System and method for detecting scrambling seeds in communications with unmanned aerial systems - Patents.com

Abstract

A system and method for detecting a scrambling seed in communication between a drone and a controller is described. The system includes a radio frequency (RF) receiver configured to receive an RF signal transmitted between the drone and the controller. The RF signal includes scrambled data, which includes repetitions of unscrambled data based on a known scrambler with an unknown scrambling seed. The system further includes a memory device in communication with a hardware processor and having stored thereon computer-executable instructions for causing the hardware processor to identify the minimum number of bits required in each segment of scrambled data for data synthesis by finding an invertible predetermined matrix. The hardware processor is configured to determine the unknown scrambling seed based on a function combining the predetermined matrix, a transition matrix of the scrambler, and the received segment of scrambled data.

Description

[Background technology]

[0001] This application claims priority to U.S. Provisional Application No. 62 / 937,596, filed November 19, 2019, which is incorporated by reference in its entirety. [Technical field]

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

[0003] <Description of Related Art> Unmanned Aerial Systems (UAS), more commonly known as drones, are widely used in many interesting and creative applications, including aerial photography, agriculture, product delivery, infrastructure inspection, aerial light shows, and recreational drone racing. Despite the usefulness of drones in many applications, they also raise increasing security, safety, and privacy concerns. Drones have been used to smuggle weapons and drugs across borders. The use of drones near airports presents safety concerns, which may require airports to be shut down until the surrounding airspace is secured. Drones are also used as a tool for corporate and nation-state espionage. This calls for an effective Counter-Unmanned Aircraft System (CUAS) solution that can detect and monitor drones and mitigate the drone threat if necessary. Summary of the Invention [Problem to be solved by the invention]

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

[0005] A system and method for detecting communication between a drone and a controller are described. In one embodiment, the system comprises a radio frequency (RF) receiver configured to receive an RF signal transmitted between the drone and the controller. The RF signal includes scrambled data r based on a known scrambler with an unknown scrambling seed s. The system further comprises a memory device in communication with a hardware processor and having stored thereon computer-executable instructions for causing the hardware processor to obtain a predetermined matrix based at least in part on a state transition matrix F of the known scrambler, and to determine the unknown scrambling seed s based on a function including the predetermined matrix, a segment of the scrambler output sequence f having a known initial state, and a segment of the scrambled data r.

[0006] In another embodiment, the method includes receiving a radio frequency (RF) signal transmitted between the drone and a controller. The RF signal includes scrambled data r based on a known scrambler and an unknown scrambling seed s. The method further includes obtaining, by a hardware processor, a predetermined matrix based at least in part on a state transition matrix F of the known scrambler, and determining, by the hardware processor, the unknown scrambling seed s based on a function including the predetermined matrix, a segment of the scrambler output sequence f having a known initial state, and a segment of the scrambled data r. [Brief description of the drawings]

[0007] [Figure 1] 1 illustrates an example environment including a drone detection system according to an aspect of the present disclosure. [Figure 2A] 1 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] 2B illustrates an example drone from FIG. 1 that can be detected by the drone detection system of FIG. 2A according to an embodiment of the present disclosure. [Figure 2C] 2 illustrates an example controller from FIG. 1 that may be used to control a drone according to an embodiment of the present disclosure. [Diagram 3] An exemplary system model of an LTE-based unmanned transmitter (TX), an additive white Gaussian noise (AWGN) channel, and a CUAS or unmanned surveillance receiver (RX) is shown. [Figure 4] 1 illustrates a method for blind detection of scrambling seeds for LTE-based drones, according to one embodiment of the present invention. [Diagram 5] 1 illustrates a method for blind detection of a scrambling seed using knowledge of the encoding scheme according to one embodiment of the present invention. [Figure 6] 1 illustrates a method for blind detection of a scrambling seed without knowledge of the encoding scheme, according to one embodiment of the present invention. [Figure 7] 13 is a graph comparing blind seed detection and coding knowledge with soft combining, hard combining, and no combining. [Figure 8] 13 is a graph comparing soft combining with blind seed detection without combining, with and without knowledge of the encoding scheme. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] In recent years, the rapid growth of drone applications in industrial, commercial, and consumer domains has raised significant security, safety, and privacy concerns, which has led to an increased demand for systems and technologies for drone detection, monitoring, and mitigation.

[0009] A CUAS system (or simply a "drone detection system") may operate using multiple stages. In the 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 may accomplish this by eavesdropping on signals exchanged between the drone and the controller. For example, some aspects of the present disclosure may relate to detecting the presence of a drone communicating with a controller using RF signals that include synchronization signals, such as a Zadoff-Chu (ZC) sequence for synchronization of RF signals.

[0010] In an aspect, detecting a drone may include receiving a sequence of samples of an RF signal transmitted between a drone and a drone controller, the RF signal including a synchronization signal for synchronization of the RF signal, obtaining a double difference of the received sequence of samples, calculating a running sum of a defined number of double differences of the received sequence of samples, and detecting the presence of the drone based on the running sum.

[0011] For certain types of synchronization signals (e.g., ZC sequences), by using the synchronization signal to detect the presence of a drone as described herein, the drone detection system can detect the synchronization signal without knowing 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 can be cost-effective compared to techniques that perform detection in parallel using all possible root values.

