Systems and methods for detecting, monitoring, and mitigating the presence of a drone using frequency hopping
Systems and methods for detecting, monitoring, and mitigating the presence of a drone are provided herein. In one aspect, a system for detecting presence of a one or more drones includes a radio-frequency (RF) receiver configured to receive an RF signal transmitted between a drone and a controller. The system can further include a processor and a computer-readable memory in communication with the processor and having stored thereon computer-executable instructions to cause the at least one processor to receive a set of samples from the RF receiver for a time interval, the set of samples comprising samples of the first RF signal, obtain a parameter model of the first frequency hopping parameters, and fit the parameter model to the set of samples.
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 defined by a plurality of first frequency hopping parameters;
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:
receive a set of samples from the RF receiver for a time interval, the set of samples comprising samples of the first RF signal,
obtain a parameter model that represents the first frequency hopping sequence transmitted between the first drone and the first drone controller, the parameter model comprising a plurality of model parameters respectively modelling the first frequency hopping parameters,
fit the parameter model to the set of samples by iterating over the following:
randomly selecting a first plurality of the samples from the set of samples,
estimating values of the plurality of model parameters based on the selected first plurality of the samples,
constructing a first instance of the parameter model, the first instance of the parameter model comprising the estimated values of the plurality of model parameters, and
classifying the first plurality of the samples as inliers or outliers by evaluating fitting errors between the first plurality of the samples and the constructed first instance of the parameter model,
selecting one of the first instances of the parameter model based on the inliers and outliers for each of the constructed first instances of the parameter model, and
identifying the first drone based on the selected first 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 defined by a plurality of second frequency hopping parameters, 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 corresponding to the outliers that do not fit the selected first instance of the parameter model by iterating over the following:
randomly selecting a second plurality of samples from the subset,
estimating the values of the plurality of model parameters based on the selected second plurality of samples,
constructing a second instance of the parameter model based on the estimated values of the plurality of model parameters, and
classifying the second plurality of the samples as inliers or outliers by evaluating fitting errors between the second plurality of the samples and the second instance of the parameter model,
selecting one of the second instances of the parameter model based on the inliers and outliers for each of the constructed second instances of the parameter model, and
identifying the second drone based on the selected second instance of the parameter model.
3. The system of claim 1 , wherein the estimating of the values of the plurality of model parameters is further based on a set of predefined channel center frequencies.
4. The system of claim 3 , wherein the plurality of first frequency hopping parameters comprise 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 , wherein the classifying of the first plurality of the samples as inliers or outliers comprises:
determine a time fitting error and a frequency fitting error for each sample of the first plurality of the samples based on the constructed first instance of the parameter model,
determine a combined fitting error for each sample of the first plurality of the samples based on the time fitting error and the frequency fitting error of the corresponding sample, and
classify each sample of the first plurality of the samples as the inlier or the outlier based on a comparison of the corresponding combined fitting error to a fitting error threshold.
6. The system of claim 1 , wherein the constructed first instance of the parameter model having a greatest number of inliers 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 and generate a jamming RF signal to disrupt communication between the first drone and the first drone controller based on the estimated values of the plurality of model parameters.
9. A method for detecting presence of a drone, the method comprising:
receiving a set of samples for a time interval, the set of samples comprising samples of a first RF signal transmitted between a first drone and a first drone controller, the first RF signal including a first frequency hopping sequence defined by a plurality of first frequency hopping parameters;
obtaining a parameter model that represents the first frequency hopping sequence transmitted between the first drone and the first drone controller, the parameter model comprising a plurality of model parameters respectively modelling the first frequency hopping parameters;
fitting the parameter model to the set of samples by iterating over the following:
randomly selecting a first plurality of the samples from the set of samples,
estimating values of the plurality of model parameters based on the selected first plurality of the samples,
constructing a first instance of the parameter model, the first instance of the parameter model comprising the estimated values of the plurality of model parameters, and
classifying the first plurality of the samples as inliers or outliers by evaluating fitting errors between the first plurality of the samples and the constructed first instance of the parameter model;
selecting one of the first instances of the parameter model based on the inliers and outliers for each of the constructed first instances of the parameter model; and
identifying the first drone based on the selected first 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 defined by a plurality of second frequency hopping parameters, wherein the method further comprises:
fitting the parameter model to a subset of the set of samples corresponding to the outliers that do not fit the selected first instance of the parameter model by iterating over the following:
randomly selecting a second plurality of samples from the subset,
estimating the values of the plurality of model parameters based on the selected second plurality of samples,
constructing a second instance of the parameter model based on the estimated values of the plurality of model parameters, and
classifying the second plurality of the samples as inliers or outliers by evaluating fitting errors between the second plurality of the samples and the second instance of the parameter model;
selecting one of the second instances of the parameter model based on the inliers and outliers for each of the constructed second instances of the parameter model; and
identifying the second drone based on the selected second instance of the parameter model.
