Patent Publication Number: US-11663922-B2

Title: Systems and methods for detecting, monitoring, and mitigating the presence of unauthorized drones

Description:
BACKGROUND 
     Technological Field 
     The systems and methods disclosed herein are directed to detecting, monitoring, and mitigating unauthorized drones. More particularly, the systems and methods can be used to detect the presence of a drone using a plurality of nodes. 
     Description of the Related Technology 
     In recent years, Unmanned Aircraft Systems (UAS), more commonly known as drones, have been used extensively in a large number of exciting and creative applications, ranging from aerial photography, agriculture, product delivery, infrastructure inspection, aerial light shows, and hobbyist drone racing. Despite the usefulness of drones in many applications they also pose increasing security, safety, and privacy concerns. Drones are being used to smuggle weapons and drugs across borders. The use of drones near airports presents safety concerns, which may require airports to shut down until the surrounding airspace is secured. Drones are also used as a tool of corporate and state espionage activities. Thus, there is demand for an effective Counter-Unmanned Aircraft System (CUAS) solution to detect and monitor drones and mitigate the threat of drones when necessary. 
     SUMMARY 
     The systems, methods and devices of this disclosure each have several innovative aspects, no single one of which is solely responsible for the desirable attributes disclosed herein. 
     In one aspect, there is provided a system for detecting presence of a drone, the system comprising: a radio-frequency (RF) receiver configured to receive an RF signal; a processor; and a computer-readable memory in communication with the processor and having stored thereon computer-executable instructions to 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 RF signal; obtain predetermined data of expected communication protocols used between the drone and a controller; determine whether the RF signal corresponds to one of the expected communication protocols by comparing the samples of the RF signal to the predetermined data; if the RF signal corresponds to one of the expected communication protocols, decode the RF signal; and extract a unique identifier of the drone based at least partially on the decoded RF signal. 
     In another aspect, there is provided a method for detecting presence of a drone, the method comprising: receiving a set of samples from an RF receiver for a time interval, the set of samples comprising samples of an RF signal; obtaining predetermined data of expected communication protocols used between the drone and a controller; determining whether the RF signal corresponds to one of the expected communication protocols by comparing the samples of the RF signal to the predetermined data; if the RF signal corresponds to one of the expected communication protocols, decoding the RF signal; and extracting a unique identifier of the drone based at least partially on the decoded RF signal. 
     In another aspect, there is provided a non-transitory computer readable storage medium having stored thereon instructions that, when executed, cause a computing device to: receive a set of samples from an RF receiver for a time interval, the set of samples comprising samples of an RF signal; obtain predetermined data of expected communication protocols used between the drone and a controller; determine whether the RF signal corresponds to one of the expected communication protocols by comparing the samples of the RF signal to the predetermined data; if the RF signal corresponds to one of the expected communication protocols, decode the RF signal; and extract a unique identifier of the drone based at least partially on the decoded RF signal. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates an example environment including a drone detection system in accordance with aspects of this disclosure. 
         FIG.  2 A  illustrates an example drone detection system from  FIG.  1    which can be used to detect, monitor, and/or mitigate drones in accordance with aspects of this disclosure. 
         FIG.  2 B  illustrates an example drone from  FIG.  1    which can be detected with the drone detection system of  FIG.  2 A  in accordance with aspects of this disclosure. 
         FIG.  2 C  illustrates an example controller from  FIG.  1    which can be used to control the drone in accordance with aspects of this disclosure. 
         FIG.  3    illustrates another example drone detection system which can be used to detect the presence of one or more drones in accordance with aspects of this disclosure. 
         FIG.  4    illustrates an embodiment of the drone detection system from  FIG.  3    in a centralized configuration. 
         FIG.  5    illustrates an embodiment of the drone detection system from  FIG.  3    in a decentralized configuration. 
         FIG.  6    illustrates a method for detecting, monitoring, and mitigating unauthorized drones. 
         FIG.  7    illustrates a method for decoding the RF signal of the drone in accordance with aspects of this disclosure. 
         FIG.  8    illustrates a method for monitoring and identifying the detected drone in accordance with aspects of this disclosure. 
         FIG.  9    illustrates a method for determining flight and drone data in accordance with aspects of this disclosure. 
         FIG.  10    illustrates a method performed by the drone detection system to mitigate the drone. 
     
    
    
     DETAILED DESCRIPTION 
     The fast growth of drone applications in industrial, commercial and consumer domains in recent years has caused great security, safety and privacy concerns. For this reason, demand has been growing for systems and technique for drone detection, monitoring, and mitigation. 