[0012] 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.

[0013] 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.

[0014] The drone detection system 101 is configured to receive eavesdropping 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 being transmitted between the drones 103A-103N and the controllers 105A-105N to eavesdropping 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, the drone detection system 101 can disrupt communication between the detected drones 103A-103N and the controllers 105A-105N by sending commands to the drones 103A-103N to land in the environment 100 or otherwise leave the environment 100, and / or send jamming RF signals to spoof the controllers 105A-105N.

[0015] I. Introduction

[0016] Many of the drones 103A-103N, also known as unmanned aerial vehicles (UAVs) in the industrial, commercial, and consumer sectors, utilize 3GPP® Long-Term Evolution (LTE), modified LTE, or LTE-like wireless protocols for communication between the drones and their control devices or controllers 105A-105N, such as radio controllers and goggles. In the physical layer (PHY) of these LTE-based drone systems, the baseband processing in the transmitter (TX) includes a scrambler that randomizes the channel-coded data before modulation, which provides a level of security and data privacy in addition to other benefits. The scrambling sequence is typically generated by a linear feedback shift register (LFSR) with a scrambling seed, also called a scrambling offset, to initialize the state of the LFSR. The receiver (RX) of the drone system knows the seed and can therefore generate the same scrambling sequence to descramble the received data without the need for blind detection. However, the seed is not known to any device outside the drone system. Furthermore, the scrambling seed is tightly tied to the identity of the drone device. Detecting the seed helps to decode important information and reveal the identity of the drones 103A-103N. Therefore, the scrambling seed is essential for drone systems (CUAS) and drone detection / monitoring systems to blindly detect the scrambling seed within critical time constraints. Blindly detecting the scrambling seed in real time is a non-trivial task. In LTE-based drones, the scrambling seed is a combination of two 31-bit Gold sequences generated by two LFSRs of length 31, one known seed, and the other unknown seed for blind detection. Therefore, with 2,147,483,648 (=2^31) choices, brute force methods cannot immediately determine the unknown seed in real time. Also, there is a risk that the seed will change periodically, which imposes strict time constraints for seed detection.Blind seed detection can be further complicated by noise and other channel impairments that can result in bit errors in the received scrambled data. The combination of multiple copies of the descrambled data performed by a normal RX cannot be directly applied to the noisy scrambled data to maximize the received signal-to-noise ratio (SNR) since the received data is randomized by scrambling. Studies of the reconstruction of the scrambler LFSR polynomial and the initial state including the secret key of the stream cipher are abundant in the literature. However, there are only a few solutions that can handle the received bit errors due to channel noise. Note that a conventional blind scrambling seed estimation scheme using the redundancy introduced by the channel coding of the scrambled data may be used. This scheme requires knowledge of the coding scheme used by the TX (e.g., drone). Furthermore, a method is provided for reconstructing the scrambler polynomial from the received data stream in the presence of noise. However, this method does not reconstruct the scrambling seed. Reconstructing the initial state of the scrambler can improve the performance of the above method. However, this improvement requires knowledge of a large number of samples and coding knowledge and can take a long time to implement, especially for high degree polynomials.

[0017] It is desirable to use an efficient and fast method for blind scrambling seed estimation in a noisy channel environment that can meet strict time requirements. Therefore, this disclosure describes a blind scrambling seed detection method using soft combining of scrambled repetitive rate-matched data to improve detection performance in noisy channels. Rate matching is a mechanism specified in the LTE standard for placing transmitted coded data into assigned OFDM resources to achieve a required data rate.

[0018] When the data size to be transmitted is small (e.g., control information or small transport blocks), the interleaved codewords will be repeatedly placed in the resources. This repetitive structure disappears after scrambling, but in one embodiment, it is utilized to soft-combine the received data after algebraic manipulation. Similar to the blind scrambling seed estimation method using the redundancy introduced by the channel coding of the scrambled data, the method of the present embodiment uses this knowledge to remove the coding effect of the null subspace of the coding and reveal the scrambling state when the channel coding scheme is known. When the coding scheme is unknown, the method of the present embodiment utilizes the repetitive structure to remove the effect of the transmitted data and facilitate seed detection. As described below, one novel point is the algebraic derivation that makes the soft-combining of the scrambled data feasible in the rate matching structure. Simulation results show that such a seed detection method with coding knowledge significantly improves the detection performance over conventional approaches. Also, the method without coding knowledge compensates for the performance loss due to the lack of the knowledge. As further described herein, a system model for blind detection is provided. Furthermore, a method of blind seed detection with coding knowledge is described. We also present a method for blind seed detection without knowledge of the coding.

[0019] 2A illustrates an example drone detection system 101 that may be used to detect the presence of one or more drones 103A-103N according to aspects of the present 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.

[0020] In certain embodiments, drone detection system 101 is configured to receive RF signals (e.g., RF signals 107A-107N of FIG. 1) via any of receive antennas 119A-119N. One of receive antennas 119A-119N provides the received RF signal to front end 115. In some embodiments, front end 115 may process the received RF signal into a format that can be read by processor 111. For example, in certain embodiments, front end 115 may perform one or more of the following actions on the received RF signal: filtering, amplification, analog-to-digital conversion, etc.

