Systems, methods, and devices having databases and automated reports for electronic spectrum management
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.
1. 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 one or more frequency bins within 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 a knowledge map of the electromagnetic environment based on a machine learning algorithm;
wherein the knowledge map is based on the power level measurements of the one or more frequency bins within the electromagnetic environment;
wherein the apparatus is operable to create deltas that are differentials from the baseline;
wherein the apparatus is operable to process signal data associated with the at least one signal using compressed data for the deltas;
wherein the apparatus is operable to reconstruct the at least one signal using the deltas and the baseline;
wherein the apparatus is operable to fill gaps during reconstruction of the at least one signal where the signal data is absent;
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 create a calibration vector;
wherein the apparatus is operable to use the calibration vector, the first smoothing filter, and the second smoothing filter to de-bias raw signal data;
wherein the deltas provide for minimization of data sets or sample data required for comparisons and/or analytics;
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 a most prominent derivative of the first derivative and the second derivative, and wherein the apparatus is operable to perform a squaring function on the most prominent derivative;
wherein the apparatus includes a Temporal Feature Extraction (TFE) system operable to utilize machine learning; and
wherein the TFE system is operable to automatically tune parameters and dynamically adjust sensitivity of the one or more frequency bins within the electromagnetic environment.
2. The apparatus of claim 1 , further comprising a multiplicity of receivers, wherein the multiplicity of receivers is operable to monitor multiple bandwidths and perform time-frequency analyses at the same time.
3. The apparatus of claim 1 , wherein the apparatus is operable to determine if the at least one signal is moving using a frequency-locked loop by determining if there is a Doppler change in the at least one signal.
4. The apparatus of claim 1 , wherein the apparatus is operable to estimate a location of a signal emitting device from which the at least one signal is emitted based on in-phase and quadrature (I/Q) data generated from a spectral sweep.
5. The apparatus of claim 1 , wherein the deltas provide for interference identification, neighboring band identification, device identification, and/or signal optimization in near real time.
6. The apparatus of claim 1 , wherein the apparatus is operable to provide automatic modulation detection.
7. The apparatus of claim 1 , wherein the machine learning algorithm provides for automatic signal variance determination.
8. The apparatus of claim 1 , further comprising a temporal anomaly detector operable to learn channels in the electromagnetic environment.
9. The apparatus of claim 1 , wherein the apparatus is operable to implement a learning routine, wherein the learning routine includes the power level measurements as an index into each distribution column corresponding to each frequency bin and incrementing a counter in a location corresponding to a power level.
10. The apparatus of claim 1 , wherein the apparatus is operable to distinguish signals based on gradients from a moving noise floor without a fixed threshold bar.
11. The apparatus of claim 1 , wherein the machine learning algorithm stores a learning map that is operable to update at a predetermined timeframe.