     A CUAS system (or simply “drone detection systems”) can operate using multiple stages. In a first stage, the drone detection system detects the presence of a drone and determine whether the drone is a friend or a foe. The drone detection system can accomplish this by eavesdropping or monitoring the signals exchanged between the drone and the controller. 
     The drone detection system can include predetermined data or knowledge of expected communication protocols used between drones and their controllers. Different brands of drones and different drone models within a brand may use different communication protocols. In certain cases, different versions of the same drone model use different communication protocols. Reverse engineering can be employed to determine the data and knowledge relating to expected communication protocols. 
     The drone detection system can store the predetermined data or knowledge of the communication protocols used by different combinations of drones and controllers to communicate with each other. The drone detection system can gather the predetermined data or knowledge prior to deployment. For example, the predetermined data can include frequencies known to be used by a particular drone model. In certain embodiments, the drone detection system receives update data to the predetermined data or knowledge after deployment of the drone detection system. In certain embodiments, the drone detection system itself updates the predetermined data or knowledge based on its ongoing operations of detecting, monitoring, and/or mitigating drones. 
     Certain aspects of this disclosure may relate to how the drone detection system can leverage the predetermined data to determine additional data uniquely associated with the detected drone and its controller. For example, the drone detection system can scan the airwaves at frequencies known to be used by a particular drone model. If a known protocol is identified, then the drone detection system can proceed to decode the signal as if it was the intended controller. 
     Certain aspects of this disclosure may relate to how the drone detection system can identify flight and drone data once the communication protocol used by the drone has been decoded. Exemplary flight and drone data can include a unique identifier of the drone such as one or more of a serial number, a frequency hopping pattern, and/or a transmission timing and frequency. 
     In certain embodiments, exemplary flight and drone data can include wireless signal properties such as received energy level, delay spread and doppler spread, power delay profile, carrier frequency offset, and/or sampling time offset. 
     Certain aspects of this disclosure may relate to how the drone detection system can determine additional or key information for the detected drone. Exemplary key information includes home position, flight duration, traveling velocity, GPS coordinates, individual propeller rotation speed, and/or video feed. 
     Small drones, which are widely used in recreational and commercial applications, have caused alarming concerns of public safety and homeland security due to frequently reported unauthorized drone incidents in recent years. To effectively disable potential threats from drones and controllers, drone detection systems may be configured to mitigate drone operation. The drone detection system can either be mounted on a fixed location or onto a mobile unit. 
     In certain embodiments, the drone detection system comprises multiple nodes over a region. The individual nodes can cooperate with other nodes in their detection and mitigation. In a centralized configuration, the nodes send intermediate detection results to a centralized processor. The centralized processor can then determine detection results. In a decentralized configuration, the nodes share their detection results and can mitigate the drones either by themselves or cooperatively with the other nodes. 
     Therefore, it is desirable to provide a drone detection system to meet all these requirements. Aspects of this disclosure relate to various node configurations of the drone detection system. Other aspects of this disclosure relate to identifying flight and drone data such as unique identifiers and/or wireless signal properties once the communication protocol used by the drone has been decoded. Other aspects of this disclosure relate to how the drone detection system can determine additional or key information for the detected drone. 
     Advantageously, aspects of this disclosure can leverage the predetermined data to efficiently determine additional data uniquely associated with the detected drone and its controller. 
       FIG.  1    illustrates an example environment  100  including a drone detection system  101  in accordance with aspects of this disclosure. In certain embodiments, the environment  100  includes the drone detection system  101 , one or more drones  103 A- 103 N, and one or more drone controllers  105 A- 105 N (or simply “controllers”). An example of the one or more drones  103 A- 103 N is illustrated in  FIG.  2 B . An example of the one or more controllers  105 A- 105 N is illustrated in  FIG.  2 C . 
     In certain embodiments, each of the drones  103 A- 103 N′ is configured to communicate to a corresponding one of the controllers  105 A- 105 N via an RF signal  107 A- 107 N. Although not illustrated, in some embodiments, a single one of the controllers  105 A- 105 N may be configured to control more than one of the drones  103 A- 103 N. 