[0021] 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 for transmission 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.

[0022] There are several different techniques that the drone detection system 101 may use to detect the presence of drones 103A-103N. For example, the drone detection system 101 may scan radio waves at frequencies known to be used by a particular model or models of drones 103A-103N. If a known protocol is identified, the drone detection system 101 may decode the signal as if it were the intended receiver / controller 105A-105N. Depending on the embodiment, these decoding steps may include synchronization, channel estimation, deinterleaving, descrambling, demodulation, and error control decoding. In certain embodiments, the drone detection system 101 may 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. Once detected, the drone detection system 101 can provide an alert(s) regarding the presence of one or more drones 103A-103N.

[0023] 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 wanders inside or outside of an acceptable airspace.

[0024] FIG. 2B illustrates an example of a drone 103 that may be detected using the drone detection system 101 in accordance with aspects of the present disclosure UAV. 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, memory 127, a processor 129, a front end 131, an antenna 133, and a camera 135. As noted 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 captured by the camera 135). In some 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 propellers 121 provide lift and controlled movement of the drone 103 as it maneuvers through the airspace. The propellers 121 may also include one or more motors (not shown) configured to power each propeller 121 individually.

[0025] In certain embodiments, the motor controller(s) 123 are configured to receive instructions from the processor 129 (e.g., based on instructions stored in memory 127 and RF signals 107 received from the controller 105) to move the drone 103 to a particular point in the airspace and translate the received instructions into motor position commands that are provided to the propeller(s) 121. In certain embodiments, the battery 125 has sufficient power storage to power each of the components of the drone 103 and enable the propellers 121 to steer 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 help a user control the movement of the drone 103.

[0026] 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 acquired 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 receive input from a user that the processor 143 can use 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 the images captured by the camera 135.

[0027] II. System Model

[0028] FIG. 3 shows an exemplary system model of an LTE-based drone transmitter (TX), an additive white Gaussian noise (AWGN) channel, and a CUAS or drone surveillance receiver (RX). The system model considered in this embodiment is shown in FIG. 3. The system includes an LTE-based drone transmitter (TX), an AWGN (Additive White Gaussian Noise) channel, and a CUAS or drone surveillance receiver (RX). Here, let m be a message for transmission at a fixed time interval T. In an LTE-based drone, m may be a transport block or control information carried from the medium access control (MAC) layer plus cyclic redundancy check (CRC) bits, and is encoded by a linear code such as a block code, a tail-bit convolutional code, or a turbo code. Let C be the state transition matrix of the channel encoder. The construction of C can be found in [3] for the case of convolutional encoding. Then, the codeword c=Cm of length c is rate-matched to d=Rc=RCm of length r by the rate-matching transition matrix R. R can be similarly constructed according to a predefined rate matching process, such as section 5.1.4 of [2] in 3GPP LTE systems. If R is not known, many reverse engineering methods can be used to discover R.

[0029] In R, c is typically interleaved with v, which is stored in a circular buffer. A cyclically rotated version of v, denoted by w, is then used repeatedly to fill the allocated OFDM resource, a rate-matching output d of length r, where N r ≧2, Nr is the number of copies of w at d, and for simplicity, r=N r ·c. That is, d=[w T w T …w T ] TThis is a reasonable assumption when the message m contains control information or transport blocks of small size. Typically, multiple copies of the codeword are transmitted to facilitate decoding at low SNR. Thus, the length of the codeword is much smaller than the size of the allocated resource.

[0030] Then, the rate-matched data d is scrambled by a scrambling sequence f. Similar to Section 7.2 of [1], let f be the exclusive-or of two L-bit linear feedback shift register (LFSR)-based scramblers outputs starting from state n (a Galois field addition of two elements denoted by GF(2)). Let i=1,2 and the initial state (scrambling seed) of scrambler i be s0. i , state transition matrix F i In this embodiment, s0 (as defined, for example, in the 3GPP standard [1]) 1 is known, while the seed s0 2 Assume that the polynomials of both scramblers are known (e.g., as defined in [1]) and that the state transition matrix F i is known and can be easily constructed. Since the scrambling starts from state n, the r×n+r extraction matrix En=[0 r×n I r ] extracts the sequence from state n to state n+r. Thus, the scrambling sequence of scrambler i from state n to state n+r-1 is: f n i =E n f0 i =E n F i S0 i =F n i s0 i (1)

[0031] Here, F n i =E n Fi is the transition matrix of scrambler i starting from state n. The scrambled data r is given by

number

[0032] Where:

number

[0033] The scrambled data r is then mapped to data symbols followed by modulation on the OFDM subcarriers. The OFDM modulated signal x is sent through the channel. The received signal y at the receiver is given by: y=hx+n (3)

[0034] where h is the channel gain, n~(0, σ 2 ) is the variance σ 2 After OFDM demodulation and modulation demapping, the received scrambled bits r ~ In the case of blind detection, r ~ is typically expressed in the form of a log-likelihood ratio (LLR) as implied by soft modulation demapping (r ~ ) llr The unknown scrambling seed s0 i is correctly detected, and r ~ If m=r, then the message m can be successfully decoded after descrambling and de-rate matching.

