A low-altitude unmanned aerial vehicle identification and situation estimation method based on a spectrum signal

Abstract

The application discloses a low-altitude unmanned aerial vehicle identification and situation estimation method based on a spectrum signal, and comprises the following steps: S1, searching for a spectrum signal through a spectrum monitoring device, and measuring the spectrum signal to obtain a plurality of parameters of a measurement signal K; S2, pre-processing the measurement signal K to filter and extract a suspected unmanned aerial vehicle signal; S3, adopting a queue algorithm to judge the suspected unmanned aerial vehicle signal to determine whether it is an unmanned aerial vehicle, if yes, subsequent steps are performed, and if not, the step S1 is returned; S4, according to a space transmission algorithm, the transmission distance of the unmanned aerial vehicle signal is estimated; S5, according to the transmission distance of the unmanned aerial vehicle signal and the position of the spectrum monitoring device, the communication frequency band and the position coordinates of the unmanned aerial vehicle are calculated. The application can avoid the confusion of customized identification algorithms for different unmanned aerial vehicles, reduce the technical requirements and deployment requirements of hardware devices, greatly reduce the cost, and improve the identification accuracy.

Description

Technical Field

[0001] This invention relates to the field of low-altitude unmanned aerial vehicle (UAV) monitoring technology, and in particular to a method for low-altitude UAV identification and situation estimation based on spectrum signals. Background Technology

[0002] There are many brands and models of low-altitude drones, and their flight control and communication systems are complex. Conventional drone monitoring and identification systems require establishing a feature database for each drone before identification can be performed. Furthermore, most of the drones targeted for defense are modified for unauthorized flights, lacking RID modules. However, in reality, it is difficult to collect and establish a complete feature database for all drones. If a drone is not in the database, it may be missed, resulting in low accuracy. Currently, only the drone's orientation is determined, but its distance and position are not estimated, making it difficult to determine the drone's real-time location and implement jamming and defense measures. This hinders timely detection and defense of low-altitude drones in sensitive areas.

[0003] Therefore, there is a need for a method that can identify and determine whether an aerial target is a drone (without relying on a signal feature database) and predict its position and situation. Summary of the Invention

[0004] This invention provides a method for low-altitude UAV identification and situation estimation based on spectrum signals, in order to solve the technical problem of how to improve the accuracy of low-altitude UAV identification.

[0005] This invention is achieved using the following technical solution: a method for low-altitude UAV identification and situation estimation based on spectrum signals, comprising the following steps: S1: Search for spectrum signals using spectrum monitoring equipment and measure the spectrum signals to obtain multiple parameters of the measured signal K; S2: Preprocess the measurement signal K to filter and extract suspected drone signals; S3: Use a queue algorithm to judge the suspected drone signal to determine whether it is a drone. If it is, proceed to the next step; otherwise, return to step S1. S4: Estimate the transmission distance of the UAV signal based on the spatial transmission algorithm; S5; Calculate the communication frequency band and location coordinates of the UAV based on the transmission distance of the UAV signal and the location of the spectrum monitoring equipment.

[0006] Furthermore, the measurement signal K includes multiple parameters such as frequency F, bandwidth B, power P, duration DR, azimuth DF, and detection time T.

[0007] Furthermore, step S2 includes the following steps: S21: Perform coarse filtering on the measurement signal K to obtain suspected UAV signals; S22: Filter the bandwidth B and duration DR of the measured signal K respectively, and determine the filtered signal frequency F as the suspected UAV signal frequency set Fn and the parameters of each frequency Fn: bandwidth Bnm, power Pnm, duration DRnm, azimuth DFnm and detection time Tnm.

[0008] Furthermore, the coarse filtering includes: Bandwidth filtering parameter group: BWmin1-BWmax1, BWmin2-BWmax2, ..., BWminN-BWmaxN; Duration filtering parameter group: DRmin1-DRmax1, DRmin2-DRmax2, ..., DRminN-DRmaxN; Where n = 1, 2, 3...N, represents the number of parameter groups.

[0009] Furthermore, step S3 includes the following steps: S31: Establish a FIFO buffer for different suspected UAV signals Fn, and set the amplitude change threshold to Pmin; S32: Add the parameters of the suspected drone signal Fn: bandwidth Bnm, power Pnm, duration DRnm, and detection time Tnm to the FIFO buffer queue; S33: Calculate the change of amplitude Pnm of Fn relative to P1 at time Tnm, ΔPn = Pnm – Pn1, and further determine whether it is a drone signal based on ΔPn.

[0010] Furthermore, step S33 includes the following sub-steps: S331: Determine if the F1 signal is a drone signal; S332: Following the judgment steps of signal frequency F1, judge signal frequencies F2……Fn in sequence to obtain the suspected UAV frequency set Gk, where k = 1, 2, 3,……K.

