Low-altitude unmanned aerial vehicle real-time detection and positioning method and system

Abstract

The application discloses a low-altitude unmanned aerial vehicle real-time detection and positioning method and system and belongs to the unmanned aerial vehicle detection field. The application adopts a fiber distributed sound wave sensing technology, uses a high-sensitivity full-distributed sound wave sensing optical cable to construct a wide-range detection network, and realizes the detection of wide-range low-altitude unmanned aerial vehicle radiation sound wave noise. Distributed unmanned aerial vehicle signals obtained from the detection network can be further analyzed to obtain time differences of the unmanned aerial vehicle signals to various sensing channels. By dividing the sensing network into different sensing arrays and constructing different time delay functions according to the spatial distribution of the sensing arrays to fit the time differences of the unmanned aerial vehicle signals to the sensing arrays, the unmanned aerial vehicle spatial position estimation with the minimum error can be further obtained. Overall, the application can provide a stable and effective method for low-altitude unmanned aerial vehicle detection and positioning, and can effectively improve the detection range and positioning precision of the low-altitude unmanned aerial vehicle.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) detection, and more specifically, relates to a method and system for real-time detection and positioning of low-altitude UAVs. Background Technology

[0002] Unmanned aerial vehicles (UAVs) possess advantages such as light weight, small size, good concealment, reusability, and no human casualties, making them highly valued and vigorously developed by various countries. In recent years, with the development of UAV technology, low-altitude UAVs have been widely used, showing great promise in both military and civilian fields. However, when low-altitude UAVs are maliciously used by criminals or terrorists, the potential risks also increase. Therefore, improving the detection capabilities of low-altitude UAVs is of great significance for ensuring national low-altitude security.

[0003] Currently, radar detection technology is the conventional method for detecting drones. However, when targeting low-altitude drones, this technology is susceptible to environmental clutter and limited by the curvature of the Earth, resulting in a certain low-elevation blind zone. This restricts its detection effectiveness, meaning that drones flying behind large obstacles or within radar blind zones cannot be detected by radar detection systems. Furthermore, as an all-weather, high-power, and high-radiation active sensor, radar is difficult to deploy high-power radar systems in densely populated cities and regions.

[0004] In addition, photoelectric monitoring systems are also a common detection method. These systems are unaffected by electromagnetic interference and use visible light or infrared sensors to monitor drones at close range. They utilize static image information and differences between video frames to detect the presence of drone targets and further capture their movement. However, photoelectric monitoring systems cannot operate in all weather conditions. In low visibility and extreme weather conditions, such as rain, snow, fog, and haze, this technology is often ineffective in detecting drones. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a method and system for real-time detection and positioning of low-altitude UAVs. Its purpose is to provide a highly efficient passive acoustic detection and positioning method for low-altitude UAVs that is available over a wide area, in all weather conditions, and is not affected by terrain.

[0006] To achieve the above objectives, this invention provides a method for real-time detection and positioning of low-altitude unmanned aerial vehicles (UAVs) over a wide area based on fiber optic distributed acoustic sensing technology. First, a high-sensitivity acoustic sensing fiber optic cable is deployed in the land or sea area to be monitored, and then connected to the distributed acoustic sensing system using a connecting fiber optic cable. The deployed high-sensitivity acoustic sensing fiber optic cable is divided into several sensing arrays, each containing multiple continuous and uniform sensing channels.

[0007] Preferably, the high-sensitivity acoustic wave sensing optical cable includes single-mode optical cable, multi-mode optical cable, scattering-enhancing microstructure optical cable, and spiral-wound sensitizing optical cable, and the detection network deployment mode includes single-line type (including straight line and curve) and overlapping type (including overlapping at any angle).

[0008] Preferably, the sensor arrays divided by the high-sensitivity acoustic wave sensing optical cable do not overlap spatially (including continuous and discontinuous divisions), the length of the sensor channels divided by the sensor array is greater than or equal to the minimum spatial resolution that the fiber optic distributed acoustic wave sensing system can resolve, and the interval between adjacent sensor channels is greater than or equal to the minimum spatial sampling resolution of the fiber optic distributed acoustic wave sensing system.

[0009] Detection and positioning include the following steps:

[0010] S1. Acoustic wave signals of each sensing channel on each sensing array of the sensing optical cable are obtained using fiber optic distributed acoustic wave sensing technology.

