Systems, methods, and devices for automatic signal detection based on power distribution by frequency over time within an electromagnetic spectrum
First Claim
1. A system for automatic signal detection in a radio-frequency (RF) environment, comprising:
- a multiplicity of node devices constructed and configured for cross-communication in a fixed nodal network; and
a remote server in network communication with the multiplicity of node devices;
wherein the multiplicity of node devices is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data;
wherein the multiplicity of node devices is operable to create a spectrum map based on the learning data;
wherein the multiplicity of node devices is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment;
wherein the multiplicity of node devices is operable to select most prominent derivatives of the first derivative and the second derivative;
wherein the multiplicity of node devices is operable to perform a squaring function on the most prominent derivatives;
wherein the multiplicity of node devices is operable to identify at least one signal based on matched positive and negative gradients;
wherein the multiplicity of node devices is operable to transmit the learning data and/or the FFT data to the remote server;
wherein the remote server is operable to perform spectrum analytics based on the learning data and/or the FFT data; and
wherein the spectrum analytics includes averaging a real-time spectral sweep, removing areas identified by the matched positive and negative gradients, connecting points between removed areas to determine a baseline, and subtracting the baseline from the real-time spectral sweep, thereby creating signal data.
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Abstract
Systems, methods, and apparatus for automatic signal detection in a radio-frequency (RF) environment are disclosed. At least one node device is in a fixed nodal network. The at least one node device is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The at least one node device is operable to create a spectrum map based on the learning data. The at least one node device is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment. The at least one node device is operable to identify at least one signal based on the first derivative and the second derivative of FFT data.
392 Citations
30 Claims
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1. A system for automatic signal detection in a radio-frequency (RF) environment, comprising:
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a multiplicity of node devices constructed and configured for cross-communication in a fixed nodal network; and a remote server in network communication with the multiplicity of node devices; wherein the multiplicity of node devices is operable to measure and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data; wherein the multiplicity of node devices is operable to create a spectrum map based on the learning data; wherein the multiplicity of node devices is operable to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment; wherein the multiplicity of node devices is operable to select most prominent derivatives of the first derivative and the second derivative; wherein the multiplicity of node devices is operable to perform a squaring function on the most prominent derivatives; wherein the multiplicity of node devices is operable to identify at least one signal based on matched positive and negative gradients; wherein the multiplicity of node devices is operable to transmit the learning data and/or the FFT data to the remote server; wherein the remote server is operable to perform spectrum analytics based on the learning data and/or the FFT data; and wherein the spectrum analytics includes averaging a real-time spectral sweep, removing areas identified by the matched positive and negative gradients, connecting points between removed areas to determine a baseline, and subtracting the baseline from the real-time spectral sweep, thereby creating signal data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A system for automatic signal detection in an electromagnetic environment, comprising:
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a multiplicity of node devices constructed and configured for mesh network communication in the electromagnetic environment; and a remote server in network communication with the multiplicity of node devices; wherein the multiplicity of node devices comprises at least one receiver and at least one processor coupled with at least one memory; wherein the multiplicity of node devices is operable to measure the electromagnetic environment in a predetermined period, thereby creating measurement data; wherein the multiplicity of node devices is operable to create a spectrum map based on the measurement data; wherein the multiplicity of node devices is operable to calculate a power distribution by frequency of the electromagnetic environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the electromagnetic environment; wherein the multiplicity of node devices is operable to select most prominent derivatives of the first derivative and the second derivative; wherein the multiplicity of node devices is operable to perform a squaring function on the most prominent derivatives; wherein the multiplicity of node devices is operable to identify at least one signal based on matched positive and negative gradients; wherein the multiplicity of node devices is operable to transmit the measurement data and/or the FFT data to the remote server; wherein the remote server is operable to perform spectrum analytics based on the measurement data and/or the FFT data; and wherein the spectrum analytics includes averaging a real-time spectral sweep, removing areas identified by the matched positive and negative gradients, connecting points between removed areas to determine a baseline, and subtracting the baseline from the real-time spectral sweep, thereby creating signal data. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. A method for automatic signal detection in an electromagnetic environment, comprising:
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providing a multiplicity of node devices constructed and configured for mesh network communication in the electromagnetic environment; and providing a remote server in network communication with the multiplicity of node devices; the multiplicity of node devices measuring and learning the electromagnetic environment in a predetermined period based on statistical learning techniques, thereby creating learning data; the multiplicity of node devices creating a spectrum map based on the learning data; the multiplicity of node devices calculating a power distribution by frequency of the electromagnetic environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the electromagnetic environment; the multiplicity of node devices selecting most prominent derivatives of the first derivative and the second derivative; the multiplicity of node devices performing a squaring function on the most prominent derivatives; the multiplicity of node devices identifying at least one signal based on matched positive and negative gradients; the multiplicity of node devices transmitting the learning data and/or the FFT data to the remote server; and the remote server averaging a real-time spectral sweep, removing areas identified by the matched positive and negative gradients, connecting points between removed areas to determine a baseline, and subtracting the baseline from the real-time spectral sweep, thereby creating signal data. - View Dependent Claims (26, 27, 28, 29, 30)
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Specification