Systems and methods for detecting, monitoring and mitigating drone presence

Abstract

Systems and methods for detecting, monitoring, and mitigating the presence of drones are provided herein. In one aspect, the system for detecting the presence of drones includes a radio frequency receiver configured to receive RF signals transmitted between a drone and a controller. The RF signals include a synchronization signal for synchronizing the RF signals. The system can further include a processor and a computer-readable memory in communication with the processor and having computer-executable instructions stored thereon to cause the at least one processor to receive a sequence of samples from the RF receiver, take second derivatives of the received sequence of samples, calculate a running sum of a predetermined number of second derivatives of the received sequence of samples, and detect the presence of a drone based on the running sum.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of U.S. Provisional Application No. 62 / 846,680, filed May 12, 2019, which is incorporated by reference in its entirety.

[0002] Systems and methods disclosed herein are directed to detecting, monitoring, and mitigating the presence of drones. More specifically, the systems and methods detect radio frequency (RF) signals transmitted between a drone and a drone controller. [Background technology]

[0003] In recent years, Unmanned Aircraft Systems (UAS), more commonly known as drones, have been widely used in a number of exciting and creative applications spanning aerial photography, agriculture, product delivery, infrastructure inspection, aerial light shows, and hobby drone racing. Despite the usefulness of drones in many applications, they also raise increasing concerns regarding security, safety, and privacy. Drones have been used to smuggle weapons and drugs across borders. Using drones near airports presents safety concerns and may require airports to be closed until the surrounding airspace is secured. Drones have also been used as tools for corporate and nation-state espionage. Hence, there is a need for effective Counter-Unmanned Aircraft System (CUAS) solutions that can detect, monitor, and, if necessary, mitigate the drone threat. Summary of the Invention

[0004] The systems, methods, and devices of the present disclosure each have several innovative aspects, no single one of which is solely responsible for the desirable attributes disclosed herein.

[0005] In one aspect, a system for detecting the presence of a drone is provided, the system comprising: a radio frequency (RF) receiver configured to receive RF signals transmitted between a drone and a controller, the RF receiver configured to receive the RF signals including a synchronization signal for synchronization of the RF signals, at least one processor, and a computer readable memory in communication with the at least one processor, the computer readable memory storing computer executable instructions for causing the at least one processor to perform the following operations: receive a sequence of samples from the RF receiver, calculate a running sum of a prescribed number of double differentials of the sequence of received samples, and detect the presence of a drone based on the running sum.

[0006] In another aspect, a method for detecting the presence of a drone is provided, the method including receiving a sequence of samples of an RF signal transmitted between a drone and a controller, the RF signal including a synchronization signal for synchronization of the RF signal, obtaining a second derivative of the received sequence of samples, calculating a running sum of a prescribed number of second derivatives of the received sequence of samples, and detecting the presence of a drone based on the running sum.

[0007] In yet another aspect, a non-transitory computer-readable storage medium is provided having instructions stored thereon that, when executed, cause a computing device to perform operations including receiving a sequence of samples of an RF signal transmitted between a drone and a controller, the RF signal including a synchronization signal for synchronization of the RF signal, obtaining a second differential of the received sequence of samples, calculating a running sum of a predetermined number of second differentials of the received sequence of samples, and detecting a presence of a drone based on the running sum. [Brief description of the drawings]

[0008] [Figure 1]FIG. 1 illustrates an example environment including a drone detection system in accordance with an embodiment of the present disclosure. [Figure 2A] FIG. 2A illustrates an example drone detection system from FIG. 1 that may be used to detect, monitor, and / or mitigate drones, according to an embodiment of the disclosure. [Figure 2B] FIG. 2B shows an example of a drone from FIG. 1 that may be detected using the drone detection system of FIG. 2A according to an embodiment of the present disclosure. [Figure 2C] FIG. 2C illustrates an example controller from FIG. 1 that can be used to control a drone, according to an embodiment of the disclosure. [Figure 2D] FIG. 2D illustrates an example RF signal from FIG. 1 that may be transmitted from the controller of FIG. 2C to the drone of FIG. 2B, or vice versa. [Figure 2E] FIG. 2E illustrates an example of a signal received by the drone detection system from FIG. 2A as a result of intercepting the RF signal from FIG. 2D transmitted between the controller and the drone. [Diagram 3] FIG. 3 illustrates a method for detecting the presence of a drone according to an embodiment of the present disclosure. [Figure 4] FIG. 4 illustrates a method for detecting the presence of a drone according to an embodiment of the present disclosure. [Diagram 5] FIG. 5 illustrates a method for detecting the presence of a drone according to an embodiment of the present disclosure. [Figure 6] FIG. 6 is a graph comparing the simulated SNR with a lower bound in the low SNR region and with a lower bound in the high SNR region. [Figure 7A] FIG. 7A is a graph showing the detection error rate of a time-domain ZC sequence. [Figure 7B] Figure 7B is a graph showing the detection error rate for frequency domain ZC sequences without guard subcarriers. The blind estimation accuracy of the root of the ZC sequence also achieves 1% error at 15dB SNR. [Figure 8]1 is a graph showing blind route detection performance in the frequency domain with guard subcarriers. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] In recent years, the rapid growth of drone applications in industrial, commercial, and consumer domains has raised significant concerns regarding security, safety, and privacy, which has led to an increased demand for systems and techniques for drone detection, monitoring, and mitigation.

