Systems and methods for detecting, monitoring, and mitigating the presence of a drone using frequency hopping

1. A system for detecting presence of one or more drones, the system comprising:

a radio-frequency (RF) receiver configured to receive a first RF signal transmitted between a first drone and a first drone controller, the first RF signal including a first frequency hopping sequence;

a processor; and

a non-transitory computer-readable memory in communication with the processor and having stored thereon computer-executable instructions to cause the at least one processor to:

obtain a parameter model that represents the first frequency hopping sequence, the parameter model comprising a plurality of model parameters that model the first frequency hopping sequence,

fit the parameter model to a set of samples by:

constructing a plurality of instances of the parameter model, each instance associated with a different subset of samples from the set of samples, and

evaluating a fitting error between each of the subsets of samples and the associated instance of the parameter model,

select an instance from the plurality of instances based on the fitting error associated with each of the subsets of samples, and

identify the first drone based on the selected instance of the parameter model.

2. The system of claim 1 , wherein the RF receiver is further configured to receive a second RF signal transmitted between a second drone and a second drone controller, the second RF signal including a second frequency hopping sequence, wherein the set of samples further comprises samples of the second RF signal, and wherein the computer-readable memory further has stored thereon computer-executable instructions to cause the at least one processor to:

fit the parameter model to a subset of the set of samples selected based on the fitting errors by:

constructing a second set of a plurality of instances of the parameter model, each instance associated with a different second subset of samples from the set of samples, and

evaluating a fitting error between each of the second subsets of the samples and the associated instance of the parameter model,

select an instance from the second set of the plurality of instances based on the fitting error associated with each of the second subsets of samples, and

identify the second drone based on the selected instance from the second set of the plurality of instances of the parameter model.

3. The system of claim 1 , further comprising:

estimating values of the plurality of model parameters for each of the subsets of samples based on a set of predefined channel center frequencies.

4. The system of claim 3 , wherein the first frequency hopping sequence is defined by a plurality of first frequency hopping parameters comprising a start time of a first hop, a hop period between two neighboring hops, a start frequency of a first hop after an initial time, a frequency difference between the two neighboring hops, and a number of channels.

5. The system of claim 1 , further comprising:

determining a time fitting error and a frequency fitting error; and

determining a combined fitting error based on the time fitting error and the frequency fitting error.

6. The system of claim 1 , wherein the constructed plurality of instances of the parameter model that has a lowest fitting error is selected.

7. The system of claim 1 , wherein the parameter model comprises a model of one of the following: a linear frequency hopping sequence, a pseudo random frequency hopping sequences, a non-linear frequency hopping sequences, and an arbitrary linear frequency hopping sequence.

8. The system of claim 1 , further comprising a jammer configured to receive the estimated values of the plurality of model parameters for each subset of the plurality of subsets of samples and generate a jamming RF signal to disrupt communication between the first drone and the first drone controller based at least in part on the estimated values.

9. A method for detecting presence of a drone, the method comprising:

receiving a first RF signal transmitted between a first drone and a first drone controller, the first RF signal including a first frequency hopping sequence;

obtaining a parameter model that represents the first frequency hopping sequence, the parameter model comprising a plurality of model parameters that model the first frequency hopping sequence;

fitting the parameter model to a set of samples by:

constructing a plurality of instances of the parameter model, each instance associated with a different subset of samples from the set of samples, and

evaluating a fitting error between each of the subsets of samples and the associated instance of the parameter model,

selecting an instance from the plurality of instances based on the fitting error associated with each of the subsets of samples, and

identifying the first drone based on the selected instance of the parameter model.

10. The method of claim 9 , wherein the set of samples further comprises samples of a second RF signal transmitted between a second drone and a second drone controller, the second RF signal including a second frequency hopping sequence, wherein the method further comprises:

fitting the parameter model to a subset of the set of samples selected based on the fitting errors by:

constructing a second set of a plurality of instances of the parameter model, each instance associated with a different second subset of samples from the set of samples, and

evaluating a fitting error between each of the second subsets of the samples and the associated instance of the parameter model,

selecting an instance from the second set of the plurality of instances based on the fitting error associated with each of the second subsets of samples, and

identifying the second drone based on the selected instance from the second set of the plurality of instances of the parameter model.

11. The method of claim 9 , further comprising estimating values of the plurality of model parameters for each of the subsets of samples based on a set of predefined channel center frequencies.

12. The method of claim 11 , wherein the first frequency hopping sequence is defined by a plurality of first frequency hopping parameters comprising a start time of a first hop, a hop period between two neighboring hops, a start frequency of a first hop after an initial time, a frequency difference between the two neighboring hops, and a number of channels.

13. The method of claim 9 , further comprising:

determining a time fitting error and a frequency fitting error; and

determining a combined fitting error based on the time fitting error and the frequency fitting error.

14. The method of claim 9 , wherein the constructed plurality of instances of the parameter model that has a lowest fitting error is selected.

15. The method of claim 9 , wherein the parameter model comprises a model of one of the following: a linear frequency hopping sequence, a pseudo random frequency hopping sequences, a non-linear frequency hopping sequences, and an arbitrary linear frequency hopping sequence.

16. A non-transitory computer readable storage medium having stored thereon instructions that, when executed, cause a computing device to:

receive a first RF signal transmitted between a first drone and a first drone controller, the first RF signal including a first frequency hopping sequence;

obtain a parameter model that represents the first frequency hopping sequence, the parameter model comprising a plurality of model parameters that model the first frequency hopping sequence;

fit the parameter model to a set of samples by:

constructing a plurality of instances of the parameter model, each instance associated with a different subset of samples from the set of samples, and

evaluating a fitting error between each of the subsets of samples and the associated instance of the parameter model,

select an instance from the plurality of instances based on the fitting error associated with each of the subsets of samples, and

identify the first drone based on the selected instance of the parameter model.

17. The non-transitory computer readable storage medium of claim 16 , wherein the set of samples further comprises samples of a second RF signal transmitted between a second drone and a second drone controller, the second RF signal including a second frequency hopping sequence, wherein the instructions, when executed, cause the at least one computing device to:

fit the parameter model to a subset of the set of samples selected based on the fitting errors by:

constructing a second set of a plurality of instances of the parameter model, each instance associated with a different second subset of samples from the set of samples, and

evaluating a fitting error between each of the second subsets of the samples and the associated instance of the parameter model,

select an instance from the second set of the plurality of instances based on the fitting error associated with each of the second subsets of samples, and

identify the second drone based on the selected instance from the second set of the plurality of instances of the parameter model.

18. The non-transitory computer readable storage medium of claim 16 , wherein the instructions, when executed, cause the at least one computing device to estimate values of the plurality of model parameters for each of the subsets of samples based on a set of predefined channel center frequencies.

19. The non-transitory computer readable storage medium of claim 18 , wherein the first frequency hopping sequence is defined by a plurality of first frequency hopping parameters comprising a start time of a first hop, a hop period between two neighboring hops, a start frequency of a first hop after an initial time, a frequency difference between the two neighboring hops, and a number of channels.

20. The non-transitory computer readable storage medium of claim 16 , further comprising:

determine a time fitting error and a frequency fitting error; and

determine a combined fitting error based on the time fitting error and the frequency fitting error.