[0011] Furthermore, step S331 includes the following sub-steps: S3311: Calculate the amplitude difference between the power P12 at time T12 and the power P11 at time T11 of signal frequency F1, ΔP = P2 - P1. If ΔP exceeds Pmin, it is judged as a drone; if ΔP is less than Pmin, continue to the next time T13 and T11 to check if the signal amplitude change meets the requirements. S3312: Determine the time T13...T1m of the signal frequency F1 in sequence according to the judgment steps of the signal frequency F1, where m=3, 4...M, and m represents the measurement time of each frequency Fn; S3313: If the signal frequency F1 is not satisfied after all the data in the FIFO has been evaluated, it is considered that F1 is not a drone signal and is deleted from the buffer L.

[0012] Furthermore, step S4 includes the following steps: S41: Calculate the corresponding distance based on the signal propagation formula in space: ; Where Att represents the power attenuation of the suspected UAV signal Gn over a distance Ln in free space; S42: Estimate the distance from the drone to the spectrum monitoring equipment, and the distance of the drone signal Gn: Pnm = Power – Att + GFn + Gr; Att = Power + GFn + Gr – Pnm; = Power + GFn + Gr – Pnm – ( ); Where Pnm is the received UAV signal strength, Ln is the signal transmission distance, Power is the radiated power of the communication signal of the low-altitude UAV, GFn is the antenna link gain at signal Gn, and Gr is the receiver link gain.

[0013] Furthermore, the method for calculating the position coordinates of the UAV is as follows: Xw1 = Xs + Ln * COS(DR1); Yw1 = Ys + Ln * SIN (DR1); Where Xw1 is the abscissa of the UAV corresponding to the distance L1 estimated by the UAV signal F1; Yw1 is the ordinate of the UAV corresponding to the distance L1 estimated by the UAV signal F1; Xs is the abscissa of the spectrum monitoring equipment; Ys is the ordinate of the spectrum monitoring equipment; and DR1 is the azimuth angle of the UAV signal F1.

[0014] Furthermore, following the steps for determining the UAV signal frequency F1, the signal frequencies F2...Fn are determined sequentially to obtain the UAV position coordinates (Xwn, Ywn) set, where n = 1, 2, 3, ... N.

[0015] The beneficial effects of this invention are as follows: This invention uses the amplitude motion variation characteristics of low-altitude UAV signals (except when the UAV is stationary) to match and identify multiple types of UAVs, avoiding the confusion of previous UAV feature database identification algorithms. It meets the need for not specifically identifying UAV models, reduces R&D and hardware investment, and is particularly suitable for scenarios requiring rapid defense against low-altitude UAVs in sensitive areas.

[0016] This invention can be adapted to the detection of different drones, avoids the confusion of customized recognition algorithms for different drones, reduces the technical and deployment requirements of hardware equipment, significantly reduces costs, and improves recognition accuracy. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0021] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0022] See Figure 1 A method for UAV identification and situation estimation based on spectrum signals includes the following steps: S1: Search for spectrum signals using spectrum monitoring equipment and measure the spectrum signals to obtain multiple parameters of the measured signal K; S2: Preprocess the measurement signal K to filter and extract suspected drone signals; S3: Use a queue algorithm to judge the suspected drone signal to determine whether it is a drone. If it is, proceed to the next step; otherwise, return to step S1. S4: Estimate the transmission distance of the UAV signal based on the spatial transmission algorithm; S5; Calculate the communication frequency band and location coordinates of the UAV based on the transmission distance of the UAV signal and the location of the spectrum monitoring equipment.

[0023] In this embodiment, step S1 includes the following sub-steps: S11: The spectrum monitoring equipment has completed receiving the air signal K through the antenna (gain G) and measuring the signal to obtain basic measurement parameters; S12: Parameters of the measured signal K; The parameters of the signal K include frequency F, bandwidth B, power P, duration DR, azimuth DF, and detection time T.

[0024] In this embodiment, step S2 includes the following sub-steps: S21: Perform coarse filtering on the measured signal K to obtain suspected UAV signals. Bandwidth filtering parameter group: BWmin1-BWmax1, BWmin2-BWmax2, ..., BWminN-BWmaxN; Duration filtering parameter group: DRmin1-DRmax1, DRmin2-DRmax2, ..., DRminN-DRmaxN, where n=1, 2, 3...N, representing the number of parameter groups; S22: Based on the bandwidth filtering parameter group BW and the duration filtering parameter group DR, the bandwidth B and duration DR parameters of the measured signal K are filtered. The filtered signal frequency F is determined as the suspected UAV signal frequency set Fn and the parameters of each frequency Fn are: bandwidth Bnm, power Pnm, duration DRnm, azimuth DFnm, and detection time Tnm. Where n = 1, 2, 3...N, n represents the frequency set; m = 1, 2, 3...M, m represents the number of measurements for each frequency Fn.