[0035] FIG. 4 illustrates a method for blind detection of scrambling seeds of an LTE-based drone, according to an embodiment of the present invention. In FIG. 4, an overview of the method for blind detection based on the system model of FIG. 3 is illustrated. Thus, the details described in relation to the system model of FIG. 4 equally apply to the method described in relation to FIG. 4. As illustrated in FIG. 4, the process starts at block 402. As described above, the model system includes a transmitter (TX) of an LTE-based drone, an Additive White Gaussian Noise (AWGN) channel, and a CUAS or drone surveillance receiver (RX). At block 404, the method includes performing OFDM demodulation and modulation demapping to obtain received scrambled bits. At block 406, the method includes using the received scrambled bits to generate an unknown scrambling seed s ~ 0 2 At block 408, the method includes detecting a scrambling sequence f constructed using the detected seed. ~ The scrambled bits received by the ~ At block 410, the method includes obtaining d ~ From c ~ At block 412, the method includes performing de-rate matching on the message m ~ To get c ~ At block 414, the method includes decoding the drone or controller ID and flight information to m ~ The method ends at block 416.

[0036] III. Blind Scramble Seed Detection Using Coding Knowledge

[0037] Here we describe a blind scrambling seed detection method that soft-combines the scrambling seed using a recursive rate matching structure in the received scrambled data with knowledge of the channel coding scheme. The described method can significantly improve detection performance over non-combining and hard-combining approaches.

[0038] A. Blind scrambling seed detection in a noise-free channel

[0039] 5 illustrates a method for blind detection of a scrambling seed using knowledge of the coding scheme, according to one embodiment of the present invention. The method begins at block 502. In the absence of noise (e.g., the SNR of the received signal is high enough that all scrambled bits are correctly received) and in the absence of a constant channel gain, r ~ = r, where R is the rate matching matrix and F is the scrambler matrix. n 1 , seed s0 1 The scramble seed s0 is obtained by using a modified method from [3] in which 2 Therefore, in block 504, the method detects the rate matching matrix R, the coding matrix C, and the scrambler matrix F 1 and F 2 At block 506, the method includes calculating a coding and rate matching transition matrix G=RC. ~ Let G be a matrix whose rows span the null subspace of G such that G=0. ~ At block 508, the method may define G ~ The null space matrix G such that G=0. ~ This includes finding the Equation (2) can be rearranged as follows:

number

[0040] In equation (4), G ~ Left multiply by G ~Consider using G=0 to get:

number

[0041] Both sides of equation (6) are (G ~ F n 2 ) T Multiplied by (G ~ F n 2 ) T GF n 2 If is invertible, then s0 2 can be uniquely calculated as follows:

number

[0042] (7) is the case when all scrambled bits are received correctly, i.e., r ~ It can be used only if =r.

[0043] On the other hand, r ~ If there is an erroneously received bit in , then in a noisy channel, ~ ≠r. In this case, (G ~ F n 2 ) T GF n 2 If is still invertible, then equation (7) becomes:

number

[0044] Therefore, if any bit is received in error, an incorrect seed is obtained from equation (8). This issue is addressed by the method description in Section III-B below. Another problem with this approach is that when the type of encoder used at the transmitter is unknown, the null subspace G of G = RC is not known. ~The problem is that we cannot construct C to find . This problem is solved by the method described in Section IV.

[0045] B. Blind scramble seed detection in noisy channels

[0046] Also, if the received scrambled data contains errors, it is preferable to use a repetitive rate matching structure.

number

number

[0047] r k , d k Let k be the l-bit interval of the scrambled data r and the unscrambled data d, which are from bit kc to bit kc+l-1, and c≦l≦2c, respectively. Here, if l=c, then d k =w, N=N r (where N is the number of repeating segments for synthesis), where c <l≦2cならば、d k =[w T w0...w l-c-1 ] T , N=N r −1 (where the j-th bit of w is wj). Due to the repetitive structure of d, the following formula holds: d0=d1=d2=…=d (N-1) (10)

[0048] Here, forgiving a slight abuse of notation, the state transition matrix F of the scrambler i from state n+kc to n+kc+l-1 is k i and the output sequence f k i , set i=1,2 respectively. r ~ k =rk As, r k d k For k=0, 1, ..., N-1, the following is given:

number

[0049] d k =R k C m (In the formula, R k is the rate matching transition matrix from state kc to kc+l-1). G k =R k Let C be the combination matrix of coding and rate matching for states kc to kc+l-1. ~ k Gk=0, k=0, 1, …, N-1. k The null subspace matrix G ~ k Similarly to equation (6), equation (11) can be defined as G ~ k Multiplying by gives:

number

[0050] Both sides of equation (12) are (G ~ k F k 2 ) T Multiply by (G ~ k F k 2 ) T G ~ k F k 2 By finding the smallest value of l such that the inverse of exists, we get:

number

[0051] Where: Xk=((G ~ k F k 2 ) T G k F k 2 ) -1 (G ~ k F k 2 ) T G ~ k It is. Received data 0、k 2 All the seeds detected from each segment of are identical and accurate. This means that 0、0 2 =…s 0、N-1 2 =s0 2 However, due to noise, r ~ k ≠r k If s 0、k 2 ≠s0 2 Fortunately, ~ k The errors in G occur independently. To mitigate the effect of reception errors on seed detection, it is desirable to soft combine all detected seeds with the received scrambled data. In block 510, the method includes: ~ From G ~ k , F n 2 From F k 2 Then, find the smallest l such that the inverse of (13) exists, and find X k This includes calculating