11. The method of claim 9 , wherein the estimating of the values of the plurality of first frequency hopping parameters is further based on a set of predefined channel center frequencies.
12. The method of claim 11 , wherein the plurality of first frequency hopping parameters comprise 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 , wherein the classifying of the first plurality of the samples as inliers or outliers comprises:
determining a time fitting error and a frequency fitting error for each sample of the first plurality of the samples based on the constructed first instance of the parameter model;
determining a combined fitting error for each sample of the first plurality of the samples based on the time fitting error and the frequency fitting error of the corresponding sample; and
classifying each sample of the first plurality of the samples as the inlier or the outlier based on a comparison of the corresponding combined fitting error to a fitting error threshold.
14. The method of claim 9 , wherein the constructed first instance of the parameter model having a greatest number of inliers 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 set of samples for a time interval, the set of samples comprising samples of a first RF signal transmitted between a first drone and a first drone controller, the first RF signal including a first frequency hopping sequence defined by a plurality of first frequency hopping parameters;
obtain a parameter model PM that represents the first frequency hopping sequence transmitted between the first drone and the first drone controller, the parameter model comprising a plurality of model parameters respectively modelling the first frequency hopping parameters;
fit the parameter model to the set of samples by iterating over the following:
randomly selecting a first plurality of the samples from the set of samples,
estimating values of the plurality of model parameters based on the selected first plurality of the samples,
constructing a first instance of the parameter model, the first instance of the parameter model comprising the estimated values of the plurality of model parameters, and
classifying the first plurality of the samples as inliers or outliers by evaluating fitting errors between the first plurality of the samples and the constructed first instance of the parameter model;
select one of the first instances of the parameter model based on the inliers and outliers for each of the constructed first instances of the parameter model; and
identify the first drone based on the selected first 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 defined by a plurality of second frequency hopping parameters, 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 corresponding to the outliers that do not fit the selected first instance of the parameter model by iterating over the following:
randomly selecting a second plurality of samples from the subset,
estimating the values of the plurality of model parameters based on the selected second plurality of samples,
constructing a second instance of the parameter model based on the estimated values of the plurality of model parameters, and
classifying the second plurality of the samples as inliers or outliers by evaluating fitting errors between the second plurality of the samples and the second instance of the parameter model;
select one of the second instances of the parameter model based on the inliers and outliers for each of the constructed second instances of the parameter model; and
identify the second drone based on the selected second instance of the parameter model.
18. The non-transitory computer readable storage medium of claim 16 , wherein the estimating of the values of plurality of first frequency hopping parameters is further based on a set of predefined channel center frequencies.
19. The non-transitory computer readable storage medium of claim 18 , wherein the plurality of first frequency hopping parameters comprise 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 , wherein the classifying of the first plurality of the samples as inliers or outliers comprises:
determine a time fitting error and a frequency fitting error for each sample of the first plurality of the samples based on the constructed first instance of the parameter model;
determine a combined fitting error for each sample of the first plurality of the samples based on the time fitting error and the frequency fitting error of the corresponding sample; and
classify each sample of the first plurality of the samples as the inlier or the outlier based on a comparison of the corresponding combined fitting error to a fitting error threshold.