[0010] A CUAS system (or simply a "drone detection system") can operate using multiple stages. In a first stage, the drone detection system detects the presence of a drone and determines whether the drone is friendly or hostile. The drone detection system can accomplish this by intercepting signals exchanged between the drone and the controller. For example, certain aspects of the present disclosure may relate to detecting the presence of a drone that communicates with a controller using RF signals that include a synchronization signal, such as a Zadoff-Chu (ZC) sequence for synchronization of the RF signals.

[0011] In a particular aspect, detecting a drone includes receiving a sequence of samples of an RF signal (wherein the RF signal includes a synchronization signal for synchronization of the RF signal) transmitted between the drone and a drone controller, obtaining a second derivative of the received sequence of samples, calculating a running sum of a prescribed number of second derivatives of the received sequence of samples, and detecting the presence of a drone based on the running sum.

[0012] By detecting the presence of a drone employing a synchronization signal described herein for a particular type of synchronization signal (e.g., a ZC sequence), the drone detection system can detect the synchronization signal without knowledge of the root of the synchronization signal. As used herein, the root of these types of synchronization signals generally refers to a unique value used to generate and decode the synchronization signal. Using a unique root can prevent the synchronization signal from interfering with other RF communication signals generated using a different root. Furthermore, the drone detection system can be implemented with low complexity and potentially cost advantages compared to techniques that perform detection in parallel using all possible root values.

[0013] 1 illustrates an example environment 100 including a drone detection system 101 according to aspects of the disclosure. In a particular embodiment, the environment 100 includes the drone detection system 101, one or more drones 103A-103N, and one or more drone controllers 105A-105N (or simply "controllers"). An example of one or more drones 103A-103N is illustrated in FIG. 2B. An example of one or more controllers 105A-105N is illustrated in FIG. 2C.

[0014] In certain embodiments, each of the drones 103A-103N is configured to communicate with a corresponding one of the controllers 105A-105N via RF signals 107A-107N. Although not shown, in some embodiments, a single one of the controllers 105A-105N may be configured to control two or more of the drones 103A-103N.

[0015] The drone detection system 101 is configured to receive eavesdrops 109A-109N on communications between the drones 103A-103N and the controllers 105A-105N to detect the presence of the drones 103A-103N. For example, the drone detection system 101 may be configured to receive RF signals 107A-107N transmitted between the drones 103A-103N and the controllers 105A-105N to eavesdrop 109A-109N on communications between the drones 103A-103N and the controllers 105A-105N. In certain embodiments, once the drone detection system 101 is able to decode the RF signals 107A-107N, the drone detection system 101 can monitor the drones 103A-103N and take certain actions to mitigate potential threats from the drones 103A-103N. For example, as described with respect to FIG. 5, the drone detection system 101 can transmit jamming RF signals to disrupt communication between the detected drones 103A-103N and the controllers 105A-105N, and / or can masquerade as the controllers 105A-105N by sending commands to the drones 103A-103N to land or otherwise leave the environment 100.

[0016] 2A illustrates an example drone detection system 101 that may be used to detect the presence of one or more drones 103A-103N in accordance with aspects of the disclosure. In a particular embodiment, the drone detection system 101 includes a processor 111, a memory 113, a front end 115, multiple transmit antennas 117A-117N, and multiple receive antennas 119A-119N. In other embodiments, one or more of the antennas 117A-119N may be used for both transmitting and receiving signals.

[0017] In certain embodiments, drone detection system 101 is configured to receive an RF signal (e.g., RF signals 107A-107N of FIG. 1) via one of receive antennas 119A-119N. One of receive antennas 119A-119N provides the received RF signal to front end 115. In certain embodiments, front end 115 can process the received RF signal into a format readable by processor 111. For example, in certain embodiments, front end 115 can perform one or more of the following operations on the received RF signal: filtering, amplification, analog-to-digital conversion, etc.

[0018] 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., drones 103A-103N of FIG. 1) based on RF signals received via the receive antennas 119A-119N. Additionally, 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 antennas 117A-117N.

[0019] There are many different techniques that the drone detection system 101 can use to detect the presence of drones 103A-103N. For example, the drone detection system 101 can scan for radio waves on frequencies known to be used by a particular model(s) of drones 103A-103N. If a known protocol is identified, the drone detection system 101 can then decode the signal as if it were the intended receiver / controller 105A-105N. In some embodiments, 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 of information known by the controllers 105A-105N (such as device ID). As described below, blind detection of drones 103A-103N using a particular communication protocol (e.g., synchronization signal) is provided herein. When a drone is detected, the drone detection system 101 can provide alert(s) regarding the presence of one or more drones 103A-103N.

[0020] The drone detection system 101 may monitor for the presence of one or more drones 103A-103N. As part of the monitoring, the position of the one or more drones 103A-103N relative to the environment 100 may be monitored in real time to determine whether the position of the one or more drones 103A-103N falls inside or outside of an allowable airspace.

