A system for detecting presence of one or more drones (103A-103N), the system comprising: a radio-frequency receiver configured to receive a first RF signal (107A-107N) transmitted between a first drone (103A-103N) and a first drone controller (105A-105N), the first RF signal (107A-107N) including a first frequency hopping sequence defined by a plurality of first frequency hopping parameters; a processor; and a computer-readable memory in communication with the processor and having stored thereon computer-executable instructions which, when executed by the processor, cause the 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 (107A-107N), obtain a parameter model of 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 the plurality of first frequency hopping parameters based on the selected first plurality of the samples, constructing a first instance of the parameter model based on the estimated plurality of first frequency hopping 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 wherein the fitting errors are compared to a fitting error threshold 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 (103A-103N) based on the selected first instance of the parameter model. a radio-frequency receiver configured to receive a first RF signal (107A-107N) transmitted between a first drone (103A-103N) and a first drone controller (105A-105N), the first RF signal (107A-107N) including a first frequency hopping sequence defined by a plurality of first frequency hopping parameters; a processor; and a computer-readable memory in communication with the processor and having stored thereon computer-executable instructions which, when executed by the processor, cause the 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 (107A-107N), obtain a parameter model of 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 the plurality of first frequency hopping parameters based on the selected first plurality of the samples, constructing a first instance of the parameter model based on the estimated plurality of first frequency hopping 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 wherein the fitting errors are compared to a fitting error threshold 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 (103A-103N) based on the selected first instance of the parameter model. receive a set of samples from the RF receiver for a time interval, the set of samples comprising samples of the first RF signal (107A-107N), obtain a parameter model of 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 the plurality of first frequency hopping parameters based on the selected first plurality of the samples, constructing a first instance of the parameter model based on the estimated plurality of first frequency hopping 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 wherein the fitting errors are compared to a fitting error threshold randomly selecting a first plurality of the samples from the set of samples, estimating the plurality of first frequency hopping parameters based on the selected first plurality of the samples, constructing a first instance of the parameter model based on the estimated plurality of first frequency hopping 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 wherein the fitting errors are compared to a fitting error threshold 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 (103A-103N) based on the selected first instance of the parameter model.