Unmanned vehicle recognition and threat management
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.
1. A system for signal identification in a radiofrequency (RF) environment, comprising:
at least one node device including a processor and at least one memory in communication with at least one RF receiver;
wherein the at least one RF receiver is operable to capture RF data in the RF environment and transmit the RF data to the at least one node device;
wherein the at least one node device is operable to average Fast Fourier Transform (FFT) data derived from the RF data into at least one tile;
wherein the at least one tile is visually represented as at least one waterfall image;
wherein the at least one node device is operable to analyze the at least one waterfall image using machine learning (ML) or at least one convolutional neural network (CNN) to identify at least one signal, at least one signal type, and/or noise to create at least one analyzed waterfall image; and
wherein the at least one analyzed waterfall image includes a visual indication of the at least one signal, the at least one signal type, and/or the noise.
2. The system of claim 1 , further comprising a display operable to display the at least one analyzed waterfall image.
3. The system of claim 1 , wherein the at least one node device is operable to estimate a geographic location for at least one signal emitting device of the at least one signal.
4. The system of claim 1 , wherein the at least one signal type includes a drone signal type.
5. The system of claim 1 , wherein the visual indication of the at least one signal or the at least one signal type is indicated with highlighting on the at least one analyzed waterfall image.
6. The system of claim 1 , wherein the analysis of the at least one waterfall image using the ML or the at least one CNN includes a comparison of the at least one waterfall image to at least one other waterfall image.
7. The system of claim 6 , wherein the at least one node device is operable to update a database including the at least one other waterfall image with the at least one waterfall image and/or the at least one analyzed waterfall image.
8. The system of claim 1 , wherein the at least one node device is operable to transmit an alert related to the at least one signal or the at least one signal type.
9. The system of claim 1 , wherein the at least one RF receiver is operable to transform the RF data to the FFT data.
10. An apparatus for signal identification in a radiofrequency (RF) environment, comprising:
a node device including a processor and at least one memory;
wherein the node device is operable to receive RF data from at least one RF receiver;
wherein the node device is operable to average Fast Fourier Transform (FFT) data derived from the RF data into at least one tile;
wherein the at least one tile is represented as at least one waterfall image;
wherein the node device waterfall image is analyzed using machine learning (ML) or at least one convolutional neural network (CNN) to identify at least one signal, at least one signal type, and/or noise to create at least one analyzed waterfall image; and
wherein the at least one analyzed waterfall image includes a visual indication of the at least one signal, the at least one signal type, and/or the noise.
11. The apparatus of claim 10 , further comprising a display operable to display the at least one analyzed waterfall image.
12. The apparatus of claim 10 , wherein the node device is operable to estimate a geographic location for at least one signal emitting device of the at least one signal.
13. The apparatus of claim 10 , wherein the visual indication of the at least one signal or the at least one signal type is indicated with highlighting on the at least one analyzed waterfall image.
14. The apparatus of claim 10 , wherein the analysis of the at least one waterfall image using the ML or the at least one CNN includes a comparison of the at least one waterfall image to at least one other waterfall image.
15. The apparatus of claim 14 , wherein the node device is operable to update a database including the at least one other waterfall image with the at least one analyzed waterfall image.
16. A method for signal analysis in a radiofrequency (RF) environment, comprising:
at least one RF receiver capturing RF data in the RF environment, converting the RF data to Fast Fourier Transform (FFT) data, and transmitting the FFT data to at least one node device;
the at least one node device averaging the FFT data derived from the RF data into at least one tile, wherein the at least one tile is represented as at least one waterfall image;
the at least one node device analyzing the at least one waterfall image using machine learning (ML) or at least one convolutional neural network (CNN) to identify at least one signal, at least one signal type, and/or noise; and
the at least one node device creating at least one analyzed waterfall image based on the identification of the at least one signal, the at least one signal type, and/or the noise;
wherein the at least one analyzed waterfall image includes a visual indication of the at least one signal, the at least one signal type, and/or the noise.
17. The method of claim 16 , further comprising displaying the at least one analyzed waterfall image.
18. The method of claim 16 , further comprising estimating a geographic location for at least one signal emitting device of the at least one signal.
19. The method of claim 16 , wherein the at least one node device analyzing the at least one waterfall image using the ML or the at least one CNN to identify the at least one signal, the at least one signal type, and/or the noise includes comparing the at least one waterfall image to at least one other waterfall image.
20. The method of claim 19 , further comprising updating a database including the at least one other waterfall image with the at least one analyzed waterfall image.