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US 12,713,260 B2Granted
Systems, methods, and devices having databases and automated reports for electronic spectrum management
AssigneeDigital Global Systems, Inc.
InventorsRonald C. Dzierwa, Daniel Carbajal
Life of this patent
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Abstract
Systems and methods are disclosed for providing at least one report relating to a wireless communications spectrum. At least one device is operable for wideband scan; to detect and measure at least one signal transmitted from at least one signal emitting device autonomously, thereby creating signal data; to analyze the signal data in near real-time, thereby creating analyzed data; generate the at least one report in near real-time; and to communicate at least a portion of the at least one report over a network to at least one remote device.
Claims20
An apparatus for automatic signal detection in an electromagnetic environment, comprising:
- at least one receiver, at least one processor, and at least one memory;
- wherein the apparatus is operable to create power level measurements of the electromagnetic environment;
- wherein the apparatus is operable to learn a baseline;
- wherein the apparatus is operable to automatically detect at least one signal based on the baseline;
- wherein the apparatus is operable to create deltas that are differentials from the baseline;
- wherein the apparatus is operable to reconstruct the at least one signal using the deltas and the baseline;
- wherein the apparatus is operable to smooth signal data using a first smoothing filter;
- wherein the apparatus is operable to use gradients and a second smoothing filter to the smoothed signal data to create a calibration vector;
- wherein the apparatus is operable to use the calibration vector to de-bias the signal data;
- wherein the apparatus is operable to calculate a first derivative and second derivative of the power level measurements, wherein the apparatus is operable to select a most prominent derivative of the power level measurements, and wherein the apparatus is operable to perform a squaring function on the most prominent derivative;
- wherein the apparatus is operable to calculate signal degradation data for the at least one signal based at least in part on noise figure parameters and/or hardware parameters;
- wherein the hardware parameters comprise antenna position, antenna type, orientation, and/or effective isotropic radiated power (EIRP);
- wherein the detecting the at least one signal in the electromagnetic environment comprises fine-tuning a threshold of energy on a segmented basis based on a sensitivity;
- wherein a pre-recognition delay parameter sets a minimum number of consecutive scans of the electromagnetic environment to determine if the at least one signal is a signal of interest;
- wherein the sensitivity is operable to be set based on the pre-recognition delay;
- wherein a Temporal Feature Extraction (TFE) system is operable to utilize machine learning (ML) to automatically tune parameters and dynamically adjust the sensitivity of one or more frequency bins within the electromagnetic environment;
- and
- wherein a monitoring unit includes a GPS receiver operable to determine an exact location of the at least one signal.
15 more elements
A method for automatic signal detection in an electromagnetic environment, comprising:
- receiving power level measurements of one or more frequency bins within the electromagnetic environment using at least one receiver;
- learning a baseline of the electromagnetic environment based on the power level measurements;
- calculating a first derivative and second derivative of the power level measurements, selecting a most prominent derivative of the power level measurements, and performing a squaring function on the most prominent derivative;
- automatically detecting at least one signal based on the baseline;
- generating deltas that represent differentials from the baseline to minimize data sets or sample data required for comparisons and analytics;
- reconstructing the at least one signal using the deltas and the baseline;
- smoothing signal data using a first smoothing filter;
- applying gradients and a second smoothing filter to the smoothed signal data to generate a calibration vector to de-bias the signal data;
- setting a minimum number of consecutive scans of the electromagnetic environment using a pre-recognition delay parameter to determine if the at least one signal is a signal of interest;
- utilizing machine learning (ML) to automatically tune parameters and dynamically adjust a sensitivity of the one or more frequency bins within the electromagnetic environment via a Temporal Feature Extraction (TFE) system;
- determining an exact location of the at least one signal by a monitoring unit using a GPS receiver;
- and
- calculating signal degradation data for the at least one signal based at least in part on noise figure parameters and/or hardware parameters;
- wherein the detecting the at least one signal in the electromagnetic environment comprises fine-tuning a threshold of energy on a segmented basis based on the sensitivity;
- wherein the sensitivity is operable to be set based on the pre-recognition delay;
- and
- wherein the hardware parameters comprise antenna position, antenna type, orientation, and/or effective isotropic radiated power (EIRP).
14 more elements
A method for automatic signal detection in an electromagnetic environment, comprising:
- learning a baseline of the electromagnetic environment based on power level measurements of one or more frequency bins within the electromagnetic environment;
- calculating a first derivative and second derivative of the power level measurements, selecting a most prominent derivative of the power level measurements, and performing a squaring function on the most prominent derivative;
- automatically detecting at least one signal based on the baseline;
- creating a knowledge map of the electromagnetic environment using a machine learning (ML) algorithm, wherein the knowledge map is based on the power level measurements of the one or more frequency bins;
- generating deltas that represent differentials from the baseline to minimize data sets or sample data required for comparisons and analytics;
- reconstructing the at least one signal using the deltas and the baseline;
- smoothing signal data using a first smoothing filter;
- applying gradients and a second smoothing filter to the smoothed signal data to generate a calibration vector to de-bias the signal data;
- and
- calculating signal degradation data for the at least one signal based at least in part on noise figure parameters and/or hardware parameters;
- setting a minimum number of consecutive scans of the electromagnetic environment using a pre-recognition delay parameter to determine if the at least one signal is a signal of interest;
- utilizing ML to automatically tune parameters and dynamically adjust a sensitivity of the one or more frequency bins within the electromagnetic environment via a Temporal Feature Extraction (TFE) system;
- and
- determining an exact location of the at least one signal by a monitoring unit using a GPS receiver;
- wherein the hardware parameters comprise antenna position, antenna type, orientation, and/or effective isotropic radiated power (EIRP);
- wherein the detecting the at least one signal in the electromagnetic environment comprises fine-tuning a threshold of energy on a segmented basis based on the sensitivity;
- and
- wherein the sensitivity is operable to be set based on the pre-recognition delay.
15 more elements
Dependencies are inferred from the claim text.