[0052] Received scrambled data r ~ k is usually expressed in real-valued LLR format (r ~ k ) llr ∈R lSince the scrambled data cannot be directly combined, it is desirable to derive a seed estimate in terms of the LLR values ​​of the scrambled data received for each segment. The seed estimates for all segments can then be soft-combined. To facilitate the soft-combining, it is desirable to first introduce the following definitions to convert binary values ​​to soft values ​​and vice versa:

[0053] Definition 1: For a binary bit x ∈ {0,1}, the soft bit of x, denoted by L(x), is defined as y = L(x) = 1.0 - 2.0 · x, y ∈ {+1.0, -1.0} ⊂ R.

[0054] Definition 2: For any y∈R, the binary value of y is x=L -1 It is defined as (y) = I{y<0}, x∈{0, 1}, where I{z} is the indicator function, which returns 1 if z is true and 0 otherwise.

[0055] Definition 3: A binary vector x of length n, x∈{0,1} n For x, the soft vector represented by L(x) is y=L(x)=1.0-2.0·x, y∈{+1.0, -1.0} n It is defined as:

[0056] Definition 4: A real-valued vector y of length n, y∈R n Let y be a binary vector, x=L -1 (y)=[L -1 (y0) L -1 (y1) … L -1 (y n-1 )] T =[I{y0<0}I{y1<0} … I{y n-1 <0}] T =I{y<0}, where I{z} is the component-by-component indicator function z i , for i=0,1,…,||z||-1, z i Returns 1 if is true, 0 otherwise.

[0057] Definition 1 simply maps {0,1} to {+1.0,-1.0}, where R + is a positive real number, R - is a negative real number, and definition 2 is {R + ∪{0},R -} to {0,1}. Note that the mapping to 0 and 1 is arbitrary, but according to definition 2, y=0 is mapped to binary 0. Definitions 3 and 4 are vector versions of definitions 1 and 2, respectively. Next, it is desirable to introduce Lemma 1 for algebraic operations on GF(2) and R as follows.

[0058] Lemma 1: a0, a1, …, a N-1 For ∈{0, 1},

number

[0059] Proof: See Appendix A.

[0060] Lemma 2: Binary vectors a0, a1, …, a N-1 ∈{0, 1} n About

number

number

[0061] Proof: The proof is similar to that in Appendix A.

[0062] Lemma 1 states that binary data a i a in GF(2), for i = 0, 1, ..., N-1 i The soft value of the addition of a i The soft value L(a i). Lemma 2 is the vector version of Lemma 1.

[0063] Using definition 3, r k r ~ k By substituting , equation (13) can be transformed into a soft estimate as follows:

number

[0064] where X k is the L × l matrix as defined in equation (13).

number

number

number

[0065] where x k,i =[x k,i,0 … x k,i,l-1 ] is X k is the i-th row vector of. The equality in (17) and (19) is based on Lemma 1. To simplify (19), it is desirable to introduce an equation for the soft value of the AND function of two binary bits as Lemma 3.

[0066] Lemma 3: For x, y ∈ {0, 1}, the soft value of x multiplied by y in GF(2), in other words, the soft value of the conjunction of x and y, is

number

number

[0067] Proof: See Appendix B.

[0068] Applying Lemma 3 to equation (19), we get

number

[0069] Where:

number

number

[0070] At block 516, the method includes soft combining N iterations of the soft value of the i-th bit of the seed using equation (21). Finally, the scrambling seed estimate s0 2 The i-th bit of L, i=0,1,...,L-1, is obtained as:

number

[0071] Where:

number

[0072] IV. Blind Scrambling Seed Detection Without Coding Knowledge

[0073] This section describes a method for blind scrambling seed detection that exploits the underlying repetitive structure in the received data without prior knowledge of the channel coding scheme. Figure 6 shows a method for blind detection of scrambling seeds without knowledge of the coding scheme according to one embodiment of the present invention. The method starts at block 602, and in this embodiment, both noiseless and noisy channel conditions are considered. In block 604, a rate-matched codeword d k is expressed from equation (11),

number

[0074] It is desirable to look at the noiseless case first. Adding two of these equations in GF(2), for example, k=u, k=v, u≠v, gives us, based on equation (10),

number

number

[0075] Where:

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[0076] Similar to equation (7), equation (25) states that r ~ k =r k , i.e., it can only be used if all the scrambled bits are received correctly. ~ k ≠r k (eg, a noisy channel), equation (23) becomes:

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[0077] r ~ u or r ~ v If any bit of is received in error, then k=u, k=v, u≠v and

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[0078] In the noisy case, it is desirable to soft combine all detected seeds from each pair of rate matching iterations using the received scrambled data, as shown in block 608. As in the derivation in Section III, by equation (25), s 2 The soft value of the estimate is:

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[0079] Where:

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[0080] where y uv,i =[y uv,i,0 …y uv,i,l-1 ] T is Y uv is the i-th row vector of

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[0081] In block 610, it is desirable to select pairs of iterations for soft combining. There are many ways to select pairs of iterations for soft combining. In equation (33), let v=u+1 and L(r ~ k,j ) to (r ~ k,j ) llr To,r ~ k,j L -1 ((r ~ k,j ) llr ) and N=N r - Take the sum over one iteration and s0 2We obtain the i-th, i=0,1,...L-1 soft combining estimate of as follows:

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[0082] Where:

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[0083] Scrambling seed estimation using soft combining is ~ 0 2 =[s ~ 0...s ~ L-1 ] T The method 600 ends at block 614.

