System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization

1. A system for spectrum management in a radio frequency (RF) environment comprising:

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

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

a semantic engine 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 learning engine, wherein the learning engine is operable to learn the RF environment;

wherein the semantic engine is operable to create a semantic map;

wherein the semantic map is formed 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 at least one alarm and/or at least one notification.

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 images.

5. The system of claim 1 , wherein the at least one data analysis engine further includes a detection engine, an identification engine, a classification engine, and/or 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 at least one rule and/or 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 , further including a programmable rules and policy editor, wherein the programmable rules and policy editor includes at least one rule and/or at least one policy.

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

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

12. 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.

13. 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.

14. 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.

15. 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.

16. 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.

17. The system of claim 1 , wherein the semantic engine further includes a language dictionary.

18. A system for spectrum management in a radio frequency (RF) environment comprising:

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

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

a semantic engine 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 learning engine, wherein the learning engine is operable to learn the RF environment;

wherein the at least one data analysis engine further includes an identification engine, a classification engine, and/or a geolocation engine;

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

wherein the semantic engine is operable to create a semantic map;

wherein the semantic map is formed 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.

19. A method for spectrum management in a radio frequency (RF) environment comprising:

providing a semantic engine;

monitoring the RF environment using at least one monitoring sensor and creating measured data based on the RF 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 learning engine, wherein the at least one data analysis engine is in network communication with the semantic engine;

learning the RF environment using the learning engine;

the semantic engine creating a semantic map, wherein the semantic map is formed using semantic fingerprinting and a Boolean vector; and

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.

20. The method of claim 19 , further including the tip and cue server activating at least one alarm and/or providing at least one notification.