     The drone detection system  101  is configured to monitor  109 A- 109 N communications between the drones  103 A- 103 N and the controllers  105 A- 105 N in order to detect the presence of the drones  103 A- 103 N and/or the controllers  105 A- 105 N. For example, the drone detection system  101  may be configured to receive the RF signals  107 A- 107 N being sent between the drones  103 A- 103 N and the controllers  105 A- 105 N in order to monitor  109 A- 109 N the communication between the drones  103 A- 103 N and the controllers  105 A- 105 N. In certain embodiments, once the drone detection system  101  can decode the RF signals  107 A- 107 N, the drone detection system  101  may monitor the drones  103 A- 103 N and take certain actions in order to mitigate the potential threat of the drones  103 A- 103 N. For example, as is explained with respect to  FIG.  10   , the drone detection system  101  may transmit a jamming RF signal to disrupt communication between the detected drone  103 A- 103 N and the controller  105 A- 105 N, and/or spoof the controller  105 A- 105 N by sending a command to the drone  103 A- 103 N to land or otherwise leave the environment  100 . 
       FIG.  2 A  illustrates an example drone detection system  101  which can be used to detect the presence of the one or more drones  103 A- 103 N in accordance with aspects of this disclosure. In certain embodiments, the drone detection system  101  includes one or more nodes  161  as will be further explained with respect to  FIGS.  4  and  5   . In certain embodiments, each node  161  consists of circuitry for receiving RF signals and circuitry for transmitting RF signals. For example, each node  161  within the drone detections system  101  can include a processor  111 , a memory  113 , a front end  115 , a plurality of transmit antennae  117 A- 117 N, and a plurality of receive antennae  119 A- 119 N. 
     In certain embodiments, the front end  115  can be configured as an Analog-to-Digital-Converter (ADC) for converting received signals. In certain embodiments, the front end  115  can be configured as a digital processing unit. In certain embodiments, the digital processing unit is in the form of, for example, field programmable gate array (FPGA) or software defined radio (SDR). In operation, the node  161  tunes to a specific frequency and samples at a rate that covers the signal of interest. Digitized samples are processed by the processor  111 . 
     In certain embodiments, the drone detection system  101  includes a receiver  114 , a transmitter  116 , a sensor/detector  118 , a jammer  120 , a Digital-to-Analog Converter (DAC)  124 , a mixer  126 , and an amplifier  128 . 
     In certain embodiments, a portion of the front end  115  is configured as a transmit front end  122 . For example, in certain embodiments, the transmit front end  122  comprises the transmitter  116 , the DAC  124 , and the mixer  126 . In certain embodiments, the transmit front end  122  comprises the amplifier  128 . 
     Although illustrated in separate blocks in  FIG.  2 A , one or more of the blocks  111 ,  113 - 120 ,  122 ,  124 ,  126 , and  128  may be implemented together by the same component(s). For example, in one implementation the receiver  114 , the transmitter  116 , the sensor/detector  118 , and the jammer  120  can be implemented as part of the front end  115  illustrated in  FIG.  2 A . 
     Depending on the implementation, the drone detection system  101  can include a greater or fewer number of components than shown in  FIG.  2 A . For example, each node  161  within the drone detection system  101  need not comprise the same components and instead can comprise different combinations of components. For example, in certain embodiments where the drone detection system  101  is in a centralized configuration, one or more of the nodes  161  within the drone detection system  101  need not include the processor  111 . 
     In certain embodiments, the processor  111  is shared by more than one node  161  within the drone detection system  101 . In certain embodiments in the centralized configuration, each node within the drone detection system  101  includes the processor  111 . However, each of the processors  111  within the nodes  161  of the drone detection system  101  need not have the same functionality. For example, in certain embodiments, the processor  111  of one of the nodes  161  has full functionality while the processors  111  within the remaining nodes have less than full functionality. In this way, certain tasks performed by the drone detection system  101  can be assigned to the node that includes the processor  111  having the required functionality without requiring all the nodes  161  to have that same functionality and associated cost. 
     In other embodiments, one or more of the antennae  117 A- 119 N can be used for both transmitting and receiving signals. 
     In certain embodiments, the one or more nodes  161  of the drone detection system  101  are configured to receive an RF signal (e.g., the RF signals  107 A- 107 N of  FIG.  1   ) via one of the receive antennae  119 A- 119 N. The one of the receive antennae  119 A- 119 N provides the received RF signal to the front end  115 . In certain embodiments, the front end  115  can process the received RF signal into a format that can be read by the processor  111 . For example, in certain embodiments, the front end  115  may perform one or more of the following actions: filtering, amplifying, analog-to-digital conversion, etc. on the received RF signal. 
