Systems, methods, and devices for automatic signal detection with temporal feature extraction within a spectrum
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.
1. An apparatus for automatic signal detection in an electromagnetic environment, comprising:
at least one receiver and at least one processor coupled with at least one memory;
wherein the apparatus is at an edge of a communication network;
wherein the apparatus is operable to sweep and learn the electromagnetic environment in a predetermined period, thereby creating learning data including power level measurements of the electromagnetic environment;
wherein the apparatus is operable to form a knowledge map of the electromagnetic environment based on the power level measurements of the electromagnetic environment;
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 create impressions on the electromagnetic environment based on a machine learning algorithm;
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; and
wherein the apparatus is operable to process the signal data using compressed data for deltas, thereby generating processed data in near-real time.
2. The apparatus of claim 1 , further comprising a multiplicity of receivers, wherein the multiplicity of receivers is operable to sweep multiple bandwidths and perform time-frequency analyses at the same time.
3. The apparatus of claim 1 , wherein the machine learning algorithm is an artificial neural network algorithm.
4. The apparatus of claim 1 , wherein the apparatus is operable to index the power level measurements for each frequency interval in a spectrum section in the predetermined period.
5. The apparatus of claim 1 , wherein the apparatus is mobile and linked to a GPS system for time and location.
6. The apparatus of claim 1 , wherein the apparatus is fixed, and wherein the apparatus is operable to obtain time from its own clock or a GPS system.
7. 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 spectral sweep in the electromagnetic environment.
8. The apparatus of claim 1 , wherein the apparatus is operable to evaluate a performance of a radio transmitter based on frequency deterioration.
9. The apparatus of claim 1 , wherein the at least one signal is a narrowband signal hidden in a wideband signal, and wherein the narrowband signal having a bandwidth ranging from 1 kHz to 60 kHz is inside the wideband signal with a bandwidth up to 100 MHz.
10. The apparatus of claim 1 , wherein the at least one signal is a second wideband signal within a first wideband signal, wherein the first wideband signal is an aggregation of the second wideband signal and a third wideband signal, and wherein the first, second and third wideband signals are 4G or 5G wideband signals.
11. The apparatus of claim 1 , wherein the apparatus is operable to generate at least one report automatically for the electromagnetic environment.
12. The apparatus of claim 1 , wherein the apparatus is water resistant.
13. An apparatus for automatic signal detection in an electromagnetic environment, comprising:
at least one receiver and at least one processor coupled with at least one memory;
wherein the apparatus is at an edge of a communication network;
wherein the apparatus is operable to sweep and learn the electromagnetic environment in a predetermined period 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 of the electromagnetic environment based on the power level measurements of the electromagnetic environment;
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 create impressions on the electromagnetic environment based on a machine learning algorithm;
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; and
wherein the apparatus is operable to process the signal data using compressed data for deltas, thereby generating processed data in near-real time.
14. A method for automatic signal detection in an electromagnetic environment, comprising:
learning the electromagnetic environment in a predetermined period, thereby creating learning data including power level measurements of the electromagnetic environment;
forming a knowledge map of the electromagnetic environment based on the power level measurements of the electromagnetic environment;
scrubbing a spectral sweep against the knowledge map;
calculating a first derivative of the power level measurements and a second derivative of the power level measurements;
smoothing the spectral sweep with a correction vector, wherein the correction vector is determined according to the spectral sweep;
creating impressions on the electromagnetic environment based on a machine learning algorithm;
detecting at least one signal in the electromagnetic environment based on matched positive and negative gradients;
averaging the spectral sweep, removing areas identified by the matched positive and negative gradients, and connecting points between removed areas to determine a baseline;
subtracting the baseline from the spectral sweep to reveal the at least one signal, thereby creating signal data; and
processing the signal data using compressed data for deltas, thereby generating processed data in near-real time.
15. The method of claim 13 , further comprising extracting at least one time-frequency feature from the knowledge map.
16. The method of claim 13 , wherein the at least one signal is a narrowband signal hidden in a wideband signal, and wherein the narrowband signal having a bandwidth ranging from 1 kHz to 60 kHz is inside the wideband signal with a bandwidth up to 100 MHz.
17. The method of claim 13 , 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.
18. The method of claim 13 , further comprising creating a profile of the electromagnetic environment based on the knowledge map, wherein the profile comprises a highest power level at each frequency during the predetermined period.
19. The method of claim 13 , further comprising sending a notification and/or an alarm to at least one remote device after detecting the at least one signal.
20. The method of claim 13 , further comprising indexing the power level measurements for each frequency interval in a spectrum section in the predetermined period.