Abstract: Systems, methods and apparatus for automatic alarm management in a radio-frequency (RF) environment are disclosed. An apparatus calculates 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 FFT data of the RF environment. The apparatus then creates a baseline based on the power distribution by frequency of the RF environment in a period of time, identifies at least one alarm situation based on a multiplicity of alarm triggering conditions by comparing the power distribution in real time or near real time to the baseline of the RF environment, identifies at least one signal based on the first derivative and the second derivative of FFT data in the at least one alarm situation, and sends at least one alarm comprising details of the at least one signal identified in the at least one alarm situation.

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.

Abstract: Systems, methods, and apparatus for detecting UAVs in an RF environment are disclosed. An apparatus is constructed and configured for network communication with at least one camera. The at least one camera captures images of the RF environment and transmits video data to the apparatus. The apparatus receives RF data and generates FFT data based on the RF data, identifies at least one signal based on a first derivative and a second derivative of the FFT data, measures a direction from which the at least one signal is transmitted, analyzes the video data. The apparatus then identifies at least one UAV to which the at least one signal is related based on the analyzed video data, the RF data, and the direction from which the at least one signal is transmitted, and controls the at least one camera based on the analyzed video data.

Abstract: Systems, methods, and apparatus are provided for automated identification of open space in a wireless communications spectrum, by identifying sources of signal emission in the spectrum by automatically detecting signals, analyzing signals, comparing signal data to historical and reference data, creating corresponding signal profiles, and determining information about the open space based upon the measured and analyzed data in near real-time.

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.

Abstract: Systems, methods and apparatus are disclosed for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.

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.

Abstract: Systems, methods, and apparatus are provided for automated identification of baseline data and changes in state in a wireless communications spectrum, by identifying sources of signal emission in the spectrum by automatically detecting signals, analyzing signals, comparing signal data to historical and reference data, creating corresponding signal profiles, and determining information about the baseline data and changes in state based upon the measured and analyzed data in near real time, which is stored on each apparatus or device and/or on a remote server computer that aggregates data from each apparatus or device.

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.

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.

Abstract: Apparatus and methods for identifying a wireless signal-emitting device are disclosed. The apparatus is configured to sense and measure wireless communication signals from signal-emitting devices in a spectrum. The apparatus is operable to automatically detect a signal of interest from the wireless signal-emitting device and create a signal profile of the signal of interest; compare the signal profile with stored device signal profiles for identification of the wireless signal-emitting device; and calculate signal degradation data for the signal of interest based on information associated with the signal of interest in a static database including noise figure parameters of a wireless signal-emitting device outputting the signal of interest. The signal profile of the signal of interest, profile comparison result, and signal degradation data are stored in the apparatus.

Abstract: Systems, methods, and apparatus for geolocating a signal emitting device are disclosed. A monitoring array comprises at least four monitoring units. A distance ratio between the at least four monitoring units relative to a midpoint is determined. The at least four monitoring units are operable to scan independently for a signal of interest. The at least four monitoring units are operable to calculate times of arrival and angles of arrival for the signal of interest. Each of the at least four monitoring units is operable to measure the signal of interest and transmit a formatted message to other monitoring units within the monitoring array. Each of the at least four monitoring units is operable to determine a location of the signal emitting device from which the signal of interest is emitted based on calculations and measurements relating to the signal of interest.

Abstract: Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

Abstract: Methods for tracking a signal origin by a spectrum analysis and management device are disclosed. Signal characteristics of other known emitters are used for obtaining a position of an emitter of a signal of interest. In one embodiment, frequency difference of arrival technique is implemented. In another embodiment, time difference of arrival technique is implemented.

Abstract: Devices and methods enable optimizing a signal of interest based on identifying and analyzing the signal of interest based on radio frequency energy measurements. Signal data is compared with stored data to identify the signal of interest. Signal degradation data is calculated based on noise figure parameters, hardware parameters and environment parameters. The signal of interest is optimized based on the signal degradation data. Terrain data may also be used for optimizing the signal of interest.

Abstract: Systems, methods and apparatus are disclosed for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.

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.

Abstract: Systems, methods, and apparatus are provided for automated identification of open space in a wireless communications spectrum, by identifying sources of signal emission in the spectrum by automatically detecting signals, analyzing signals, comparing signal data to historical and reference data, creating corresponding signal profiles, and determining information about the open space based upon the measured and analyzed data in near real-time.

Abstract: Systems, methods, and devices enable spectrum management by identifying, classifying, and cataloging signals of interest based on radio frequency measurements. Signal data is compared with stored data to identify the signal of interest. Signal degradation data is calculated based on noise figure parameters, hardware parameters and environment parameters.

Abstract: Systems, methods, and apparatus for detecting UAVs in an RF environment are disclosed. An apparatus is constructed and configured for network communication with at least one camera. The at least one camera captures images of the RF environment and transmits video data to the apparatus. The apparatus receives RF data and generates FFT data based on the RF data, identifies at least one signal based on a first derivative and a second derivative of the FFT data, measures a direction from which the at least one signal is transmitted, analyzes the video data. The apparatus then identifies at least one UAV to which the at least one signal is related based on the analyzed video data, the RF data, and the direction from which the at least one signal is transmitted, and controls the at least one camera based on the analyzed video data.