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

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

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

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

a tip and cue server;

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

wherein the tip and cue server is operable to use analyzed data from the at least one data analysis 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 mounted on a drone, a vehicle, in or on a street light, in or on a traffic pole, and/or on top of a building.

5. 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.

6. The system of claim 1 , wherein the geolocation engine is operable to determine the location of the at least one signal of interest based on an angle of arrival, a time difference of arrival, a frequency difference of arrival, and power distribution ratio measurements.

7. The system of claim 1 , wherein the geolocation engine is operable to use statistical approximations to remove error causes from noise, timing and power measurements, multipath, and non-line of sight (NLOS) measurements.

8. The system of claim 7 , wherein the statistical approximations include maximum likelihood, nearest neighbor, Kalman filter, least squares approximation, or Bayesian filter.

9. The system of claim 1 , wherein the geolocation engine includes passive methods of geolocation, wherein the passive methods of geolocation include single directional beam antenna response, multidirectional beam antenna response, multi-antenna element response, line of bearing (LOB)-to-position solutions, general optimization, phase interferometry, beamforming, conventional array manifold processing approaches, and/or high-resolution array manifold processing approaches using signals subspace, digital pre-distortion (DPD), convex programming, and/or distributed swarm approaches.

10. The system of claim 1 , wherein the geolocation engine is operable to utilize graphical geolocation techniques, and wherein the graphical geolocation techniques include an image comparison between a two-dimensional map or a three-dimensional map of possible outputs and the analyzed data.

11. The system of claim 10 , wherein the graphical geolocation techniques include topology to create the three-dimensional map of the possible outputs.

12. The system of claim 1 , wherein the geolocation engine is operable to use spinning direction finding via rotating directional antennas and estimate a direction of arrival of an emitter of the at least one signal of interest.

13. The system of claim 1 , wherein the geolocation engine is operable to use amplitude ratio methods for geolocation using a set of fixed directional antennas pointing in different directions.

14. The system of claim 1 , wherein the geolocation engine is operable to solve a cross-ambiguity function to determine the location of the at least one signal of interest.

15. The system of claim 14 , wherein an area including an emitter of the at least one signal of interest is operable to be divided into at least one grid including a plurality of receiver points, results of the cross-ambiguity function at one or more of the plurality of receiver points are operable to be precomputed, actual cross-ambiguity functions based on the measured data are operable to be calculated, and the actual cross-ambiguity functions are operable to be compared to the precomputed results of the cross-ambiguity function to select a best match to determine the location of the at least one signal of interest.

16. The system of claim 15 , wherein the emitter is stationary or mobile.

17. The system of claim 15 , wherein the plurality of receiver points are stationary and/or mobile.

18. The system of claim 14 , wherein the cross-ambiguity function is computed using the measured data, peaks of the cross-ambiguity function are operable to be found and parameterized, and a set of non-linear equations are operable to be solved to determine the location of the at least one signal of interest.

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

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

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

a tip and cue server;

wherein the at least one data analysis engine includes a detection engine, an identification engine, a geolocation engine, and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, wherein the learning engine is operable to learn the electromagnetic environment, and wherein the geolocation engine is operable to determine a location of the at least one signal of interest using at least one cross-ambiguity function; and

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

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

monitoring and capturing 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 learning engine, and a geolocation engine;

learning the electromagnetic environment using the learning engine;

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

the geolocation engine determining a location of the at least one signal of interest by solving a cross-ambiguity function; and

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

21. The method of claim 20 , wherein solving the cross-ambiguity function includes dividing an area including an emitter of the at least one signal of interest into at least one grid including a plurality of receiver points, precomputing results of the cross-ambiguity function at one or more of the plurality of receiver points, calculating actual cross-ambiguity functions based on the measured data, and comparing the actual cross-ambiguity functions to the precomputed results of the cross-ambiguity function to select a best match.

22. The method of claim 21 , wherein the emitter is stationary or mobile.

23. The method of claim 21 , wherein the plurality of receiver points are stationary and/or mobile.

24. The method of claim 20 , wherein solving the cross-ambiguity function includes computing the cross-ambiguity function using the measured data, finding peaks of the cross-ambiguity function, parameterizing the peaks, and solving a set of non-linear equations.