SYSTEM, METHOD, AND APPARATUS FOR PROVIDING DYNAMIC, PRIORITIZED SPECTRUM MANAGEMENT AND UTILIZATION
Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.
1 . A system for spectrum analysis in an electromagnetic environment comprising:
a radio receiver front-end subsystem configured to create processed data based on the electromagnetic environment; and
a spectral correlation density (SCD) analysis engine operable to analyze the processed data to detect and/or classify at least one signal;
wherein the SCD analysis engine is operable to use machine learning (ML) to compare the at least one signal to at least one second signal.
2 . The system of claim 1 , wherein the SCD analysis engine uses a fast Fourier transform (FFT) Accumulation Method (FAM) to analyze the processed data.
3 . The system of claim 1 , further comprising a frequency domain programmable channelizer.
4 . The system of claim 3 , wherein the frequency domain programmable channelizer includes at least one channel definition, at least one channelization vector, at least one fast Fourier transform (FFT) configuration, at least one deference matrix, at least one detector configuration, and/or at least one channel detection.
5 . The system of claim 3 , wherein the SCD analysis engine is included in the frequency domain programmable channelizer.
6 . The system of claim 1 , wherein the SCD analysis engine is operable to use a convolutional neural network (CNN).
7 . The system of claim 1 , wherein the SCD analysis engine is operable to detect transmitted signals with cyclo-stationary properties.
8 . The system of claim 1 , wherein the SCD analysis engine is operable to detect patterns in magnitude of a mid frequency (f) and cyclic frequency/alpha (a) graph.
9 . The system of claim 1 , wherein the SCD analysis engine is operable to determine cyclo-stationary moments and a ratio of the cyclo-stationary moments.
10 . The system of claim 1 , wherein the at least one signal and/or the at least one second signal is an orthogonal frequency-division multiplexing (OFDM) signal or a Quadrature Phase Shift Keying (QPSK) signal.
11 . The system of claim 1 , further comprising a blind detection engine, wherein the SCD analysis engine is included in the blind detection engine.
12 . The system of claim 1 , wherein the system is operable to identify interference.
13 . A system for spectrum analysis in an electromagnetic environment comprising:
a radio receiver configured to create processed data based on the electromagnetic environment; and
a spectral correlation density (SCD) analysis engine operable to detect and/or classify at least one signal;
wherein the SCD analysis engine is operable to use machine learning (ML) to compare the at least one signal to at least one second signal; and
wherein the at least one second signal is operable to be previously identified and/or stored in a database.
14 . The system of claim 13 , wherein the SCD analysis engine is operable to identify interference in the electromagnetic environment.
15 . The system of claim 13 , wherein the SCD analysis engine is operable to detect transmitted signals with cyclo-stationary properties.
16 . The system of claim 13 , wherein the SCD analysis engine is operable to use a convolutional neural network (CNN).
17 . The system of claim 13 , wherein the SCD analysis engine is operable to utilize a cyclic autocorrelation function (CAF) for signal detection and processing.
18 . A method for spectrum analysis in an electromagnetic environment comprising:
creating processed data based on the electromagnetic environment; and
analyzing the processed data using a spectral correlation density (SCD) analysis engine to detect and/or classify at least one signal;
wherein the SCD analysis engine is operable to use machine learning (ML) for image comparison of the at least one signal to at least one second signal.
19 . The method of claim 18 , further comprising channelizing the processed data using a channelizer.
20 . The method of claim 19 , wherein the channelizer is a frequency domain programmable channelizer.