[0021] There are also numerous mitigating actions that can be taken by the drone detection system 101. For example, after detecting one or more drones 103A-103N, the drone detection system 101 can take one or more of the actions described with reference to Figure 5. For example, in certain embodiments, these actions can include do nothing / continue to monitor, drone-specific jamming, broadband jamming, and takeover of control.

[0022] 2B illustrates an exemplary drone 103 that may be detected using the drone detection system 101 according to aspects of the present 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 an RF signal 107 from the controller 105 (see FIG. 2C) and transmit the RF signal 107 back to the controller 105 (e.g., an image obtained from the camera 135). In certain embodiments, the RF signal 107 transmitted / received from the antenna 133 is provided to / from the processor 129 and processed by the front end 131. In certain embodiments, the propeller(s) 121 provide lift to the drone 103 and control the movement of the drone 103 as it navigates the airspace. The propeller(s) 121 may also include one or more motors (not shown) configured to power each propeller 121 individually.

[0023] In certain embodiments, motor controller(s) 123 are configured to receive instructions from processor 129 (e.g., based on instructions stored in memory 127 and RF signals 107 received from controller 105) to move drone 103 to a particular point in the airspace and convert the received instructions into motor position commands that are provided to propellers 121. In certain embodiments, battery 125 has a sufficient charge storage capacity to power each of the drone 103 components and enable propellers 121 to steer drone 103 for a predetermined period of time. Camera 135 can capture images in real time and provide the captured images to controller 105 via antenna 133, which can assist a user in controlling the movement of drone 103.

[0024] FIG. 2C illustrates an exemplary controller 105 that may be used to control the drone 103 according to aspects of the disclosure. In certain embodiments, the controller 105 includes 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 an RF signal 107 (e.g., an image obtained from the camera 135) from the drone 103 (see FIG. 2B) and transmit the RF signal 107 back to the drone 103 to control the movement of the drone 103. In certain embodiments, the RF signal 107 transmitted / received from the antenna 147 is provided to / from the processor 143 and processed by the front end 145. In certain embodiments, the input device 149 is configured to accept input from a user. The input from the user may be used by the processor 143 to generate commands to control the movement of the drone 103. In particular embodiments, the display 151 is configured to display images received from the drone 103 to the user to provide feedback regarding the current position of the drone 103 and its environment. In some embodiments, the display may be implemented as a pair of goggles worn by the user to provide a first person view of images captured by the camera 135.

[0025] FIG. 2D illustrates an exemplary RF signal 107 that may be transmitted from the controller 105 to the drone 103 or vice versa. With reference to FIG. 2D, in certain embodiments, the RF signal 107 includes a ZC sequence that is periodically transmitted (e.g., inserted into the RF signal at regular intervals) as part of the RF signal 107. In certain embodiments, the RF signal 107 further includes a number of data packets 202-206 that are used to provide data between the controller 105 and the drone 103 (e.g., including commands to move the drone and images captured by the camera 135). As previously described, the ZC sequence 201 used to synchronize the transmission of the RF signal 107 between the drone 103 and the controller 105 is generated using a ZC route that is unknown to the drone detection system 101.

[0026] 2E illustrates an example of a signal 109 received by the drone detection system 101 as a result of intercepting an RF signal transmitted between the controller 105 and the drone 103. Referring to FIG. 2E, in a particular embodiment, the signal 109 includes a number of samples 211-215 of the RF signal 107. Because the drone detection system 101 does not know the ZC root used to generate the ZC sequence 201, the position of the ZC sequence within the set of samples 211-215 is unknown to the drone detection system 101. Aspects of the present disclosure relate to techniques that can be used by the drone detection system 101 to detect the presence of the drone 103 using the signal 109 and to estimate the value of the ZC root.

[0027] Drone detection using synchronization signals 1, many of the drones 103A-103N on the market today employ orthogonal frequency division multiplexing (OFDM) modulation to transmit high-definition video footage and communicate with their controllers 105A-105N and goggles. Referring to FIG. 1, some drones 103A-103N employing OFDM can communicate with the controllers 105A-105N using a synchronization signal that is used to synchronize RF signals transmitted between the drones 103A-103N and the controllers 105A-105N.

[0028] One example of a synchronization signal that can be used for RF synchronization is a Zadoff-Chu (ZC) sequence. For example, the ZC sequence used for synchronization can have certain desirable properties, such as zero autocorrelation for non-zero offsets and low cross-correlation between two ZC sequences with different roots. Because drones 103A-103N typically use different ZC sequences to avoid interference when occupying the same airspace, the ZC sequence can be used as a unique signature to detect the presence of drones 103A-103N. However, the ZC sequences used in these systems are only known to the drone system. In particular, the ZC sequence can be generated using a unique ZC "root" value. There can be hundreds to thousands of possible ZC root values, and therefore it can be difficult to blindly detect the ZC sequences used by drones 103A-103N in real time (e.g., detect without knowledge of the ZC root value being used).

[0029] Aspects of the present disclosure relate to detecting drones 103A-103N that use synchronization signals to synchronize RF communications with controllers 105A-105N. In the following description, we provide an embodiment in which a ZC sequence is used as the synchronization signal, but those skilled in the art will recognize that the systems and techniques described herein can also be used to detect the presence of drones 103A-103N that use other types of synchronization signals.