     In certain embodiments, the memory  113  can store computer readable instructions for causing the processor  111  to detect the presence of a drone (e.g., the drones  103 A- 103 N of  FIG.  1   ) based on the RF signals received via the receive antennae  119 A- 119 N. In addition, in certain embodiments, the drone detection system  101  can also be configured to provide a signal (e.g., a jamming signal or an RF communication signal) to the front end  115  to be transmitted to the detected drone(s). The front end  115  can then process the signal received from the processor  111  before providing the processed signal to one or more of the transmit antennae  117 A- 117 N. 
     There are several different techniques that the drone detection system  101  can use to detect the presence of the drones  103 A- 103 N. For example, the drone detection system  101  can scan the airwaves at frequencies known to be used by particular model(s) of the drones  103 A- 103 N. If a known protocol is identified, the drone detection system  101  can then decode the signal as if it was the intended receiver/controller  105 A- 105 N. Depending on the embodiment, these decoding steps can include: synchronization, channel estimation, deinterleaving, descrambling, demodulation, and error control decoding. 
     In certain embodiments, the drone detection system  101  can be configured to perform some of the aforementioned steps blindly due to lack of knowledge (such as device id) on information known by the controller  105 A- 105 N. Aspects of the disclosure uniquely identify a drone  103 A- 103 N from flight and drone data. Other aspects of this disclosure relate to how the drone detection system  101  can determine additional or key information for the detected drone  103 A- 103 N. Once detected, the drone detection system  101  can provide alert(s) regarding the presence of the one or more drones  103 A- 103 N. 
     The drone detection system  101  can monitor the presence of the one or more drones  103 A- 103 N. As part of monitoring, a position of the one or more drones  103 A- 103 N relative to the environment  100  can be monitored in real-time to determine if the position of the one or more drones  103 A- 103 N strays inside or outside acceptable airspace. 
     There are also several mitigation actions which can be taken by the drone detection system  101 . For example, after detecting the one or more drones  103 A- 103 N, the drone detection system  101  may take one or more of the actions described with reference to  FIG.  10   . For example, in certain embodiments, these actions can include do nothing/keep monitoring, drone-specific jamming, wideband jamming, and control takeover. 
       FIG.  2 B  illustrates an example drone  103  which can be detected with the drone detection system  101  in accordance with aspects of this disclosure. In certain embodiments, the drone  103  includes one or more propellers  121 , one or more motor controllers  123 , a battery or other power source  125 , a memory  127 , a processor  129 , a front end  131 , an antenna  133 , and a camera  135 . As described above, the antenna  133  may be configured to receive RF signals  107  from the controller  105  (see  FIG.  2 C ) and provide RF signals  107  back to the controller  105  (e.g., images obtained from the camera  135 ). In certain embodiments, the RF signals  107  sent/received from the antenna  133  are provided to/from the processor  129  and processed by the front end  131 . In certain embodiments, the propeller(s)  121  provide lift and control movement of the drone  103  as it maneuvers through airspace. The propeller(s)  121  may also include one or more motor(s) (not illustrate) configured to individually power each of the propeller(s)  121 . 
     In certain embodiments, the motor controller(s)  123  are configured to receive instructions from the processor  129  (e.g., based on instructions stored in the memory  127  and the RF signal  107  received from the controller  105 ) to move the drone  103  to a specific point in the airspace and translate the received instructions into motor position commands which are provided to the propeller(s)  121 . In certain embodiments, the battery  125  provides power to each of the components of the drone  103  and has sufficient power storage to enable the propellers  121  to maneuver the drone  103  for a predetermined length of time. The camera  135  can capture images in real-time and provide the captured images to the controller  105  via the antenna  133  which can aid a user in controlling movement of the drone  103 . 
       FIG.  2 C  illustrates an example controller  105  which can be used to control the drone  103  in accordance with aspects of this disclosure. In certain embodiments, the controller  105  comprises a memory  141 , a processor  143 , a front end  145 , an antenna  147 , an input device  149 , and a display  151 . As described above, the antenna  147  may be configured to receive RF signals  107  (e.g., images obtained from the camera  135 ) from the drone  103  (see  FIG.  2 B ) and provide RF signals  107  back to the drone  103  to control movement of the drone  103 . In certain embodiments, the RF signals  107  sent/received from the antenna  147  are provided to/from the processor  143  and processed by the front end  145 . In certain embodiments, the input device  149  is configured to receive input from a user which can be used by the processor  143  to generate commands for controlling movement of the drone  103 . In certain embodiments, the display  151  is configured to display images received from the drone  103  to the user to provide feedback on the current position of the drone  103  and its environment  100 . In some embodiments, the display can be implemented as a pair of goggles worn by the user to provide a first person view of images obtained by the camera  135 . 
