Systems, methods, and devices for automatic signal detection with temporal feature extraction within a spectrum
First Claim
1. A method for automatic signal detection in a radio-frequency (RF) environment, comprising:
- learning 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;
indexing the power level measurements for each frequency interval in a spectrum section in the period of time;
forming a knowledge map of the RF environment based on the power level measurements of the RF environment;
automatically extracting at least one temporal feature of the RF environment from the knowledge map;
scrubbing a real-time spectral sweep against the knowledge map;
calculating a first derivative of the power level measurements and a second derivative of the power level measurements;
selecting most prominent derivatives of the first derivative and the second derivative;
performing a squaring function on the most prominent derivatives;
detecting at least one signal in the RF environment based on matched positive and negative gradients;
averaging the real-time 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 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.
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Abstract
Systems, methods and apparatus for automatic signal detection with temporal feature extraction in an RF environment are disclosed. An apparatus learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. A knowledge map is formed based on the learning data. The apparatus automatically extracts temporal features of the RF environment from the knowledge map. A real-time spectral sweep is scrubbed against the knowledge map. The apparatus is operable to detect a signal in the RF environment, which has a low power level or is a narrowband signal buried in a wideband signal, and which cannot be identified otherwise.
362 Citations
20 Claims
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1. A method for automatic signal detection in a radio-frequency (RF) environment, comprising:
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learning 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; indexing the power level measurements for each frequency interval in a spectrum section in the period of time; forming a knowledge map of the RF environment based on the power level measurements of the RF environment; automatically extracting at least one temporal feature of the RF environment from the knowledge map; scrubbing a real-time spectral sweep against the knowledge map; calculating a first derivative of the power level measurements and a second derivative of the power level measurements; selecting most prominent derivatives of the first derivative and the second derivative; performing a squaring function on the most prominent derivatives; detecting at least one signal in the RF environment based on matched positive and negative gradients; averaging the real-time 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 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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for automatic signal detection in a radio-frequency (RF) environment, comprising:
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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. - View Dependent Claims (12, 13, 14, 15)
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16. An apparatus for detecting at least one signal in a radio-frequency (RF) environment, comprising:
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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. - View Dependent Claims (17, 18, 19, 20)
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Specification