[0030] To detect drone(s) 103A-103N that use ZC sequences for synchronization, the presence of drone 103A-103N can be detected by detecting the presence of these ZC sequences in RF signals received by drone detection system 101. ZC sequences are commonly used in communication protocols such as LTE systems. One technique for detecting ZC sequences typically uses a bank of correlators to search a very limited number of unknown ZCs. This is because the set of ZC sequences that an LTE receiver needs to blindly search is very limited, which is intended by the design of the LTE standard. For example, in a given cell, there are typically up to three ZC sequences for Primary Synchronization Signal (PSS) search and 64 ZC sequences for the Physical Random Access Channel (PRACH). These conventional techniques cannot be applied to blind ZC detection for systems that detect and monitor drones 103A-103N without incurring huge costs. This is because performing parallel detection for the large number of possible routes used for ZC sequences in communicating with drones 103A-103N would require too many parallel searches.

[0031] The number of drones 103A-103N that can operate within a given area is limited to a maximum of three. For this reason, a larger number of ZC routes are used to ensure that each unique pair of drones 103A-103N and controllers 105A-105N uses a different ZC route in generating the ZC sequence. To avoid using the same ZC route, the number of possible routes used to generate the ZC sequence that synchronizes the RF drone communications may be hundreds, thousands, or even more. Thus, potentially thousands of parallel searches would need to be performed to attempt detection of the ZC sequence using each of the possible ZC routes, which is impractical due to the costs associated with building such a system. Thus, blind detection in real time of thousands of unknown ZC sequences that may be used by drones is a major challenge.

[0032] Aspects of the present disclosure relate to systems and methods for low-complexity, cost-effective blind ZC detection. At least some of the techniques used to detect ZC sequences described herein are based on second-order differentiation for low-cost drone detection and surveillance systems. In particular, aspects of the present disclosure relate to low-complexity blind ZC detection systems and methods based on second-order differentiation that can detect in real time a large number of unknown ZC sequences used in OFDM-based drone systems. Certain embodiments of the present disclosure may be applied to blind ZC detection of ZC sequences deployed in the time domain, the frequency domain without guard bands, and the frequency domain with guard bands.

[0033] Model for OFDM-based synchronous signal communication To describe the systems and techniques used for the detection of ZC sequences, first, a model of the ZC sequence used for the synchronization of communication between drones 103A to 103N and controllers 105A to 105N will be provided. Consider an OFDM-based drone system with N subcarriers and a cyclic prefix (CP) of L samples. L is greater than the maximum multipath delay spread for removing inter-symbol interference. To facilitate synchronization, ZC sequences in the time domain or frequency domain are periodically transmitted by drones 103A to 103N and controllers 105A to 105N. The ZC sequence of length P with root u is defined by the following formula.

[0034]

Number

[0035] Here, P (P ≤ N) is an odd prime number, and u (1 ≤ u < P) is the root of the sequence. In the case of the time-domain ZC sequence, the time-domain OFDM sample s[n] (n = 0, 1, ···, L + N - 1) including the CP is given by the following formula.

[0036]

Number

[0037] In the case of the frequency-domain ZC sequence, the frequency-domain OFDM sample S[k] (k = -[N / 2], ···, N - [N / 2]) having the ZC sequence mapped to the center of the channel bandwidth is given by the following formula.

[0038]

Number

[0039] Note that the above formula includes the special case where P=N. The time domain sample is simply the Inverse Discrete Fourier Transform (IDFT) of S[k]. f s and Δ f are the sampling frequency and frequency offset, respectively. The received sequence y[n] is expressed by the following equation.

[0040]

number

[0041] Where: JPEG0007628308000005.jpg1242 where h is the channel gain and w is the power constraint E{|w| 2}=σ 2 For the purposes of modeling the ZC sequence, we assume that the channel experiences frequency-flat block fading. Under frequency-flat block fading, a single fading coefficient is applied to the entire bandwidth and is held constant for the OFDM symbol. We also assume that the ZC sequence length P is known and can be obtained by reverse engineering.

[0042] Overview of technology for detecting synchronization sequences 3 illustrates a method 300 for detecting the presence of a drone 103A-103N according to an embodiment of the present disclosure. Specifically, the method 300 includes detecting a synchronization sequence used in synchronizing communications between the drones 103A-103N and the controllers 105A-105N to detect the presence of the drones 103A-103N.

[0043] The method 300 begins at block 301. At block 302, the method 300 includes receiving a sequence of samples of RF signals 107A-107N transmitted between the drones 103A-103N and the controllers 105A-105N. The RF signals 107A-107N include a synchronization signal for synchronization of the RF signals between the drones 103A-103N and the controllers 105A-105N. In some implementations, the synchronization signal is a ZC sequence.

[0044] At block 304, the method 300 includes obtaining a second differential of the sequence of received samples. At block 306, the method 300 includes calculating a running sum of a prescribed number of second differentials of the sequence of received samples. Finally, at block 308, the method 300 includes detecting the presence of a drone based on the running sum. The method 300 ends at block 310.