       FIG.  3    illustrates another example drone detection system  101  which can be used to detect the presence of the one or more drones  103 A-C in accordance with aspects of this disclosure. In particular, the drone detection system  101  illustrated in  FIG.  3    is a simplified system model that includes two controllers  105 A-B controlling three drones  103 A-C. Of course, the disclosure is not limited to the illustrated system model and can include any number of drones  103  and controllers  105 . Further the number of drones  103  associated with a given controller  105  is not limited to the illustrated system model. 
     In certain embodiments, the drone detection system  101  comprises multiple nodes  161  over a region. The individual nodes  161  can cooperate with other nodes  161  in their detection and mitigation. In a centralized configuration as is illustrated in  FIG.  4   , the nodes  161  send intermediate detection results to a centralized processor  111 . The centralized processor  111  can then determine detection results. In a decentralized configuration as is illustrated in  FIG.  5   , the nodes  161  share their detection results and can mitigate the drones  103  either by themselves or cooperatively with the other nodes  161 . 
     In certain embodiments, the drone detection system  101  can receive signals from all the drones  103  and/or controllers  105  within a detection range of the drone detection system  101 . In certain embodiments, the drone detection system  101  compares stored data or knowledge of the communication protocols used by different combinations of drones  103  and controllers  105  to communicate with each other with the received signals. The drone detection system  101  can gather the predetermined data or knowledge prior to deployment. When analyzing the eavesdrop link, the drone detection system  101  can rely on the predetermined data related to the communication protocol used between the drone  103  and its controller  105 . Different brands of drones and different drone models within a brand may use different communication protocols. Different versions of the same drone model can use different communication protocols which presents additional complexity to the analysis. Reverse engineering can be employed to determine the data and knowledge relating to expected communication protocols. For example, the predetermined data can include frequencies known to be used by a particular drone model. 
     In certain embodiments, the drone detection system  101  receives update data to the predetermined data or knowledge after deployment of the drone detection system  101 . In certain embodiments, the drone detection system  101  itself updates the predetermined data or knowledge based on its ongoing operations of detecting, monitoring, and/or mitigating drones  101 . 
     The drone detection system  101  can leverage the predetermined data to determine additional data uniquely associated with the detected drone  103  and its controller  105 . For example, the drone detection system  101  can scan the airwaves at frequencies known to be used by a particular drone model. If a known protocol is identified, then the drone detection system  101  can proceed to decode the signal as if it was the intended controller  105 . 
     The drone detection system  101  can identify flight and drone data once the communication protocol used by the drone  103  has been decoded. Exemplary flight and drone data can include a unique identifier of the drone  103  such as one or more of a serial number, a frequency hopping pattern, and/or a transmission timing and frequency. 
       FIG.  4    illustrates an embodiment of the drone detection system  101  from  FIG.  3    in a centralized configuration. A centralized configuration involves individual nodes  161  sending intermediate detection results to a processor  111  that is shared or centralized. 
     Depending on the implementation, the drone detection system  101  can include a greater or fewer number of components than shown in  FIG.  2 A . For example, each node  161  within the drone detection system  101  need not comprise the same components and instead can comprise different combinations of components. For example, in the embodiment illustrated in  FIG.  4    where the drone detection system  101  is in a centralized configuration, one or more of the nodes  161  within the drone detection system  101  need not include the processor  111 . In certain embodiments, the processor  111  is shared by more than one node  161  within the drone detection system  101 . In certain embodiments in the centralized configuration, each node within the drone detection system  101  includes the processor  111 . However, each of the processors  111  within the nodes  161  of the drone detection system  101  need not have the same functionality. For example, in certain embodiments, the processor  111  of one of the nodes  161  has full functionality while the processors  111  within the remaining nodes have less than full functionality. In this way, certain tasks performed by the drone detection system  101  can be assigned to the node that includes the processor  111  having the required functionality without requiring all the nodes  161  to have that same functionality and associated cost. The shared processor  111  may also perform receiving tasks as other nodes  161 . The link between nodes  161  and the centralized processor  111  can either be wired or wireless. 
       FIG.  5    illustrates an embodiment of the drone detection system  101  from  FIG.  3    in a decentralized configuration. In a decentralized configuration, the nodes  161  share their detection results and can mitigate the drones  103  either by themselves or cooperatively with other nodes  161 . 
