Systems, methods, and devices having databases and automated reports for electronic spectrum management

An apparatus for spectrum data management for a radio frequency (RF) environment, comprising:

• at least one receiver;
• wherein the apparatus is operable to detect at least one signal in the RF environment;
• wherein the apparatus is operable to smooth a real-time spectral sweep with a correction vector and a first smoothing filter;
• wherein the apparatus is operable to perform a second smoothing filter only on frequencies outside a frequency range of the at least one signal;
• wherein the apparatus is operable to apply a gradient detection algorithm to smoothed signal data based on a machine learning (ML) algorithm;
• wherein the apparatus is operable to use gradients from the gradient detection algorithm and the first smoothing filter to create a calibration vector;
• wherein the apparatus is operable to subtract a baseline from the real-time spectral sweep to reveal the at least one signal;
• wherein the apparatus is operable to add the at least one signal to a signal observation database based on a pre-recognition delay;
• wherein the apparatus is operable to process power level measurements of the RF environment using compressed data for deltas;
• wherein the apparatus is operable to determine if the at least one signal is noise based on the pre-recognition delay;
• wherein the apparatus is operable to calculate signal degradation data for the at least one signal based at least in part on hardware parameters;
• wherein the hardware parameters comprise antenna position, antenna type, orientation, and/or effective isotropic radiated power (EIRP);
• and
• wherein the apparatus is configured for conflict recognition and anomaly identification.

A method of spectrum data management for a radio-frequency (RF) environment, comprising:

• a node device comprising at least one receiver creating learning data of the RF environment;
• the node device detecting at least one signal in the RF environment based on a machine learning (ML) algorithm;
• the node device averaging a real-time spectral sweep, removing areas identified by matched positive and negative gradients, and connecting points between removed areas to determine a baseline;
• the node device smoothing the real-time spectral sweep with a correction vector and a first smoothing filter;
• the node device applying a second smoothing filter only on frequencies outside a frequency range of the at least one signal;
• the node device using a calibration vector to de-bias raw signal data based on the ML algorithm;
• the node device subtracting the baseline from the real-time spectral sweep to reveal the at least one signal;
• the node device processing power level measurements of the RF environment using compressed data for deltas;
• the node device determining if the at least one signal is noise based on a pre-recognition delay;
• and
• the node device calculating signal degradation data for the at least one signal based at least in part on hardware parameters;
• wherein the hardware parameters comprise antenna position, antenna type, orientation, and/or effective isotropic radiated power (EIRP).