[0045] The specific operations included in each of steps 302-308 may vary depending on the particular implementation of the communication protocol between the drones 103A-103N and the controllers 105A-105N. For example, these steps may vary depending on whether the ZC sequence is transmitted in the time domain or the frequency domain. Additionally, these steps may further vary based on whether a guard band is used if the ZC sequence is transmitted in the frequency domain. Further details regarding how these blocks 302-308 may be implemented are provided herein.

[0046] Time domain sequences with unknown roots In some implementations, the drones 103A-103N can communicate with the controllers 105A-105N by providing ZC sequences in the time domain. This section provides a description of techniques used to detect (e.g., blind detection) ZC sequences with unknown roots in the time domain.

[0047] Since P is an odd prime number, there are P-1 possible roots and corresponding ZC sequences. Typical values ​​of P in drone systems may range from hundreds to thousands. However, aspects of the present disclosure may be used to detect ZC sequences where the value of P is smaller or larger than the hundreds to thousands range. For values ​​of P that are in the above range, the complexity of a large correlator bank required to perform an exhaustive search (e.g., a search over all possible root values) is generally too high to perform in real time. To address this challenge, the drone detection system 101 may be configured to perform a low-complexity second-order differential method to blindly estimate the ZC root.

[0048] The single differential and double differential of a sequence s[n] are defined as follows:

[0049]

number

[0050] Here, the superscripts d and dd are the first and second derivative operators, respectively, and H denotes the Hermitian operator. Using (5) and (6), the ZC sequence x u The first and second derivatives of [n] can be derived as follows:

[0051]

number

[0052] Taking the first and second derivatives of both sides of equation (4), we obtain the following equation.

[0053]

number

[0054] where the effective noise term is JPEG0007628308000009.jpg1018 and JPEG0007628308000010.jpg1222 is expressed by the following formula.

[0055]

number

[0056] One result of these equations is the carrier frequency offset (CFO) term in (8), exp(jΔ θ ) disappears from (9) and does not affect the distribution of the noise term in (11).

[0057] To detect the ZC sequence in the received signal (e.g., in the sequence of samples received in block 302), the drone detection system 101 calculates y dd The normalized running sum in block 306 of method 300 of the last P-2 time domain derivative samples (calculated in block 304) of may be calculated as follows:

[0058]

number

[0059] Drone Detection System 101 is td In response to the fact that |[n]| exceeds a predetermined threshold, the presence of the drones 103A to 103N can be detected. The OFDM symbol boundary estimate is JPEG0007628308000013.jpg1363 ZC route estimate JPEG0007628308000014.jpg98 is expressed by the following formula.

[0060]

number

[0061] Where: JPEG0007628308000016.jpg1217 returns the angle of γ in [0;2π], where Hermitian arithmetic on the metric works to keep the angle positive. * If lands within the CP region, the analysis is the same for the special case of N=P, due to the periodic property of ZC sequences. If N≠P, then (13) is still valid, with less performance degradation.

[0062] It is not trivial to mathematically analyze the estimation error probability of (13). However, below, we will explain a method to determine the lower limit of the effective SNR in the high SNR region and the low SNR region that generally show the estimation performance. Specifically, the power bounds of (10) and (11) are obtained, and then the effective SNR is obtained as follows.

[0063]

number

[0064] Where: JPEG0007628308000018.jpg1299 where O(.) is the notation for big O. (14) uses a trigonometric inequality. (15) gives, for identically and independently complex white Gaussian distributions w[n-1] and w[n], JPEG0007628308000019.jpg12105 where α is an arbitrary real constant, JPEG0007628308000020.jpg1114 means that the distributions are identical. (16) is the Cauchy–Schwarz inequality and the result of the chi-square distribution, E|w[n]| 4 =2σ 2 Using σ 8and |h| 4 σ 4 (17) uses trigonometric inequalities. (18) uses (15), (16), and the Lp norm inequality for complex numbers. JPEG0007628308000021.jpg2071 Also, (19) can be derived from (7). (20) can be obtained using (18) and (19). Finally, the lower bounds of the effective SNR in the high SNR region and the low SNR region can be obtained as follows:

[0065]

number

[0066] Note that at high SNRs, (21) grows linearly with the native SNR. At low SNRs, (22) degrades as the fourth power of the native SNR, as expected from using second derivatives.

[0067] Frequency domain synchronization sequence without guard bands In some implementations, the drones 103A-103N can communicate with the controllers 105A-105N by providing ZC sequences in the frequency domain without guard bands. This section provides a description of techniques used to detect (e.g., blind detect) ZC sequences with unknown roots in the frequency domain without guard bands in certain embodiments.

[0068] In this implementation, an OFDM symbol contains N=P subcarriers, and the subcarriers are allocated as in (3) above. This implementation is a variation of the implementation described in the section entitled "Time-Domain Sequences with Unknown Roots".

[0069] The Fourier Dual property of the ZC sequence in both the DFT and IDFT forms is described in Property 1. Property 1: The P-point DFT and IDFT of (1) are expressed as follows:

[0070]

number

[0071] Here, the inverse root u -1 uuu -1 = 1 mod P.