     In certain embodiments, the drone detection system  101  is mounted on a fixed location. In other embodiments, the drone detection system  101  is mounted onto a mobile unit. Fixed location setup may be advantageous for surveillance and monitoring at a fixed location such as border wall, airports, or other secured sites. Mobile mounting may be advantageous for surveillance over a larger area. In such an embodiment, the drone detection system  101  can be attached to a vehicle, vessel, or aircraft. In this way, multiple nodes  161  can be deployed over a region with individual nodes  161  cooperating in their detection and mitigation strategies. 
     Detection of Drone(s) Using Predetermined Data 
     In recent years, the use of drones  103  has gained popularity due to their affordability and versatility. Drones  103  have been widely used in many applications from recreational flying such as drone racing to commercial uses such as package delivery and real estate photography. According to FAA Forecast, the use of non-model (commercial) drones  103  will grow three-fold from 2018 to 2023 whereas the use of model (recreational) drones  103  will increase from 1.25 to 1.39 million units in 5 years. However, unauthorized drone activities and incidents have been reported more and more frequently near airports, stadiums, and borders, which has caused growing concerns about public safety and homeland security. Therefore, an effective drone detection system  101  becomes an indispensable mechanism for law enforcement and military to detect, identify, and disable any potential and imminent threats caused by the improper and unauthorized uses of drones  103 . 
     In certain embodiments, the drone detection system  101  is capable of detecting any wireless signal transmitted from the drones  103  and the controllers  105  when they are in the detection range and determining when in time and where in frequency the received signals are detected. Nevertheless, the time and frequency information are subject to errors, which can be caused by sensing and measurement errors, channel impairments such as fading and interference, and/or hardware limitations such as carrier frequency offsets and timing jitters. One of the main challenges in detecting the drones  103  is to decode noisy time-frequency samples. Decoding is further complicated by multi-target scenarios where multiple drones  103  are present within the detection range. Techniques for detecting a radio-frequency (RF) signal transmitted between a drone and a drone controller are disclosed in U.S. patent application Ser. No. 16/871,713, filed May 11, 2020, and Ser. No. 16/886,482, filed May 28, 2020, both of which are hereby incorporated by reference in their entireties. 
     Overview of Techniques for Detecting, Monitoring, and Mitigating Unauthorized Drones 
       FIG.  6    illustrates a method  600  for detecting, monitoring, and mitigating unauthorized drones  103 A- 103 N in accordance with aspects of this disclosure. Specifically, the method  600  involve detecting a communication protocol used for the one or more drones  103  within a detection range. In particular, the drone detection system  101  can detect the protocol used for communication between the one or more drones  103 A- 103 N and the controllers  105 A- 105 N in order to detect the presence of the one or more drones  103 A- 103 N. 
     The method  600  begins at block  602 . At block  604 , the method  600  involves detecting the communication protocol. The protocol can be in the form of a set of samples received by the node  161  for a time interval. The drone detection system  101  can compare stored data or knowledge of the communication protocols used by different combinations of drones  103  and controllers  105  to communicate with each other with the received signals. When analyzing the eavesdrop link, the drone detection system  101  can rely on the predetermined data related to the communication protocol used between the drone  103  and its controller  105 . The drone detection system  101  can gather the predetermined data or knowledge prior to deployment. In certain embodiments, the drone detection system  101  receives update data to the predetermined data or knowledge after deployment of the drone detection system  101 . In certain embodiments, the drone detection system  101  itself updates the predetermined data or knowledge based on its ongoing operations of detecting, monitoring, and/or mitigating drones  101 . 
     At block  606 , the method  600  involves decoding the RF signal. Exemplary decoding steps are further disclosed with respect to  FIG.  7   . Once decoded, the drone detecting system  101  can identify flight and drone data. Exemplary methods for determining flight and drone data is disclosed with respect to  FIGS.  8  and  9   . For example, the method  600  continues to block  608  where data is identified as being associated with the drone  103 . Exemplary flight and drone data can include a unique identifier of the drone  103  such as one or more of a serial number, a frequency hopping pattern, and/or a transmission timing and frequency. In certain embodiments, exemplary flight and drone data can include wireless signal properties such as received energy level, delay spread and doppler spread, power delay profile, carrier frequency offset, and/or sampling time offset. 
     The method  600  continues to block  610  where the drone detecting system  101  can determine data associated with the drone  103  or key data beyond the unique identifiers identified in block  608 . Aside from the unique identifiers identified in block  608 , the drone detection system  101  can determine in block  610 , for example, home position, flight duration, traveling velocity, GPS coordinates, and video feed. 
     The method  600  moves to block  612  where the node  161  stores the aggregate of the data gathered in blocks  608  and  610 . The drone detection system  101  can determine abstract information related to the drone  103  by analyzing the aggregated stored data. For example, the abstract information can include weight of the drone  103 , whether the drone  103  is under active human control or traveling a pre-programmed route, and intention estimation based on route analysis. 