[0084] V. Simulation Results

[0085] In this section, we evaluate the performance of the described blind scrambling seed detection method using Monte Carlo simulations with 10,000 iterations for each setting. We compare the performance of the described method with that of the traditional approach without combining specified in [3] and the hard combining approach, assuming first knowledge of the encoding and then no knowledge of the encoding.

[0086] A. Simulation environment settings

[0087] In each iteration, message bits of length 20 are randomly generated and encoded by a tail-biting convolutional code. Then, a 60-bit codeword is generated, which is 720 bits (Nr =12) or 1440 bits (N r =24). These rate-matched bits are then rate-matched to the seed s0 2 The QPSK symbols are then scrambled with and mapped to QPSK symbols by modulation mapping. The convolutional coding and rate matching follow the procedure specified in [2]. The scrambling and modulation mapping follow [1]. The QPSK symbols are then placed in equally spaced OFDM resources modulating subcarriers at specific positions of an OFDM symbol. The remaining subcarriers are modulated by random data symbols. This placement of QPSK symbols in the OFDM resources is similar to the way in which control information for a specific consumer drone is placed in resource elements (REs) of 10 MHz and 20 MHz channel bandwidths. The OFDM modulator then converts the guard-banded frequency-domain symbols to time-domain samples by an Inverse Fast Fourier Transform (IFFT) of size 1024, adds a Cyclic Prefix (CP), and distributes the OFDM signal from the TX with variance σ. 2 The signal received at RX is first synchronized and demodulated by the OFDM demodulator. The QPSK symbol carrying the message is extracted from a specific subcarrier position of a certain OFDM symbol. After modulation demapping, the scrambled data (r ~ The LLR values ​​of llr are fed to a blind seed detector for detection. ~ 0 2 However, the randomly selected s0 in TX 2 If m is equal to , then the detection is successful, which means that the recovered message m after descrambling using the detected seed, de-rate matching, and decoding is ~ It can be verified by:

[0088] B. Hard compositing vs. soft compositing

[0089] For performance comparison, the soft combining approach is compared with hard combining in the context of coding knowledge. ~ 0,k 2 The i-th bit of i=0,1,…L-1 of ~ k,i Let N be chosen to be odd and using definitions 1 and 2, the hard combining of bit i of the detected seed is given by

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[0090] Also, the hard synthetic seed is s ~ 0,HC 2 =[s ~ 0s ~ 1…s ~ L-1 ] T It becomes. where Equation (36) essentially states that s ~ 0,k 2 In ~ k,i Here, for N odd, j∈{0,1}, there are at least ((N+1) / 2)s ~ k,i If j, then j. Figure 7 is a graph comparing soft combining, hard combining, and blind seed detection without combining, with knowledge of the encoding scheme. More specifically, Figure 7 shows the r = 12, 24), we show the scrambling seed detection probability vs. SNR value for the performance comparison of soft combining, hard combining, and no combining approaches with coding knowledge. Here, (G ~ k F k 2 ) T G ~ k F k 2 The minimum value l that can be inverted is 68, and (N=N r−1). As shown in FIG. 7, the performance of the hard combining scheme is inferior in the low SNR region and only slightly superior in the high SNR region compared to that of the soft combining scheme. The described soft combining approach achieves a performance gain of about 2 dB in the low SNR region compared to the hard combining approach. Furthermore, the described soft combining approach achieves a gain of about 1.0 to 1.5 dB across the low to high SNR regions compared to no combining for the same detection performance. This demonstrates the effectiveness of the scramble seed detection method described above. As also shown in FIG. 7, the performance of the hard combining scheme is improved by increasing the number of iterations N r Increasing N by a factor of 12 to 24 does not result in significant improvement in SNR gain. r = 12) is a good trade-off between detection performance and computational complexity.

[0091] C. With coding knowledge vs. without coding knowledge

[0092] Next, we compare the performance of soft combining with and without coding knowledge. Figure 8 is a graph comparing soft combining with blind seed detection without coding knowledge and with and without coding scheme knowledge. More specifically, Figure 8 shows the scrambled seed detection probability versus SNR value in a performance comparison of soft combining with coding knowledge, soft combining without coding knowledge, and no combining with and without coding knowledge. N r12, 24 as shown in the figure. As shown in the figure, the described soft-combining method with coding knowledge generally achieves an SNR gain of 1.0-1.2 dB over the described soft-combining approach without such knowledge for the same detection performance. Even without combining, coding knowledge improves the SNR gain by 0.3-0.7 dB. This is the expected result because coding knowledge is utilized to negate the effect of coding in the null subspace, which reveals the effect of unknown scrambling conditions for seed detection. Without such knowledge, it can only respond to cancellation of the descrambled data by two iterative additions in GF(2). In general, cancellation is not perfect for the received noisy scrambled data, resulting in a degradation in performance compared to the case with coding knowledge. Nevertheless, without coding knowledge, soft-combining achieves an SNR gain of 0.5-1.0 dB over the non-combining case. Also, the performance of soft-combining without coding knowledge is comparable to that of non-combining with coding knowledge. Thus, the described soft combining method compensates for the performance loss due to the lack of coding knowledge.