[0072] After some manipulations, the first and second derivatives of (24) can be obtained as follows:

[0073]

number

[0074] Here, (25) is uu -1 =1modP was used.

[0075] For synchronization and blind ZC route detection, the drone detection system 101 calculates, in block 304, y dd The normalized running sum of [n] can be calculated as follows:

[0076]

number

[0077] Drone Detection System 101 is |γ fd1 In response to |[n]| exceeding a predetermined threshold, the presence of the drones 103A to 103N can be detected. The estimate of the symbol boundary is JPEG0007628308000026.jpg1268 The drone detection system 101 can estimate the ZC route as follows:

[0078]

number

[0079] JPEG0007628308000028.jpg98 teeth JPEG0007628308000029.jpg1053 The SNR bounds are the same as (21) and (22) in the section entitled "Time-Domain Sequences with Unknown Roots" because the power terms for the signal and noise are the same.

[0080] Frequency domain synchronization sequence using guard bands To meet the spectral mask, modern OFDM systems typically do not allocate data to edge subcarriers. Thus, in some implementations, the drones 103A-103N can communicate with the controllers 105A-105N by providing ZC sequences in the frequency domain using guard bands. This section provides a description of techniques used to detect (e.g., blind detection) ZC sequences with unknown roots in the frequency domain using guard bands.

[0081] In this implementation, an OFDM symbol contains N>P subcarriers. The ZC sequence is modulated on the subcarriers with a guard band as in (3). Unfortunately, in this case, property 1 no longer holds. Without this property, it is very difficult to derive a closed-form expression for detecting a ZC sequence with an unknown root in the time domain.

[0082] Assuming that a coarse timing acquisition has been performed, the drone detection system 101 may be able to blindly identify the root of the unknown ZC sequence. This is possible if there is another synchronization mechanism in other parts of the RF signal transmission between the drones 103A-103N and the controllers 105A-105N. A method to detect the drones 103A-103N is to determine whether the estimated OFDM symbol starts within the CP domain and therefore the time domain samples. JPEG0007628308000030.jpg1540 It may work under the assumption that represents a circularly shifted noisy version of the intended transmission.

[0083] With Δ being the unknown timing offset, phase ramping will occur in the frequency domain. JPEG0007628308000031.jpg1540 We can take the N-point DFT of JPEG0007628308000032.jpg1766 If so, use (1).

[0084]

number

[0085] where W[k] is the power E|W[k]| 2 = σ 2 Following the derivation in the section entitled “Time-Domain Sequences with Unknown Roots”, and using (7), the drone detection system 101 calculates Y as follows: d [k], Y dd [k], metric γ fd2 [k], and the route estimate JPEG0007628308000034.jpg98 can be sought.

[0086]

number

[0087] Metric γ in Certain Embodiments fd2 [k] does not need to be normalized when detecting ZC sequences with guard bands because it is only necessary to determine the angle of the resulting metric. Thus, the drone detection system 101 can normalize the metric γ fd2 In response to [k] exceeding a predetermined threshold, the presence of drones 103A-103N can be detected. In this embodiment, the same operations are performed except in the frequency domain, so the SNR characteristics may be the same as those in the section entitled "Time Domain Sequence with Unknown Root". However, unlike the time domain differentiation approach, certain frequency domain approaches may not be robust to CFO.

[0088] Example of how drone(s) can be used for surveillance Once a drone 103 is detected, for example using one of the above metrics, the drone detection system 101 may monitor the drone 103 for a period of time before potentially taking mitigating action against the drone 103. FIGURE 4 is an example method 400 that may be performed by the drone detection system 101 to monitor one or more of the drones 103.

[0089] The method 400 begins at block 401. In some implementations, the method 400 may be performed in response to detecting the drone 103 based on the method 300 of FIG. 3. At block 402, the method 400 includes decoding an RF signal 107 transmitted between the drone 103 and the controller 105. If the RF signal 107 includes a ZC sequence, decoding the RF signal 107 may be performed using a ZC root determined according to aspects of the present disclosure.

[0090] At block 404, the method 400 includes storing data associated with the drone 103 from the decoded RF signal 107. For example, the data may include any activity performed by the drone 103 (e.g., flight data), drone behavior that may indicate whether the drone 103 is friendly or hostile, a unique drone identifier, etc. At block 406, the method 400 includes determining whether mitigation action should be taken against the drone 103. In response to determining that mitigation action is warranted, the method 400 may end at block 406, where mitigation action 500 may be taken. In response to determining that mitigation action is not warranted, the method 400 returns to block 402.

[0091] Exemplary Methods for Mitigating Drone(s) After determining that mitigation of the drone 103 is appropriate, in certain embodiments, the drone detection system 101 implements one or more of a number of different mitigation actions in accordance with aspects of the disclosure. Figure 5 is an example method 500 that may be implemented by the drone detection system 101 to mitigate one or more of the drones 103.

[0092] Method 500 begins at block 501. In certain implementations, method 500 may be performed in response to determining that mitigation action is warranted at block 406 of FIG. 4. At block 502, method 500 includes selecting a mitigation technique to implement. Method 500 then includes continuing to one of blocks 504-510 based on the mitigation technique selected at block 502.

