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

A system for automatic signal detection in a radio-frequency (RF) environment, comprising:

• at least one apparatus for detecting signals in the RF environment;
• wherein the at least one apparatus is operable to sweep and learn the RF environment in a period of time to a settled percent of at least 99.95% based on statistical learning techniques, thereby creating learning data including power level measurements of the RF environment;
• wherein the at least one apparatus is operable to index the power level measurements for each frequency interval in a spectrum section in the period of time;
• wherein the at least one apparatus is operable to form a knowledge map based on the power level measurements of the RF environment;
• wherein the at least one apparatus is operable to automatically extract at least one temporal feature of the RF environment from the knowledge map;
• wherein the at least one apparatus is operable to scrub a real-time spectral sweep against the knowledge map;
• wherein the at least one apparatus is operable to calculate a first derivative of the power level measurements and a second derivative of the power level measurements;
• wherein the at least one apparatus is operable to select most prominent derivatives of the first derivative and the second derivative;
• wherein the at least one apparatus is operable to perform a squaring function on the most prominent derivatives;
• wherein the at least one apparatus is operable to detect at least one signal in the RF environment based on matched positive and negative gradients;
• wherein the at least one apparatus is operable to average the real-time spectral sweep, remove areas identified by the matched positive and negative gradients, and connect points between removed areas to determine a baseline;
• wherein the at least one apparatus is operable to subtract the baseline from the real-time spectral sweep to reveal the at least one signal;
• and
• wherein one or more of the at least one signal is a narrowband signal hidden in a wideband signal.

An apparatus for detecting at least one signal in a radio-frequency (RF) environment, comprising:

• at least one processor coupled with at least one memory, and at least one sensor;
• wherein the apparatus is operable to sweep and learn the RF environment in a period of time based on statistical learning techniques, thereby creating learning data including power level measurements of the RF environment;
• wherein the apparatus is operable to index the power level measurements for each frequency interval in a spectrum section in the period of time;
• wherein the apparatus is operable to form a knowledge map of the RF environment based on the power level measurements of the RF environment;
• wherein the apparatus is operable to automatically extract at least one temporal feature of the RF environment from the knowledge map;
• wherein the apparatus is operable to scrub a real-time 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 select most prominent derivatives of the first derivative and the second derivative;
• wherein the apparatus is operable to perform a squaring function on the most prominent derivatives;
• wherein the apparatus is operable to detect at least one signal in the RF environment based on matched positive and negative gradients;
• wherein the apparatus is operable to average the real-time 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 real-time spectral sweep to reveal the at least one signal;
• and
• wherein one or more of the at least one signal is a narrowband signal hidden in a wideband signal.