[0093] As described in this disclosure, a blind scrambling seed detection method is described utilizing soft combining of a repetitive rate matching structure in received noisy scrambled data for a counter UAS system to detect and decode data transmitted by an LTE-based drone. The described method can be used with or without knowledge of the coding scheme. With knowledge of the coding scheme, the described method with soft combining achieves SNR gain in low SNR regions across hard combining and no combining. Even without coding knowledge, the described soft combining method compensates for the performance loss due to lack of coding knowledge compared to no combining with coding knowledge. Thus, the described method significantly improves performance for blind scrambling seed detection in noisy channels.

[0094] 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 that can be accessed by a computer or processor. By way of example and not limitation, such media 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 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 / executable by a computing device or processor.

[0095] The methods disclosed herein include one or more steps or actions for achieving the method described. The steps and / or actions of the method may be interchanged 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 method described, the order and / or use of specific steps and / or actions may be modified without departing from the scope of the claims.

[0096] As used herein, the term "plurality" refers to two or more. For example, a plurality of components is intended to refer to two or more components. The term "determining" encompasses a wide variety of actions, and thus "determining" includes calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, database, or another data structure), ascertaining, and the like. "Determining" can also include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory), and the like. "Determining" can also include resolving, selecting, choosing, establishing, and the like.

[0097] The phrase "based on" does not mean "based only on," unless otherwise specified. In other words, the phrase "based on" refers to both "based only on" and "based at least on."

[0098] The various exemplary logic blocks and modules described in connection with the embodiments disclosed herein, e.g., the functional block diagrams of Figures 1-5 above, may be implemented or performed by machines such as a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but alternatively, the processor may be a controller, a microcontroller, or a state machine, combinations thereof, and the like. A processor may include electrical circuitry configured to process computer-executable instructions. In another embodiment, a processor includes an FPGA or other programmable device that performs logical operations without processing computer-executable instructions. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Although described herein primarily with respect to digital technology, a processor may also include primarily analog components. For example, some or all of the signal processing algorithms described herein may be implemented in analog circuitry or mixed analog and digital circuitry. The computing environment may include any type of computer system, including, but not limited to, computer systems based on computational engines within microprocessors, mainframe computers, digital signal processors, portable computing devices, device controllers, or appliances, to name a few.

[0099] Elements of the methods, processes, or algorithms described in connection with the embodiments disclosed herein may be embodied directly in hardware, in software modules stored in one or more memory devices and executed by one or more processors, or in a combination of the two. The software modules may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of non-transitory computer readable storage medium, medium, or physical computer storage device known in the art. An exemplary storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium. Alternatively, the storage medium may be integral to the processor. The storage medium may be volatile or non-volatile. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Alternatively, the processor and the storage medium may reside as discrete components in a user terminal.

[0100] Unless otherwise noted, conditional language such as "can," "may," "can be," "may be," and "may be" are otherwise understood within the context in which they are generally used to convey that a particular embodiment includes certain features, elements, and / or blocks, while other embodiments may not include those features, elements, and / or blocks. Thus, such conditional language does not generally imply that the features, elements, and / or blocks are in any way required by one or more embodiments, or that one or more embodiments include or implement those features, elements, and / or blocks in any particular embodiment, with or without user input or prompting.

[0101] Branching language, such as the phrase "at least one of X, Y, or Z," is understood together with the context of general usage, unless otherwise noted, to indicate that an item, term, etc. can be X, Y, or Z, or any combination thereof (e.g., X, Y, and / or Z). Thus, such disjunction language is generally not intended and should not imply that a particular embodiment requires at least one of X, at least one of Y, or at least one of Z, respectively, to exist.

[0102] Any process descriptions, elements, or blocks in the flow diagrams described herein and / or shown in the accompanying figures should be understood as potentially representing modules, segments, or portions of code that include one or more executable instructions for implementing a particular logical function or element in the process. As will be appreciated by those skilled in the art, alternative implementations are included within the scope of the embodiments described herein that may omit elements or functions, perform or discuss in a different order than that shown, including substantially simultaneously or in reverse order, depending on the functionality involved.

[0103] Unless otherwise noted, articles such as "a" or "an" should generally be construed to include one or more of the described items. Thus, a phrase such as "a device configured" is intended to include one or more of the listed devices. Also, such one or more listed apparatuses may be collectively configured to execute the recited enumeration. For example, "a processor configured to execute instructions A, B, and C" may include a first processor configured to execute instruction A in cooperation with a second processor configured to execute instructions B and C.

[0104] It should be emphasized that many variations and modifications can be made to the above-described embodiments, elements of which are to be understood as being among other acceptable examples, and all such modifications and variations are intended to be included within the scope of this specification and protected by the following.