[0025] In this embodiment, step S3 includes the following sub-steps: S31: Establish FIFO buffers for different suspected UAV signals Fn. The length L of the buffer is configurable, and the amplitude change threshold is set to Pmin. S32: Add the parameters of the suspected drone signal Fn from S21: bandwidth Bnm, power Pnm, duration DRnm, and detection time Tnm to the FIFO buffer queue.

[0026] S33: Calculate the change of the amplitude Pnm of Fn relative to P1 at time Tnm, ΔPn = Pnm – Pn1, and make further judgments based on ΔPn.

[0027] Specifically, step S33 includes the following sub-steps: S331: Determine if the F1 signal is a drone signal. S3311: Calculate the amplitude difference between the power P12 at time T12 and P11 at time T11 of signal frequency F1, ΔP = P2 - P1. If ΔP exceeds Pmin, it is determined to be a drone, and signal frequency F1 is recorded as a further suspected drone signal frequency G1. Then, continue to judge the next signal frequency F2. If ΔP is less than Pmin, continue to the next time T13 and T11 to check if the signal amplitude change meets the requirements.

[0028] S3312: According to the judgment steps of time T12 of signal frequency F1, judge time T13...T1m of signal frequency F1 in sequence, m=3, 4...M, where m represents the measurement time of each frequency Fn; S3313: If the signal frequency F1 is not satisfied after all the data in the FIFO has been evaluated, it is considered that F1 is not a drone signal and is deleted from the buffer L.

[0029] S332: Following the judgment steps of signal frequency F1, judge signal frequencies F2……Fn in sequence to obtain a further set of suspected UAV frequencies Gk, where k = 1, 2, 3,……K.

[0030] In this embodiment, S4 includes the following sub-steps: S41: Calculate the corresponding distance based on the signal propagation formula in space: ; Where Att represents the power attenuation of the suspected UAV signal Gn over a distance Ln in free space; S42: Let Power be the typical radiated power of the communication signal of a low-altitude UAV, GFn be the gain of the antenna link at signal Gn, Gr be the gain of the receiver link, and the cable and connector losses be very small and negligible.

[0031] S43: Estimate the distance from the drone to the spectrum monitoring device, and the distance of the drone signal Gn: Pnm = Power – Att + GFn + Gr; Att = Power + GFn + Gr – Pnm; = Power + GFn + Gr – Pnm – ( ); Where Pnm is the received drone signal strength; the signal transmission distance Ln can be calculated based on the above information.

[0032] In this embodiment, step S5 includes the following sub-steps: S51: Let the position coordinates of the monitoring device be (Xs, Ys). Estimate the position coordinates (Xw1, Yw1) of the corresponding UAV based on the distance L1 corresponding to the UAV signal F1. Xw1 = Xs + Ln * COS(DR1); Yw1= Ys + Ln * SIN (DR1); Wherein, DR1 is the orientation angle of the UAV signal F1; S52: Following the judgment steps of UAV signal frequency F1, judge signal frequencies F2...Fn in sequence to obtain a further set of UAV position coordinates (Xwn, Ywn), where n = 1, 2, 3, ... N; S53: Based on the above recognition algorithm and distance estimation, further obtain the UAV's communication signal Fn, distance Ln, and position coordinates (Xwn, Ywn).

[0033] This invention uses the amplitude motion variation characteristics of low-altitude UAV signals (except when the UAV is stationary) to match and identify multiple types of UAVs, avoiding the confusion of previous UAV feature database identification algorithms. It meets the need for not specifically identifying UAV models, reduces R&D and hardware investment, and is particularly suitable for scenarios requiring rapid defense against low-altitude UAVs in sensitive areas.

[0034] For the foregoing embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to this application.

[0035] The above embodiments describe the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Modifications and variations made by those skilled in the art without departing from the spirit and scope of the invention should be within the protection scope of the appended claims.