[0093] At block 504, method 500 includes continuing to monitor the drone 103, which may include returning to block 402 of method 400. For example, if the drone 103 is determined to be friendly and / or if the drone detection system 101 does not have legal authority to take more aggressive action, the drone detection system 101 may only be permitted to continue monitoring the drone 103 while alerting a user to the presence of the drone 103.

[0094] At block 506, the method 500 includes performing drone-specific jamming. For example, if the drone detection system 101 deciphers the ZC route used by the drone 103 and the controller 105, the drone detection system 101 may be able to jam the RF signal 107 to affect the movement of the target drone 103 using the ZC route without affecting the operation of other drones 103 in the environment 100.

[0095] At block 508, the method 500 includes the drone detection system 101 performing broadband jamming. In certain embodiments, broadband jamming may be appropriate if the drone detection system 101 does not have sufficient information about the communication protocol used by the RF signal 107 to perform drone-specific jamming and if the broadband jamming will not affect other friendly drones 103 in the environment 100.

[0096] At block 510, the method includes the drone detection system 101 taking over control of the drone 103. For example, in certain embodiments, using the ZC route detected according to aspects of the present disclosure, the drone detection system 101 can send commands to the drone 103 to cause the drone 103 to perform a particular maneuver, such as landing the drone 103 in a safe area. The method 500 ends at block 512.

[0097] simulation The following description provides a validation of the above technique for detecting drones 103A-103N. The results were validated using a Monte Carlo simulation with 30,000 iterations. First, validation of the derived SNR lower bounds for the high and low SNR regions is shown. Figure 6 is a graph comparing the simulated SNR (20) with the lower bounds for the low SNR region (22) and the lower bounds for the high SNR region (21). In the low SNR region, (22) closely matches the simulated SNR. In the high SNR region, (21) has a certain gap from the simulated SNR. As mentioned above, this gap is due to the looseness of some approximations. Next, a ZC sequence developed in the time domain is considered with P=1021, N=1024, L=80, and an arbitrary ZC root of 100. Since P is a prime number, there are P-1=1020 possible ZC roots. Simulations for the case without a CFO and for the case where the CFO is 5% of the subcarrier spacing are described below.

[0098] FIG. 7A is a graph showing the detection error rate of the time-domain ZC sequence. When the estimated root is not the same as the true root of 100, a detection error event occurs. As can be seen from FIG. 7A, the blind estimation accuracy of the ZC sequence root achieves an error probability of 1% at an SNR of 15 dB. Therefore, in the medium to high SNR region, the method described herein is effective in both synchronization to the time-domain ZC sequence and blind estimation of the ZC sequence root. It should also be noted that the CFO does not affect the detection performance.

[0099] Figure 7B is a graph showing the detection error rate for frequency domain ZC sequences without guard subcarriers. The blind estimation accuracy of the ZC sequence root achieves a 1% error probability at an SNR of 15 dB. This is similar to the time domain version, as analyzed in the section entitled "Frequency Domain Synchronization Sequences without Guard Bands". In this case, the CFO also has no effect on the detection performance.

[0100] Finally, Figure 8 is a graph showing blind route detection performance in the frequency domain with guard subcarriers. Both perfect and offset symbol timing with 20 samples are plotted. Note that the timing offset does not affect the detection performance. However, as expected, a CFO size of 5% of the subcarrier spacing substantially degrades the detection performance.

[0101] Implementation system and terminology Implementations disclosed herein provide systems, methods, and devices for detecting the presence of a drone. 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, when a first component is "coupled" to a second component, the first component may be indirectly connected to the second component through another component, or may be directly connected to the second component.

[0102] The drone detection functions described herein may be stored as one or more instructions on a processor-readable medium or computer-readable medium. The term "computer-readable medium" refers to any available medium accessible by a computer or processor. By way of example and not limitation, such media may include RAM, ROM, EEPROM, flash memory, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage, or any other medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer. It should be noted that computer-readable media may be tangible and non-transitory. As used herein, the term "code" may refer to software, instructions, code, or data that is executable by a computing device or processor.

[0103] The methods disclosed herein comprise one or more steps or actions for achieving the described method. The steps and / or actions of the methods 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 the proper operation of the described method, the order and / or use of specific steps and / or actions may be modified without departing from the scope of the claims.

[0104] As used herein, the term "plurality" refers to two or more. For example, a plurality of components refers to two or more components. The term "determining" encompasses a wide variety of operations, and thus "determining" can include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up a table, 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.

[0105] The term "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."

[0106] The above description of the disclosed embodiments 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 general principles defined herein may be applied to other implementations without departing from the scope of the present invention. For example, those skilled in the art will recognize that many corresponding alternative and equivalent structural details may be employed, such as equivalent methods of fastening, mounting, coupling, or engaging tool parts, equivalent mechanisms for generating specific actuation motions, and equivalent mechanisms for supplying 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.