[0105] APPENDIX A

[0106] Proof of Lemma 1

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[0107] Appendix B

[0108] Proof of Lemma 3 Let z = x y ∈ {0,1}. From the multiplication table or logical product truth table of GF(2), z = 1 if and only if both x and y are 1. Similarly, using definition 1, L(z) = -1.0 if and only if L(x) = -1.0 and L(y) = -1.0. Here, when expressing L(z) based on L(y), there are the following three cases.

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Claims

1. 1. A system for detecting communications with a drone, comprising: a radio frequency (RF) receiver configured to receive an RF signal communicated between the drone and a controller, the RF signal including scrambled data r based on a known scrambler having an unknown scrambling seed s; a hardware processor; in communication with the hardware processor, deriving a predetermined matrix based at least in part on a state transition matrix F of the known scrambler; determining the unknown scrambling seed s based on a function including the predetermined matrix, a segment of a scrambler output sequence f with a known initial state, and a segment of the scrambled data r; a memory device having computer-executable instructions stored thereon for causing the processing; A system including:

2. 2. The system of claim 1, wherein the RF receiver is configured to receive the RF signal having OFDM modulated data and a known channel code with a sufficiently high signal-to-noise ratio (SNR) such that all scrambled bits, including at least one repetition of unscrambled data, are received over the channel.

3. 2. The system of claim 1, wherein the instructions further cause the hardware processor to determine a minimum number of data bits required in each segment of the received scrambled data r for data combining to determine the unknown scrambling seed s using a channel encoder state transition matrix C and a rate matcher state transition matrix R, and to find a null subspace matrix of matrix R and a Moore-Penrose inverse of the predetermined matrix.

4. 2. The system of claim 1, wherein the instructions further cause the hardware processor to determine the unknown scrambling seed s based on the scrambling data r.

5. The system of claim 1 , wherein the RF receiver is configured to receive the scrambled data r as modulation symbols and to obtain real log-likelihood ratio (LLR) values ​​from modulation demapping.

6. 6. The system of claim 5, wherein the instructions cause the hardware processor to derive a seed estimate for the LLR values ​​for each iteration and to soft-combine the seed estimates for all iterations.

7. 2. The system of claim 1, wherein the instructions cause the hardware processor to decode and identify a drone using the detected scrambling seed s.

8. 2. The system of claim 1, wherein the instructions cause the hardware processor to determine a minimum number of data bits required in each segment of the scrambled data, r, for data combining, and to determine the unknown scrambling seed, s, by determining the predetermined matrix based on an addition of two sub-matrices of the state transition matrix, F, in a Galois field, GF(2), of two elements, and determining a Moore-Penrose inverse of the predetermined matrix.

9. The instructions cause the hardware processor to select different segments of the scrambled data r that include repetitions of unscrambled data based on: The system of claim 1 , further comprising obtaining a soft combining estimate of the unknown scrambling seed s.

10. 2. The system of claim 1, wherein the function used to determine the unknown scrambling seed s further comprises a scrambler output sequence f of the state transition matrix F based on the known scrambling seed.

11. 1. A method for detecting communications with a drone, comprising: receiving a radio frequency (RF) signal transmitted between the drone and a controller, the RF signal including scrambled data r based on a known scrambler and an unknown scrambling seed s; a hardware processor for deriving a predetermined matrix based at least in part on a state transition matrix F of the known scrambler; the hardware processor determines an unknown scrambling seed s based on a function including the predetermined matrix, a segment of a scrambler output sequence f with a known initial state, and a segment of the scrambled data r; method.

12. 12. The method of claim 11, wherein the RF signal having OFDM modulated data and a known channel code is received with a sufficiently high signal-to-noise ratio (SNR) such that all scrambled bits, including at least one repetition of unscrambled data, are received over the channel.

13. 12. The method of claim 11, further comprising: determining a minimum number of data bits required in each segment of the received scrambled data r for data combining using a channel encoder state transition matrix C and a rate matcher state transition matrix R; determining an unknown scrambling seed s; and finding a null subspace matrix of matrix R and a Moore-Penrose inverse of the predetermined matrix.

14. The method of claim 11 , further comprising determining the unknown scrambling seed s based on the scrambling data r.

15. The method of claim 11, wherein receiving the scrambled data r is accomplished as modulated symbols, and modulation demapping results in real log-likelihood ratio (LLR) values.

16. The method of claim 15 , further comprising: deriving a seed estimate for the LLR values ​​for each iteration; and soft-combining the seed estimates for all iterations.

17. The method of claim 11 , further comprising: using the detected scrambling seed s to decode and identify the drone.

18. 12. The method of claim 11, comprising receiving the RF signal having OFDM modulated data and an unknown channel code with a sufficiently high signal-to-noise ratio (SNR) such that all scrambled bits, including at least two repetitions of unscrambled data, are received over the channel.

19. 13. The method of claim 12, further comprising: determining a minimum number of data bits required for each segment of the scrambled data r for data combination; and determining the predetermined matrix based on an addition of two submatrices of a state transition matrix F in a two element Galois field GF(2) and a Moore-Penrose inverse of the predetermined matrix.

20. 13. The method of claim 12, comprising obtaining a soft combining estimate of the unknown scrambling seed s based on selecting different segments of the scrambled data r that include repetitions of unscrambled data.

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