Systems, methods, and devices for electronic spectrum management
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.
1. A method for automatic signal detection in a radio-frequency (RF) environment, comprising:
learning the RF environment in a learning period based on statistical learning techniques, thereby creating learning data including power level measurements of the RF environment;
forming a knowledge map based on the power level measurements of the RF environment;
creating a channel plan based on the knowledge map, user input, and/or external databases;
scrubbing a real-time spectral sweep against the knowledge map;
calculating a first derivative of the power level measurements and a second derivative of the power level measurements;
selecting most prominent derivatives of the first derivative and the second derivative;
performing a squaring function on the most prominent derivatives;
detecting at least one signal in the RF environment based on matched positive and negative gradients;
averaging the real-time 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 real-time spectral sweep to reveal the at least one signal; and
determining that one or more of the at least one signal violates the channel plan.
2. The method of claim 1 , 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.
3. The method of claim 1 , further comprising creating a profile of the RF environment based on the knowledge map, wherein the profile comprises a highest power level at each frequency during the learning period.
4. The method of claim 1 , further comprising automatically extracting at least one temporal feature of the RF environment from the knowledge map.
5. The method of claim 1 , further comprising automatically fine-tuning a threshold of power level on a segmented basis while extracting at least one temporal feature from the knowledge map.
6. The method of claim 1 , further comprising indexing the power level measurements for each frequency interval in a spectrum section in the learning period.
7. The method of claim 1 , further comprising periodically reevaluating the RF environment and updating the knowledge map.
8. The method of claim 1 , further comprising sending a notification and/or an alarm to an operator after detecting the at least one signal.
9. The method of claim 1 , further comprising displaying the knowledge map and/or detecting results in real time on a remote device.
10. The method of claim 1 , wherein the learning period is a predetermined period of time or a period of time required to reach a settled percent.
11. A system for automatic signal detection in a radio-frequency (RF) environment, comprising:
at least one apparatus for detecting signals in the RF environment;
wherein the at least one apparatus is operable to sweep and learn the RF environment in a learning period based on statistical learning techniques, thereby creating learning data including power level measurements of the RF environment;
wherein the at least one apparatus is operable to form a knowledge map based on the power level measurements of the RF environment;
wherein the at least one apparatus is operable to create a channel plan based on the knowledge map, user input, and/or external databases;
wherein the at least one apparatus is operable to scrub a real-time spectral sweep against the knowledge map;
wherein the at least one apparatus 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 apparatus is operable to select most prominent derivatives of the first derivative and the second derivative;
wherein the at least one apparatus is operable to perform a squaring function on the most prominent derivatives;
wherein the at least one apparatus is operable to detect at least one signal in the RF environment based on matched positive and negative gradients;
wherein the at least one apparatus is operable to average the real-time 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 apparatus is operable to subtract the baseline from the real-time spectral sweep to reveal the at least one signal; and
wherein the at least one apparatus is operable to determine that one or more of the at least one signal violates the channel plan.
12. The system of claim 11 , 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.
13. The system of claim 11 , wherein the at least one apparatus and/or a remote device is operable to create a profile of the RF environment based on the knowledge map, wherein the profile comprises a highest power level at each frequency during the learning period.
14. The system of claim 11 , wherein the at least one apparatus is operable to automatically extract at least one temporal feature of the RF environment from the knowledge map.
15. The system of claim 11 , wherein the at least one apparatus automatically fine-tunes a threshold of power level on a segmented basis while extracting at least one temporal feature from the knowledge map.
16. The system of claim 11 , wherein the at least one apparatus is operable to index the power level measurements for each frequency interval in a spectrum section in the learning period.
17. The system of claim 11 , wherein the at least one apparatus periodically reevaluates the RF environment and updates the knowledge map.
18. The system of claim 11 , wherein the at least one apparatus is operable to send a notification and/or an alarm to an operator after detecting the at least one signal.
19. The system of claim 11 , further comprising a remote device in network-based communication with the at least one apparatus, wherein the knowledge map and/or detecting results are displayed on the remote device in real time.
20. The system of claim 11 , wherein the learning period is a predetermined time period or a period of time to reach a settled percent.