Claims

1. A system for detecting the presence of a drone, a radio frequency (RF) receiver configured to receive an RF signal transmitted between the drone and the controller, the RF signal including a synchronization signal for synchronization of the RF signal; At least one processor; a computer-readable memory in communication with the at least one processor storing computer-executable instructions to cause the at least one processor to perform the following operations: receive a sequence of samples from the RF receiver; take a second differential of the received sequence of samples; calculate a running sum of a prescribed number of the second differentials of the received sequence of samples; determine a metric based on the running sum; estimate a route of the sequence of samples based on the metric; detect the presence of the drone based on the metric; and synchronize with the RF signal based on the estimated route; A system comprising:

2. A system for detecting the presence of a drone, a radio frequency (RF) receiver configured to receive an RF signal transmitted between the drone and the controller, the RF signal including a synchronization signal for synchronization of the RF signal; At least one processor; a computer readable memory in communication with the at least one processor storing computer executable instructions to cause the at least one processor to perform the following operations: receive a sequence of samples from the RF receiver; take a second differential of the received sequence of samples; calculate a running sum of a prescribed number of the second differentials of the received sequence of samples; determine a metric based on the running sum; detect the presence of the drone based on the metric; and synchronize with the RF signal based on the running sum; Equipped with The metric is determined according to the following formula: Here, γ ｆｄ２ [k] is the metric, Y ｄｄ [k] is the second derivative and P is the length of the synchronization signal. system.

3. A system for detecting the presence of a drone, a radio frequency (RF) receiver configured to receive an RF signal transmitted between the drone and the controller, the RF signal including a synchronization signal for synchronization of the RF signal; At least one processor; a computer readable memory in communication with the at least one processor storing computer executable instructions to cause the at least one processor to perform the following operations: receive a sequence of samples from the RF receiver; take a second differential of the received sequence of samples; calculate a running sum of a predetermined number of the second differentials of the received sequence of samples; detect the presence of the drone based on the running sum; and synchronize with the RF signal based on the running sum; and Equipped with the synchronization signal includes a time domain sequence and the running sum is normalized; system.

4. The synchronization signal includes a Zadoff-Chu (ZC) sequence.

3. A system according to any one of claims 1 to 3.

5. The system of claim 1 or claim 2, wherein the synchronization signal comprises a frequency domain sequence including a guard band.

6. The computer-readable memory further stores the computer-executable instructions to cause the at least one processor to perform operations to determine that the metric is greater than a threshold; The system of claim 1 or claim 2, wherein the detection of the presence of the drone is further based on the determination that the metric is greater than a threshold.

7. 3. The system of claim 2, wherein the computer-readable memory further stores the computer-executable instructions that cause the at least one processor to perform an operation of estimating a root of the sequence of samples based on the metric.

8. The system of claim 7 , wherein synchronizing with the RF signal is performed using the estimated route.

9. The system of claim 1 , wherein the metric is determined according to the following formula: Here, γ ｆｄ２ [k] is the metric, Y ｄｄ [k] is the second derivative and P is the length of the synchronization signal.

10. The system of claim 1 or claim 2, wherein the synchronization signal includes a time domain sequence and the running sum is normalized.

11. The computer-readable memory further stores the computer-executable instructions for causing the at least one processor to perform an operation of determining that an absolute value of the normalized running sum is greater than a threshold; 4. The system of claim 3, wherein the detection of the presence of the drone is further based on the determination that an absolute value of the normalized running sum is greater than the threshold.

12. 4. The system of claim 3, wherein the computer-readable memory further stores the computer-executable instructions that cause the at least one processor to perform an operation of estimating a root of the sequence of samples based on the normalized running sum.

13. 1. A method for detecting the presence of a drone, comprising: Receiving a sequence of samples of an RF signal transmitted between a drone and a controller, the RF signal including a synchronization signal for synchronization of the RF signal, the synchronization signal including a time domain sequence; obtaining a second derivative of the sequence of received samples; calculating a running sum of a prescribed number of second derivatives of the sequence of received samples, the running sum being normalized; Detecting the presence of the drone based on the running sum; synchronizing with the RF signal based on the running sum; The method includes:

14. 14. The method of claim 13, wherein the synchronization signal comprises a Zadoff-Chu (ZC) sequence. method.

15. determining that the absolute value of the normalized running sum is greater than a threshold value; Including, 15. The method of claim 13 or claim 14, wherein detecting the presence of the drone is further based on determining that the absolute value of the normalized running sum is greater than a threshold.

16. 1. A non-transitory computer-readable storage medium, comprising: When executed, the computer device receiving a sequence of samples of an RF signal transmitted between a drone and a controller, the RF signal including a synchronization signal for synchronization of the RF signal; Obtaining a second derivative of the sequence of received samples; Calculating a running sum of a prescribed number of second derivatives of the sequence of received samples; determining a metric based on the running sum; estimating a root of the sequence of samples based on the metric; Detecting the presence of the drone based on the metric; synchronizing with the RF signal based on the estimated route; It stores the commands to perform each operation. A non-transitory computer-readable storage medium.

17. 20. The non-transitory computer-readable storage medium of claim 16, wherein the synchronization signal comprises a frequency domain sequence that includes a guard band.

18. The instructions, when executed, cause at least one of the computing devices to perform an operation of determining that the metric is greater than a threshold; 18. The non-transitory computer-readable storage medium of claim 16 or claim 17, wherein the detection of the presence of the drone is further based on the determination that the metric is greater than a threshold.

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