Claims

1. A method for low-altitude unmanned aerial vehicle (UAV) identification and situation estimation based on spectrum signals, characterized in that, Includes the following steps: S1: Search for spectrum signals using spectrum monitoring equipment and measure the spectrum signals to obtain multiple parameters of the measured signal K; S2: Preprocess the measurement signal K to filter and extract suspected drone signals; S3: Use a queue algorithm to judge the suspected drone signal to determine whether it is a drone. If it is, proceed to the next step; otherwise, return to step S1. S4: Estimate the transmission distance of the UAV signal based on the spatial transmission algorithm; S5; Calculate the communication frequency band and location coordinates of the UAV based on the transmission distance of the UAV signal and the location of the spectrum monitoring equipment; Step S3 includes the following steps: S31: Establish a FIFO buffer for different suspected UAV signals Fn, and set the amplitude change threshold to Pmin; S32: Add the parameters of the suspected drone signal Fn: bandwidth Bnm, power Pnm, duration DRnm, and detection time Tnm to the FIFO buffer queue; S33: Calculate the change of power Pnm relative to Pn1 at time Tnm of Fn, ΔPn = Pnm – Pn1, and further determine whether it is a drone signal based on ΔPn. Step S33 includes the following sub-steps: S331: Determine if the F1 signal is a drone signal; S332: Following the judgment steps of signal frequency F1, judge signal frequencies F2……Fn in sequence to obtain the suspected UAV frequency set Gk, where k = 1, 2, 3,……K; Step S331 includes the following sub-steps: S3311: Calculate the amplitude difference between the power P12 at time T12 and the power P11 at time T11 of signal frequency F1, ΔP = P12 – P11. If ΔP exceeds Pmin, it is judged as a drone; if ΔP is less than Pmin, continue to the next time T13 and T11 to check if the signal amplitude change meets the requirements. S3312: Determine the time T13...T1m of the signal frequency F1 in sequence according to the judgment steps of the signal frequency F1, where m=3, 4...M, and m represents the measurement time of each frequency Fn; S3313: If the signal frequency F1 is not satisfied after all the data in the FIFO has been evaluated, it is considered that F1 is not a drone signal and is deleted from the buffer L.

2. The method for low-altitude UAV identification and situation estimation based on spectrum signals as described in claim 1, characterized in that, The measurement signal K has multiple parameters including: frequency F, bandwidth B, power P, duration DR, azimuth DF, and detection time T, among one or more of these.

3. The method for low-altitude UAV identification and situation estimation based on spectrum signals as described in claim 1, characterized in that, Step S2 includes the following steps: S21: Perform coarse filtering on the measurement signal K to obtain suspected UAV signals; S22: Filter the bandwidth B and duration DR of the measured signal K respectively, and determine the filtered signal frequency F as the suspected UAV signal frequency set Fn and the parameters of each frequency Fn: bandwidth Bnm, power Pnm, duration DRnm, azimuth DFnm and detection time Tnm.

4. The method for low-altitude UAV identification and situation estimation based on spectrum signals as described in claim 3, characterized in that, The coarse filtering includes: Bandwidth filtering parameter group: Bmin1-Bmax1, Bmin2-Bmax2, ..., BminN-BmaxN; Duration filtering parameter group: DRmin1-DRmax1, DRmin2-DRmax2, ..., DRminN-DRmaxN; Where n = 1, 2, 3...N, represents the number of parameter groups.

5. The method for low-altitude UAV identification and situation estimation based on spectrum signals as described in claim 1, characterized in that, Step S4 includes the following steps: S41: Calculate the corresponding distance based on the signal propagation formula in space: ； Where Att represents the power attenuation of the suspected UAV signal Gn over a distance Ln in free space; S42: Estimate the distance from the drone to the spectrum monitoring equipment, and the distance of the drone signal Gn: Pnm = Power – Att + GFn + Gr; Att = Power + GFn + Gr – Pnm; = Power + GFn + Gr – Pnm – ( ); Where Pnm is the power, i.e. the received UAV signal strength, Ln is the signal transmission distance, Power is the radiated power of the communication signal of the low-altitude UAV, GFn is the antenna link gain at signal Gn, and Gr is the receiver link gain.

6. The method for low-altitude UAV identification and situation estimation based on spectrum signals as described in claim 1, characterized in that, The method for calculating the position coordinates of the UAV is as follows: Xw1 = Xs + Ln * COS(DR1); Yw1 = Ys + Ln * SIN (DR1); Where Xw1 is the abscissa of the UAV corresponding to the distance L1 estimated by the UAV signal F1; Yw1 is the ordinate of the UAV corresponding to the distance L1 estimated by the UAV signal F1; Xs is the abscissa of the spectrum monitoring equipment; Ys is the ordinate of the spectrum monitoring equipment; and DR1 is the azimuth angle of the UAV signal F1.

7. The method for low-altitude UAV identification and situation estimation based on spectrum signals as described in claim 6, characterized in that, Following the steps for determining the UAV signal frequency F1, the signal frequencies F2...Fn are determined sequentially to further obtain the set of UAV position coordinates (Xwn, Ywn), where n = 1, 2, 3, ... N.

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