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-11552674-B2
Publication Date
2023-01-10
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 counter-unmanned aircraft system (CUAS) drone detection system that detects the presence of one or more drones by receiving an RF signal transmitted between a drone and a drone controller. The received RF signal includes a frequency hopping sequence defined by a plurality of frequency hopping parameters, and the system obtains a parameter model that represents the frequency hopping sequence and models the frequency hopping parameters with a plurality of model parameters.
The system fits the parameter model to a set of samples derived from the received signal by selecting a plurality of subsets of samples, estimating values of the plurality of model parameters for each subset, and constructing a plurality of instances of the parameter model. The system evaluates a fitting error between each subset of samples and its associated instance, selects an instance based on the fitting error associated with each subset, and identifies the drone based on the selected instance of the parameter model.
The described approach models time-frequency samples using frequency hopping parameters and estimates them using a RANSAC-style HopSAC estimator framework with repeated random subset selection, inlier/outlier classification based on fitting errors, and best-consensus selection of the parameter model instance. The document further describes parameter estimation informed by channel-center frequencies, fitting error evaluation separated into time fitting error and frequency fitting error and combined into a combined fitting error, and monitoring by reconstructing the hopping sequence for decoding.
Claims Coverage
The document provides three independent claims (system, method, and non-transitory computer-readable storage medium) that share a common core workflow: receive an RF signal carrying a frequency hopping sequence, obtain a parameter model, fit the model to sample subsets using fitting error across multiple model instances, select an instance based on fitting error, and identify the transmitting drone. Across dependent claims, the main inventive features refine this workflow by extending to multiple drones/signals, constraining parameter estimation with predefined channel center frequencies, specifying detailed frequency-hopping parameter components, and separating fitting error into time and frequency terms.
Drone identification by fitting a frequency-hopping parameter model to sample subsets
A radio-frequency (RF) receiver receives an RF signal transmitted between a drone and a drone controller, where the RF signal includes a frequency hopping sequence defined by a plurality of frequency hopping parameters; a processor obtains a parameter model representing the frequency hopping sequence; the processor fits the parameter model to a set of samples by selecting subsets, estimating model parameters for each subset, constructing instances of the parameter model associated with the subsets, evaluating a fitting error between each subset and its instance, selecting an instance based on the fitting error, and identifying the drone based on the selected instance.
Multi-drone detection via selecting the best-fitting parameter model instance
A system that receives a first and a second frequency-hopping RF signal, fits the parameter model to subsets of samples by estimating model parameters, constructing instances, evaluating fitting errors, selecting an instance based on the fitting error, and identifying a second drone based on the selected instance of the parameter model.
Predefined channel center frequency constrained parameter estimation
The method and corresponding system/storage-medium implementation estimates values of the model parameters for each sample subset using predefined channel center frequencies.
Frequency-hopping parameter structure including start time, hop period, start frequency, frequency difference, and number of channels
The frequency hopping parameters include a start time, a hop period between two neighboring hops, a start frequency after an initial time, a frequency difference between the two neighboring hops, and a number of channels.
Combined fitting error from time fitting error and frequency fitting error
The system and corresponding method/storage-medium implementation determine a time fitting error and a frequency fitting error and determine a combined fitting error based on both errors.
Across the independent claims, the key inventive concept is identifying the transmitting drone by fitting a frequency-hopping parameter model to sample subsets using fitting-error evaluation across multiple model instances, then selecting the instance with fitting error. Dependent claims refine this by extending detection to additional drones/signals, constraining estimation using predefined channel center frequencies, specifying the frequency-hopping parameter components, and separating time and frequency fitting errors.
Stated Advantages
Superior detection probability versus linear least-squares under gross errors, timing errors, and multiple-target scenarios.
Robustness that enables lower SNR operation.
Reduced collateral impact.
Documented Applications
CUAS drone detection and monitoring by eavesdropping RF frequency-hopping communications between drones and controllers, estimating frequency-hopping parameters, and identifying the transmitting drone.
Jamming mitigation actions including drone-specific jamming and wideband jamming, based on estimated frequency-hopping parameters.
Monitoring for decoding by reconstructing the hopping sequence.
Possible control takeover is mentioned.
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