Systems and methods for detecting, monitoring, and mitigating the presence of a drone using frequency hopping
Inventors
Lo, Brandon Fang-Hsuan • Torborg, Scott • Au Yeung, Chun Kin
Assignees
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Publication Number
US-11894870-B2
Publication Date
2024-02-06
Expiration Date
Abstract
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.
Core Innovation
The invention relates to a system for detecting presence of one or more drones using a radio-frequency (RF) receiver that receives RF signals transmitted between a drone and a drone controller, where the RF signal includes a frequency hopping sequence. The system obtains a parameter model representing the frequency hopping sequence, with the parameter model comprising a plurality of model parameters that model the frequency hopping sequence.
The system fits the parameter model to a set of samples by constructing a plurality of instances of the parameter model. Each instance is associated with a different subset of samples, a fitting error is evaluated between each subset and the associated instance, and an instance is selected based on the fitting error to identify the drone based on the selected instance of the parameter model.
The invention estimates values of the model parameters using predefined channel center frequencies for each subset of samples and characterizes the frequency hopping sequence using multiple frequency hopping parameters. Such parameters include 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 neighboring hops, and a number of channels, and a combined fitting error is determined from a time fitting error and a frequency fitting error.
Claims Coverage
The partial content includes three independent claims, covering a system, a method, and a non-transitory computer-readable storage medium. Across these independent claims, the coverage centers on fitting a frequency hopping parameter model to sample subsets by evaluating fitting error across multiple parameter-model instances and selecting an instance to identify the drone, with dependent refinements covering parameter estimation, explicit hopping parameterization, and combined error metrics.
Fitting a frequency-hopping parameter model with subset-based instances
The system, method, and storage medium each receive an RF signal including a frequency hopping sequence, obtain a parameter model comprising a plurality of model parameters, fit the parameter model to a set of samples by constructing a plurality of instances each associated with a different subset of samples, evaluate a fitting error between each subset and the associated instance, select an instance based on the fitting error, and identify the drone based on the selected instance of the parameter model.
Estimating model parameters using predefined channel center frequencies
The claims include estimating values of the model parameters using predefined channel center frequencies for each subset of samples.
Characterizing the frequency hopping sequence with multiple hopping parameters
The claims include characterizing the frequency hopping sequence using multiple frequency hopping parameters, including 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 neighboring hops, and a number of channels.
Determining combined fitting error from time and frequency fitting errors
The claims include determining a time fitting error and a frequency fitting error and determining a combined fitting error based on both errors to support selection.
All independent claims share the core concept of representing a frequency hopping sequence with a parameter model, fitting that model to sample subsets by evaluating fitting error across multiple model instances, selecting an instance based on the fitting error, and identifying the drone based on the selected instance. The claim set further includes refinements for predefined channel center frequencies, explicit frequency-hopping parameters, and combined time and frequency fitting errors.
Stated Advantages
Accurate frequency-hopping parameter estimation with as few as approximately 2 samples.
Strong gross-error rejection versus linear least squares.
Robustness to timing errors.
High target-detection probability in multi-target scenarios with only linearly increasing complexity.
Documented Applications
Counter-unmanned aircraft system monitoring and mitigation actions for detecting one or more drones using RF frequency-hopping signals.
Jamming RF communications by configuring a jammer using estimated frequency-hopping parameters.
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