     The method  600  continues to decision block  614  to determine whether to perform mitigation on the drone  103 . When determining whether to perform mitigation, the drone detection system  101  can monitor and uniquely identify the detected drone  103  in accordance with  FIG.  8   . Mitigation can be performed as disclosed with respect to  FIG.  10   . If mitigation is not performed for a specific drone  103 , the method  600  returns to block  606  where the drone detections system  101  continues to decode RF signals transmitted to or from the specific drone  103 . The method  600  can also return to block  604  to detect new target drones  103 . 
     Decoding the RF Signal of the Drone 
       FIG.  7    illustrates a method  700  for decoding the RF signal of a drone  103  in accordance with aspects of this disclosure. For example, the method  700  can be performed as a part of block  606  of method  600 . The method  600  involves decoding the RF signal  107  transmitted between the drone  103  and the controller  105 . 
     The method  700  begins at block  704  where synchronization is performed on the received RF signal. The method  700  continues to block  706  where channel estimation is performed on the received RF signal. Signal parameters of the RF signal  107  can be estimated in accordance with aspects of this disclosure. The decoding of the RF signal  107  may be performed by configuring the receiver  114  to follow the reconstructed signal parameters and synchronize with the RF signal  107 . 
     The method  700  then moves to block  708  where demodulation is performed. The method  700  then moves to block  710  where descrambling is performed. The method  700  continues with block  714  where forward error correction (FEC) decoding is performed. The method  700  ends at block  716 . 
     Monitoring the Drone(s) 
     Once a drone  103  has been detected, for example, at block  604  of  FIG.  6   , the drone detection system  101  can monitor the drone  103  for a period of time before potentially taking mitigation actions against the drone  103 . 
       FIG.  8    illustrates a method  800  for monitoring and uniquely identifying the detected drone  103  in accordance with aspects of this disclosure. The method  800  begins at block  802  and moves to block  804  where the drone detection system  101  logs activity of target drones  103 . The method  800  moves to block  806  where the drone detection system  101  extracts one or more unique identifiers from the decoded RF signal and/or the wireless properties of the RF signal. 
     The drone detection system  101  can include predetermined data or knowledge of expected communication protocols used between drones  103  and their controllers  105 . The drone detection system  101  can store the predetermined data or knowledge of the communication protocols used by different combinations of drones  103  and controllers  105  to communicate with each other. The drone detection system  101  can gather the predetermined data or knowledge prior to deployment. In certain embodiments, the drone detection system  101  receives update data to the predetermined data or knowledge after deployment of the drone detection system  101 . In certain embodiments, the drone detection system itself updates the predetermined data or knowledge based on its ongoing operations of detecting, monitoring, and/or mitigating drones. 
     In this way, the drone detection system  101  can leverage the predetermined data to determine additional data uniquely associated with the detected drone and its controller. For example, the drone detection system  101  can identify or extract flight and drone data once the communication protocol used by the drone has been decoded. Exemplary flight and drone data can include a unique identifier of the drone such as one or more of a serial number, a frequency hopping pattern, and/or a transmission timing and frequency. In certain embodiments, exemplary flight and drone data can include wireless signal properties such as received energy level, delay spread and doppler spread, power delay profile, carrier frequency offset, and/or sampling time offset. 
     The method  800  moves to block  808  where the drone detection system  101  can classify the target drone  103  as, for example, friend or foe based at least partly on the extracted unique identifier in block  806 . The method  800  continues to block  810  where the user is alerted to the determination in block  808 . The method  800  ends at block  812 . 
     Determining Additional Key Information 
     Certain aspects of this disclosure may relate to how the drone detection system  101  can determine additional or key information for the detected drone  103 . Exemplary key information includes, home position, flight duration, traveling velocity, GPS coordinates, and/or video feed. 
       FIG.  9    illustrates a method  900  for determining additional or key information in accordance with aspects of this disclosure. The method  900  begins at block  902  and then moves to block  904  where logged data related to the target drone  103  is analyzed by the drone detection system  101 . The logged data can include stored data associated with the drone  103  and determined from the decoded RF signal  107 . For example, the data can include any activities performed by the drone  103  (e.g., flight data) and drone behaviors that may indicate whether the drone  103  is a friend or foe. 
     The method  900  moves to block  906  where flight data for the drone  103  is identified. The method moves to block  908  where the drone detection system  101  determines key flight data from the flight data identified in block  906  for the drone  103 . Exemplary flight data includes home position, flight duration, traveling velocity, GPS coordinates, and/or video feed. 
