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-11190233-B2
Publication Date
2021-11-30
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
A CUAS drone detection system detects the presence of one or more drones by receiving a radio-frequency (RF) signal transmitted between a drone and a drone controller. The RF signal includes a frequency hopping sequence defined by a plurality of frequency hopping parameters, and the system receives a set of samples for a time interval and represents the frequency hopping sequence using a parameter model having model parameters respectively modeling the frequency hopping parameters.
The system fits the parameter model to the received samples by iterating over randomly selecting a plurality of the samples, estimating values of the model parameters based on the selected samples, and constructing a parameter model instance with the estimated values. The system classifies the selected samples as inliers or outliers by evaluating fitting errors between the samples and the constructed instance of the parameter model, and selects one of the constructed parameter model instances based on the inliers and outliers for the constructed instances.
Finally, the system identifies the drone based on the selected parameter model instance. The document also describes constraining parameter estimation using predefined channel center frequencies, performing inlier/outlier classification using combined time and frequency fitting errors with a fitting error threshold, extending the approach to identifying additional drones based on additional RF signals, and an optional jammer that generates an RF jamming signal based on estimated model parameters.
Claims Coverage
The independent claims are clm-00001, clm-00009, and clm-00016. Across these independent claims, there are 3 main inventive features: iterative inlier/outlier model fitting for RF frequency-hopping sequence samples, selection of a best parameter model instance based on inlier/outlier results to identify a drone, and implementation as a system, method, and non-transitory computer-readable storage medium.
Iterative parameter-model fitting with inlier/outlier classification
Iterating by randomly selecting a plurality of the samples, estimating values of a plurality of model parameters, constructing a parameter model instance with the estimated values, and classifying the selected samples as inliers or outliers by evaluating fitting errors between the samples and the constructed instance.
Best parameter-model instance selection for drone identification
Selecting one of the constructed instances based on the inliers and outliers for each constructed instance, and identifying the drone based on the selected first instance of the parameter model.
Drone detection implemented as system, method, and non-transitory medium
Receiving a set of samples for a time interval and performing parameter-model fitting and inlier/outlier selection to identify a drone as recited in a system, a method, and a non-transitory computer-readable storage medium having stored instructions.
Overall, the independent claims cover a workflow that receives RF samples of a frequency-hopping drone/controller signal, fits a parameter model via iterative random subset selection with fitting-error-based inlier/outlier classification, selects the best-fitting model instance based on inliers/outliers, and identifies the drone; this workflow is claimed as a system, a method, and a non-transitory computer-readable storage medium.
Stated Advantages
Enables low-complexity real-time operation.
Achieves near-100% target detection and parameter accuracy under gross errors, timing errors, and multiple-target scenarios.
Outperforms linear Least Squares (LS) estimator under gross errors, timing errors, and multiple targets.
Documented Applications
CUAS drone detection using RF frequency-hopping communications to detect presence and identify one or more drones.
Drone-specific monitoring/mitigation by generating frequency-hopping-parameter-based drone-specific jamming or control takeover.
Multiple-target detection scenarios detecting presence of one or more drones.
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