# US 12,003,990 B2 · Systems, methods, and devices for electronic spectrum management > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,003,990 B2 - Document ID: US12003990B2 - Status: Granted - Kind: B2 - Application number: 18/086,196 - Filed: 2022-12-21 - Published: 2023-04-27 - Granted: 2024-06-04 - Earliest claimed: 2013-03-15 - Assignees: Digital Global Systems, Inc. - Applicants: Digital Global Systems, Inc. - Inventors: David William Kleinbeck; Ronald C. Dzierwa; Gabriel R. Garcia; Daniel Carbajal - Web page: https://patentgrep.com/patent/US12003990B2 ## CPC - H04W 24/08: Testing, {supervising or monitoring} using real traffic - H04B 17/201: {for measurement of specific parameters of the receiver or components thereof} - H04B 17/318: Received signal strength - H04B 17/347: {Path loss} - H04W 4/029: Location-based management or tracking services - H04W 16/14: Spectrum sharing arrangements {between different networks} - H04W 24/10: Scheduling measurement reports {; Arrangements for measurement reports} - H04W 64/006: {with additional information processing, e.g. for direction or speed determination} - H04B 17/23: Indication means, e.g. displays, alarms, audible means - H04B 17/27: for locating or positioning the transmitter - H04B 17/3911: {Fading models or fading generators} - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - H04B: TRANSMISSION ## Abstract Systems, methods, and devices enable spectrum management by identifying, classifying, and cataloging signals of interest based on radio frequency measurements. In an embodiment, signals and the parameters of the signals may be identified and indications of available frequencies may be presented to a user. In another embodiment, the protocols of signals may also be identified. In a further embodiment, the modulation of signals, data types carried by the signals, and estimated signal origins may be identified. ## Claims ### Claim 1 A system for managing spectrum data for an electromagnetic environment, comprising: at least one node device; wherein the at least one node device comprises at least one receiver, an automatic signal detection (ASD) module, and a learning and conflict detection engine; wherein the at least one node device is operable to sweep and learn the electromagnetic environment, thereby creating learning data including power level measurements of the electromagnetic environment; wherein the at least one node device is operable to form a knowledge map based on the power level measurements of the electromagnetic environment; wherein the knowledge map comprises an array of normal distributions, wherein each normal distribution corresponds to how often a power level at each frequency has been at a particular level; wherein the at least one node device is operable to scrub a spectral sweep against the knowledge map; wherein the at least one node device 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 node device is operable to smooth the spectral sweep with a correction vector, wherein the correction vector is determined according to the spectral sweep; wherein the at least one node device is operable to detect at least one signal in the electromagnetic environment based on matched positive and negative gradients; wherein the at least one node device 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 at least one node device is operable to subtract the baseline from the spectral sweep to reveal the at least one signal, thereby creating signal data; wherein the at least one node device is operable to process the signal data, thereby generating processed data; wherein the at least one node device is operable to process the signal data using compressed data for deltas, thereby generating the processed data in near-real time; wherein the at least one node device is operable to calculate and store signal degradation data for the at least one signal based at least in part on noise figure parameters, hardware parameters, and environmental parameters; wherein the processed data includes In-Phase and Quadrature (I/Q) data; wherein the at least one node device is operable to determine a signal's modulation type and protocol using the I/Q data; wherein the I/Q data is equalized to remove multipath, fading, white noise, and/or interference from at least one other signaling system by a fast parallel adaptive filter; and wherein one or more of the at least one node device is mobile. ### Claim 2 (depends on 1) The system of claim 1, wherein the learning and conflict detection engine is configured for conflict recognition and anomaly identification based on the processed data. ### Claim 3 (depends on 1) The system of claim 1, wherein the one or more of the at least one node device is installed on a drone, a vehicle, and/or a convoy. ### Claim 4 (depends on 1) The system of claim 1, wherein the at least one receiver comprises a primary receiver and a secondary receiver, wherein the primary receiver is configured to generate the I/Q data for at least one target bandwidth, and wherein the secondary receiver is configured to perform a fast Fourier transform (FFT) based on a wideband sweeping of the electromagnetic environment. ### Claim 5 (depends on 1) The system of claim 1, wherein the at least one node device further comprises an I/Q buffer, wherein the learning and conflict detection engine is operable to determine whether to keep the I/Q data in the I/Q buffer. ### Claim 6 (depends on 1) The system of claim 1, wherein the at least one node device further comprises a demodulator configured to distill the I/Q data and store actionable I/Q data, wherein the actionable I/Q data comprises signal metrics, protocol data, radio identification (ID), network ID and layer 3 data. ### Claim 7 (depends on 1) The system of claim 1, wherein the learning and conflict detection engine is operable to tune the ASD module automatically. ### Claim 8 (depends on 1) The system of claim 1, wherein