     The method  900  continues at block  910  where other useful information is deduced from the key flight data determined in block  908 . Exemplary useful information includes weight of the drone  103 , whether the drone  103  is under active human control or traveling a pre-programmed route, and intention estimation based on route analysis. The method ends at block  912 . 
     Mitigating the Drone(s) 
     After determining that mitigation of the drone  103  is appropriate and in certain embodiments, the drone detection system  101  performs one or more of a number of different mitigation actions in accordance with aspects of this disclosure. 
       FIG.  10    is an example method  1000  which can be performed by the drone detection system  101  to mitigate one or more of the drones  103 . The method  1000  begins at block  1002 . In certain implementations, the method  1000  may be performed in response to determining that mitigation actions are warranted in block  614  of  FIG.  6   . At block  1004 , the method  1000  involves selecting a mitigation technique to perform. The method  1000  then involves continuing to one of blocks  1006 - 1012  based on the mitigation technique selected in block  1004 . 
     At block  1006 , the method  1000  involves continuing to monitor the drone  103 , which may involve returning to block  606  of method  600 . For example, if the drone  103  is determined to be friendly and/or if the drone detection system  101  does not have the legal authority to take more aggressive actions, the drone detection system  101  may only be authorized to continue monitoring the drone  103  while alerting a user to the presence of the drone  103 . 
     At block  1008 , the method  1000  involves performing drone specific jamming. For example, in the case that the drone detection system  101  has estimated the frequency hopping parameters used by the drone  103 , the drone detection system  101  can configured the jammer  120 . The jammer  120  can then generate a jamming signal and transmit the jamming signal to all drones  103  within the detection range via the transmit antenna  117  to disrupt the RF communications between the drones  103  and the controllers  105 . 
     At block  1010 , the method  1000  involves the drone detection system  101  performing wideband jamming. In certain embodiments, wideband jamming may be appropriate where the drone detection system  101  does not have sufficient knowledge of the communication protocol used by the RF signal  107  to perform drone specific jamming and where the wideband jamming will not affect other friendly drones  103  within the environment  100 . 
     At block  1012 , the method involves the drone detection system  101  taking over control of the drone  103 . For example, and in certain embodiments, using the estimated frequency hopping parameters estimated in accordance with aspects of this disclosure to reconstruct the RF signal  107 , the drone detection system  101  can send commands to the drone  103  in order to have the drone  103  perform certain maneuvers, such as landing the drone  103  in a safe area. The method  1000  ends at block  1014 . 
     Implementing Systems and Terminology 
     Implementations disclosed herein provide systems, methods and apparatus for detecting the presence of drones. It should be noted that the terms “couple,” “coupling,” “coupled” or other variations of the word couple as used herein may indicate either an indirect connection or a direct connection. For example, if a first component is “coupled” to a second component, the first component may be either indirectly connected to the second component via another component or directly connected to the second component. 
     The drone detection functions described herein may be stored as one or more instructions on a processor-readable or computer-readable medium. The term “computer-readable medium” refers to any available medium that can be accessed by a computer or processor. By way of example, and not limitation, such a medium may comprise RAM, ROM, EEPROM, flash memory, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. It should be noted that a computer-readable medium may be tangible and non-transitory. As used herein, the term “code” may refer to software, instructions, code or data that is/are executable by a computing device or processor. 
     The methods disclosed herein comprise one or more steps or actions for achieving the described method. The method steps and/or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is required for proper operation of the method that is being described, the order and/or use of specific steps and/or actions may be modified without departing from the scope of the claims. 
     As used herein, the term “plurality” denotes two or more. For example, a plurality of components indicates two or more components. The term “determining” encompasses a wide variety of actions and, therefore, “determining” can include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like. Also, “determining” can include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like. Also, “determining” can include resolving, selecting, choosing, establishing and the like. 
     The phrase “based on” does not mean “based only on,” unless expressly specified otherwise. In other words, the phrase “based on” describes both “based only on” and “based at least on.” 
     The previous description of the disclosed implementations is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these implementations will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other implementations without departing from the scope of the invention. For example, it will be appreciated that one of ordinary skill in the art will be able to employ a number corresponding alternative and equivalent structural details, such as equivalent ways of fastening, mounting, coupling, or engaging tool components, equivalent mechanisms for producing particular actuation motions, and equivalent mechanisms for delivering electrical energy. Thus, the present invention is not intended to be limited to the implementations shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.