalvo (Winter Garden, FL); Dwight Inman (Travelers Rest, SC); Edward Hummel (Hillsborough, NJ)

Assignee: DIGITAL GLOBAL SYSTEMS, INC.

H04W16/10G06F30/27G06N3/02G06N5/022G06N5/04G06N20/00G06N20/10G06N20/20H04L41/0893H04W24/02H04W72/0453G06N3/0427G06N3/0454H04L41/0894H04W16/14H04W24/08

Abstract

Claims (64)

1. A system for spectrum management in an electromagnetic environment comprising:

at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment;

at least one data analysis engine for analyzing the measured data;

a semantic engine including a language dictionary and a programmable rules and policy editor, wherein the semantic engine is in network communication with the at least one data analysis engine; and

a tip and cue server;

wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment;

wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer;

wherein the semantic engine is operable to create a semantic map including target data using semantic fingerprinting and a Boolean vector; and

wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine and information from the semantic engine to create actionable data.

2. The system of claim 1 , wherein the tip and cue server is operable to activate an alarm and/or provide at least one report based on the actionable data.

3. The system of claim 1 , wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.

4. The system of claim 1 , wherein one or more of the at least one monitoring sensor is integrated with at least one camera to capture video and/or still images.

5. The system of claim 1 , wherein the at least one data analysis engine further includes an identification engine, a classification engine, and a geolocation engine.

6. The system of claim 1 , further including a resource brokerage application, wherein the resource brokerage application is operable to optimize resources to improve performance of at least one customer application and/or at least one customer device.

7. The system of claim 1 , further including a certification and compliance application, wherein the certification and compliance application is operable to determine if at least one customer application and/or at least one customer device is behaving according to the at least one rule and/or the at least one policy.

8. The system of claim 1 , further including a survey occupancy application, wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy in at least one frequency band.

9. The system of claim 1 , wherein the actionable data indicates that one or more of the at least one signal of interest is behaving like a drone.

10. The system of claim 1 , wherein the semantic engine is operable to receive queries, searches, and/or search-related functions using natural language.

11. The system of claim 1 , wherein the learning engine is operable to use machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.

12. The system of claim 1 , wherein the learning engine is operable to compute a set of possible conditional probabilities depicting a set of all possible outputs based on input measurements to provide a predicted outcome using a data model, and wherein the predicted outcome represents an outcome with the least probability of error and/or a false alarm.

13. The system of claim 1 , wherein the learning engine is operable to use third party data, wherein the third party data includes social media, population, real estate, traffic, geographic information system (GIS), network, signal site, site issue, and/or crowdsourced information.

14. The system of claim 1 , wherein the learning engine is operable to determine whether a data set processed and/or analyzed represents a sufficient statistical data set.

15. The system of claim 1 , wherein the learning engine includes a learning engine software development kit (SDK) operable to manage system resources relating to monitoring, logging, and/or organizing learning aspects of the system.

16. A system for spectrum management in an electromagnetic environment comprising:

at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment;

at least one data analysis engine for analyzing the measured data;

a semantic engine including a language dictionary and a programmable rules and policy editor, wherein the semantic engine is in network communication with the at least one data analysis engine; and

a tip and cue server;

wherein the at least one data analysis engine includes a detection engine, an identification engine, a classification engine, a geolocation engine, and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment;

wherein the learning engine is operable to learn information from the detection engine, the classification engine, the identification engine, and/or the geolocation engine;

wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer;

wherein the semantic engine is operable to create a semantic map including target data using semantic fingerprinting and a Boolean vector; and

wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine and information from the semantic engine to create actionable data.

17. The system of claim 16 , wherein the learning engine is operable to use machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.

18. The system of claim 16 , wherein the learning engine is operable to use third party data, wherein the third party data includes social media, population, real estate, traffic, geographic information system (GIS), network, signal site, site issue, and/or crowdsourced information.

19. The system of claim 16 , wherein the learning engine is operable to determine whether a data set processed and/or analyzed represents a sufficient statistical data set.

20. The system of claim 16 , wherein the learning engine includes a learning engine software development kit (SDK) operable to manage system resources relating to monitoring, logging, and/or organizing learning aspects of the system.

21. A method for spectrum management in an electromagnetic environment comprising:

providing a semantic engine including a language dictionary and a programmable rules and policy editor, wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer;

monitoring the electromagnetic environment using at least one monitoring sensor and creating measured data based on the electromagnetic environment;

analyzing the measured data using at least one data analysis engine, thereby creating analyzed data, wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the at least one data analysis engine is in network communication with the semantic engine;

learning the electromagnetic environment using the learning engine;

automatically detecting at least one signal of interest using the detection engine;

the semantic engine creating a semantic map including target data using semantic fingerprinting and a Boolean vector;

creating actionable data using a tip and cue server based on the analyzed data from the at least one data analysis engine and information from the semantic engine.

22. The method of claim 21 , further including the tip and cue server activating an alarm and/or providing at least one report based on the actionable data.

23. The method of claim 21 , wherein the learning engine is operable to use machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.

24. The method of claim 21 , wherein the learning engine is operable to use third party data, wherein the third party data includes social media, population, real estate, traffic, geographic information system (GIS), network, signal site, site issue, and/or crowdsourced information.

25. The method of claim 21 , further including the learning engine managing system resources relating to monitoring, logging, and/or organizing learning aspects of the system using a learning engine software development kit (SDK).

26. The method of claim 21 , further including the learning engine determining whether a data set processed and/or analyzed represents a sufficient statistical data set.

27. A method for spectrum management in an electromagnetic environment comprising:

providing a semantic engine including a language dictionary and a programmable rules and policy editor, wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer; monitoring the electromagnetic environment using at least one monitoring sensor and creating measured data based on the electromagnetic environment;

analyzing the measured data using at least one data analysis engine, thereby creating analyzed data, wherein the at least one data analysis engine includes a detection engine, a classification engine, an identification engine, a geolocation engine, and a learning engine, wherein the at least one data analysis engine is in network communication with the semantic engine;

learning the electromagnetic environment using the learning engine;

automatically detecting at least one signal of interest using the detection engine;

classifying the at least one signal of interest using the classification engine;

identifying the at least one signal of interest using the identification engine;

determining a location of the at least one signal of interest using the geolocation engine;

the semantic engine creating a semantic map including target data using semantic fingerprinting and a Boolean vector;

creating actionable data using a tip and cue server based on the analyzed data from the at least one data analysis engine and information from the semantic engine;

wherein the learning engine is operable to learn information from the detection engine, the classification engine, the identification engine, and/or the geolocation engine.

28. The method of claim 27 , wherein the learning engine is operable to use machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.

29. The method of claim 27 , wherein the learning engine is operable to use third party data, wherein the third party data includes social media, population, real estate, traffic, geographic information system (GIS), network, signal site, site issue, and/or crowdsourced information.

30. The method of claim 27 , further including the learning engine managing system resources relating to monitoring, logging, and/or organizing learning aspects of the system using a learning engine software development kit (SDK).

Assignments (1)

Continuity (3)

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