Systems, methods, and devices for automatic signal detection with temporal feature extraction within a spectrum

An apparatus for automatic signal detection in an electromagnetic environment, comprising:

• at least one receiver and at least one processor coupled with at least one memory;
• wherein the apparatus is at an edge of a communication network;
• wherein the apparatus is operable to sweep and learn the electromagnetic environment in a predetermined period based on statistical learning techniques, thereby creating learning data including power level measurements of the electromagnetic environment;
• wherein the apparatus is operable to form a knowledge map of the electromagnetic environment based on the power level measurements of the electromagnetic environment;
• wherein the apparatus is operable to scrub a spectral sweep against the knowledge map;
• wherein the apparatus is operable to calculate a first derivative of the power level measurements and a second derivative of the power level measurements;
• wherein the apparatus is operable to smooth the spectral sweep with a correction vector, wherein the correction vector is determined according to the spectral sweep;
• wherein the apparatus is operable to create impressions on the electromagnetic environment based on a machine learning algorithm;
• wherein the apparatus is operable to detect at least one signal in the electromagnetic environment based on matched positive and negative gradients;
• wherein the apparatus is operable to average the spectral sweep, remove areas identified by the matched positive and negative gradients, and connect points between removed areas to determine a baseline;
• wherein the apparatus is operable to subtract the baseline from the spectral sweep to reveal the at least one signal, thereby creating signal data;
• and
• wherein the apparatus is operable to process the signal data using compressed data for deltas, thereby generating processed data in near-real time.

A method for automatic signal detection in an electromagnetic environment, comprising:

• learning the electromagnetic environment in a predetermined period, thereby creating learning data including power level measurements of the electromagnetic environment;
• forming a knowledge map of the electromagnetic environment based on the power level measurements of the electromagnetic environment;
• scrubbing a spectral sweep against the knowledge map;
• calculating a first derivative of the power level measurements and a second derivative of the power level measurements;
• smoothing the spectral sweep with a correction vector, wherein the correction vector is determined according to the spectral sweep;
• creating impressions on the electromagnetic environment based on a machine learning algorithm;
• detecting at least one signal in the electromagnetic environment based on matched positive and negative gradients;
• averaging the spectral sweep, removing areas identified by the matched positive and negative gradients, and connecting points between removed areas to determine a baseline;
• subtracting the baseline from the spectral sweep to reveal the at least one signal, thereby creating signal data;
• and
• processing the signal data using compressed data for deltas, thereby generating processed data in near-real time.