Machine-learning based analysis, prediction, and response based on electromagnetic waveforms
A method includes determining, based at least in part on parameters of a software defined radio (SDR), waveform data descriptive of an electromagnetic waveform. The method also includes generating feature data based on the waveform data. The method further includes providing the feature data as input to a first machine learning model to predict a future action of a device associated with at least a portion of the electromagnetic waveform and initiating a response action based on the predicted future action.
1. A method comprising:
determining, based at least in part on parameters of a software defined radio (SDR), waveform data descriptive of an electromagnetic waveform, wherein the waveform data includes first particular data identifying one or more physical layer attributes;
generating feature data based on the waveform data, wherein the feature data includes a first feature vector generated based on the first particular data;
providing the feature data as input to a first machine learning model to predict a future action of a device associated with at least a portion of the electromagnetic waveform; and
initiating a response action based on the predicted future action.
2. The method of claim 1 , further comprising selecting the parameters of the SDR using a second machine-learning model that is trained to tune the SDR to scan an electromagnetic spectrum for waveforms of interest.
3. The method of claim 1 , further comprising determining the waveform data by providing a digital representation of at least a portion of the electromagnetic waveform as input to a third machine-learning model that is trained to generate the waveform data.
4. The method of claim 1 , further comprising determining the waveform data by comparing a digital representation of at least a portion of the electromagnetic waveform to mapping data.
5. The method of claim 1 , wherein the predicted future action includes transmission of a signal and initiating the response action comprises initiating transmission of a jamming signal based on predicted characteristics of the signal.
6. The method of claim 1 , wherein the predicted future action includes retransmitting the electromagnetic waveform based on stored waveform data.
7. The method of claim 1 , wherein the predicted future action includes transmission of a signal and initiating the response action comprises cooperating with a plurality of devices to transmit a plurality of jamming signals with different characteristics based on a set of predicted characteristics of the signal.
8. The method of claim 1 , wherein initiating the response action comprises storing data indicating a predicted future position or movement direction associated with the device.
9. The method of claim 1 , wherein initiating the response action comprises transmitting a command to one or more peer devices to initiate a swarm response to the device.
10. The method of claim 1 , wherein the response action comprises estimating a future position of the device and directing response resources based on the estimated future position.
11. The method of claim 1 , wherein the first machine learning model comprises one or more decision trees, one or more neural networks, one or more support vector machines, or one or more prototypical networks.
12. A system comprising:
one or more interfaces configured to receive an electromagnetic waveform;
one or more processors; and
one or more memory devices accessible to the one or more processors, the one or more memory devices storing instructions that are executable by the one or more processors to cause the one or more processors to:
determine, based at least in part on parameters of a software defined radio (SDR), waveform data descriptive of the electromagnetic waveform, wherein the waveform data includes first particular data identifying one or more physical layer attributes;
generate feature data based on the waveform data, wherein the feature data includes a first feature vector generated based on the first particular data;
provide the feature data as input to a first machine learning model to predict a future action of a device associated with at least a portion of the electromagnetic waveform; and
initiate a response action based on the predicted future action.
13. The system of claim 12 , wherein initiating the response action comprises storing data indicating a predicted future position or movement direction associated with the device.
14. The system of claim 12 , wherein initiating the response action comprises transmitting a command to one or more peer devices to initiate a swarm response to the device.
15. The system of claim 12 , wherein the response action comprises estimating a future position of the device and directing response resources based on the estimated future position.
16. The system of claim 12 , wherein the first machine learning model comprises one or more decision trees, one or more neural networks, one or more support vector machines, or one or more prototypical networks.
17. The system of claim 12 , further comprising one or more antennas coupled to the one or more interfaces.
18. The system of claim 12 , wherein the one or more interfaces, the one or more processors, and the one or more memory devices are integrated within an unmanned vehicle.
19. The system of claim 12 , wherein the device corresponds to a target recipient of a signal encoded in the electromagnetic waveform.
20. The system of claim 12 , wherein the device corresponds to a transmitter of a signal encoded in the electromagnetic waveform.
21. A computer-readable storage device storing instructions that are executable by one or more processors to perform operations comprising:
determining, based at least in part on parameters of a software defined radio (SDR), waveform data descriptive of an electromagnetic waveform, wherein the waveform data includes first particular data identifying one or more physical layer attributes;
generating feature data based on the waveform data, wherein the feature data includes a first feature vector generated based on the first particular data;
providing the feature data as input to a first machine learning model to predict a future action of a device associated with at least a portion of the electromagnetic waveform; and
initiating a response action based on the predicted future action.
22. The computer-readable storage device of claim 21 , wherein the operations further comprise, before generating the feature data, determining that the portion of the electromagnetic waveform encodes a signal by providing a digital representation of at least the portion of the electromagnetic waveform as input to a fourth machine-learning model that is trained to detect signals in electromagnetic waveforms.