the ASD module is configured to extract meta data and detect anomalies based on the processed data. ### Claim 9 (depends on 1) The system of claim 1, wherein the ASD module is operable for signal recognition based on temporal feature extraction. ### Claim 10 (depends on 1) The system of claim 1, wherein the at least one node device is operable to communicate at least one report for the electromagnetic environment to at least one remote device. ### Claim 11 (depends on 10) The system of claim 10, wherein the at least one report comprises a correlated event report, an alert, an alarm, meta data, channelized data, actionable I/Q data, coverage, capacity, or conflict analysis. ### Claim 12 (depends on 1) The system of claim 1, wherein the at least one node device is operable for audio recognition. ### Claim 13 An apparatus for spectrum data management for an electromagnetic environment, comprising: at least one receiver, an automatic signal detection (ASD) module, and a learning and conflict detection engine; wherein the apparatus is operable to sweep and learn the electromagnetic environment, thereby creating learning data including power level measurements of the electromagnetic environment; wherein the apparatus is operable to form a knowledge map based on the power level measurements of the electromagnetic environment; wherein the knowledge map comprises an array of normal distributions, wherein each normal distribution corresponds to how often a power level at each frequency has been at a particular level; 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 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; wherein the apparatus is operable to process the signal data, thereby generating processed data; wherein the apparatus is operable to process the signal data using compressed data for deltas, thereby generating the processed data in near-real time; wherein the apparatus is operable to calculate and store signal degradation data for the at least one signal based at least in part on noise figure parameters, hardware parameters, and environmental parameters; wherein the processed data includes In-Phase and Quadrature (I/Q) data; wherein the apparatus is operable to generate a fast convolution periodogram using the I/Q data; wherein the apparatus is operable to use the fast convolution periodogram to compute spectral density; wherein the I/Q data is equalized to remove multipath, fading, white noise, and/or interference from at least one other signaling system by a fast parallel adaptive filter; and wherein the apparatus is mobile. ### Claim 14 (depends on 13) The apparatus of claim 13, wherein the apparatus is operable to communicate at least one report for the electromagnetic environment to at least one remote device. ### Claim 15 (depends on 13) The apparatus of claim 13, wherein the at least one receiver comprises a primary receiver and a secondary receiver, wherein the primary receiver is configured to generate the I/Q data for at least one target bandwidth, and wherein the secondary receiver is configured to perform a fast Fourier transform (FFT) based on a wideband sweeping of the electromagnetic environment. ### Claim 16 (depends on 13) The apparatus of claim 13, further comprising an I/Q buffer, wherein the learning and conflict detection engine is operable to determine whether to keep the I/Q data in the I/Q buffer. ### Claim 17 (depends on 13) The apparatus of claim 13, further comprising a demodulator configured to distill the I/Q data and store actionable I/Q data, wherein the actionable I/Q data comprises signal metrics, protocol data, radio identification (ID), network ID and layer 3 data. ### Claim 18 (depends on 13) The apparatus of claim 13, wherein the ASD module is configured to extract meta data and detect anomalies based on the processed data. ### Claim 19 (depends on 13) The apparatus of claim 13, wherein the apparatus is installed on a drone, a vehicle, and/or a convoy. ### Claim 20 An apparatus for spectrum data management for an electromagnetic environment, comprising: at least one receiver, an automatic signal detection (ASD) module, and a learning and conflict detection engine; wherein the apparatus is at an edge of a communication network; wherein the apparatus is mobile; wherein the apparatus is operable to sweep and learn the electromagnetic environment 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 based on the power level measurements of the electromagnetic environment; wherein the knowledge map comprises an array of normal distributions, wherein each normal distribution corresponds to how often a power level at each frequency has been at a particular level; 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 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; wherein the apparatus is operable to process the signal data, thereby generating processed data; wherein the apparatus is operable to process the signal data using compressed data for deltas, thereby generating the processed data in near-real time; wherein the apparatus is operable to calculate and store signal degradation data for the at least one signal based at least in part on noise figure parameters, hardware parameters, and environmental parameters; wherein the processed data includes In-Phase and Quadrature (I/Q) data; wherein the apparatus is operable to determine a signal's modulation type and protocol using the I/Q data; wherein the apparatus is operable to generate a fast convolution periodogram using the I/Q data; wherein the apparatus is operable to use the fast convolution periodogram to compute spectral density; wherein the I/Q data is equalized to remove multipath, fading, white noise, and/or interference from at least one other signaling system by a fast parallel adaptive filter; and wherein the apparatus is operable to communicate at least one report for the electromagnetic environment to at least one remote device.