[0011] S2. Denoise the acoustic signal acquired by each sensing channel on each sensing array, and divide each denoised signal into N continuous segments of equal time length. Where a is the sequence number of the sensor array, b a This represents the number of sensing channels in the sensing array;

[0012] Preferably, the noise reduction algorithm for the acoustic signal detected by each sensing channel includes filtering, empirical mode decomposition, wavelet decomposition, adaptive filtering, and machine learning.

[0013] Preferably, the time interval between each time segment signal is greater than or equal to the minimum time sampling interval of the optical fiber distributed acoustic wave sensing system for the acoustic wave signal.

[0014] S3. Take the nth time segment signal from each sensing channel on the a-th sensing array and construct the signal time delay matrix, which is as follows:

[0015] S31. The nth time segment signal of the acoustic wave signal detected by each sensor channel can be represented as: Find the average energy of the signal in the nth time segment on each sensing channel of the sensing array. Based on previous experience, an energy threshold E was set for the low-altitude UAV signal. T And the sensor channel segment signals with average energy greater than the energy threshold are identified as valid sensor channel segment signals. It is assumed that the number of valid sensor channel segment signals is... The segmented signal of the effective sensing channel of the sensor array can then be expressed as: Among them l m The sequence number of the sensor channel for the segmented signal of the m-th valid sensor channel;

[0016] Specifically, the energy threshold E T It must be greater than the noise energy, but lower than the maximum average energy. in This refers to the index of the sensor channel with the highest average energy among the segmented signals of the effective sensor channels on the sensor array, and the number of segmented signals of the effective sensor channels. Greater than or equal to 3.

[0017] S32. Select the signal with the largest average energy from the segmented signals of the effective sensing channel. Then, the time delay matrix between the segmented signals of other sensing channels and this signal is obtained sequentially. in

[0018] Preferably, the methods for determining the time delay between the signal and the other signals in the effective sensing channel segmented signal include the generalized cross-correlation method, the high-order cross-correlation method, the short-time energy delay estimation method, the fractional delay estimation method, and the adaptive filtering delay estimation method.

[0019] S33. The time delay matrix of the entire detection network can be represented as follows:

[0020] S4. Construct a theoretical time delay function based on the spatial matrix of the sensor array in the nth time segment, specifically as follows:

[0021] S41. Based on the spatial locations of the effective sensing channels determined in step S31, the spatial matrix of the sensing array for the nth time segment can be constructed as follows: in Among them l m This refers to the sensor channel number;

[0022] S42. Assume the spatial position of the UAV in the nth time segment is F(X). n ,Y n Z n If the spatial distance between the UAV in the nth time segment and each effective sensor channel on the ath sensor array can be expressed as: in For the UAV at time segment n and on the a-th sensor array, the l-th time segment... m The distance between each of the sensing channels (the m-th valid sensing channel), and Theoretically, within this time segment, the signal from the drone reaches the lth sensor on the a-th sensor array. m The sensor channel with the highest average signal energy. The time delay of each sensing channel can be calculated as follows: in in v sThe speed of sound wave propagation;

[0023] S43. That is, the time delay function of the entire detection network;

[0024] S5. Obtain the spatial location estimate of the UAV within this time segment, specifically as follows:

[0025] Using ΔT n Weighted fitting of time delay function Δt n The time delay matrix, and the weighting coefficients of the effective sensing channels on each sensing array within n time segments. It is negatively correlated with the average energy of its nth time segment, and the fitting error of the entire sensor network can be expressed as... When the fitting error is minimized, its corresponding position parameter (X) n ,Y n Z n This represents the optimal spatial position estimate of the UAV in the nth time segment.

[0026] Preferably, the weighting coefficients of each effective sensing channel are in the form of a negative correlation polynomial. Where i = 0, 1, 2, 3, ..., k i (≥0 and not all are 0) form.

[0027] S6. Further obtaining the spatial position estimate of the UAV in any time segment will enable real-time tracking of the low-altitude UAV position.

[0028] The present invention also provides a real-time detection and positioning system for low-altitude unmanned aerial vehicles, comprising: a computer-readable storage medium and a processor;

[0029] The computer-readable storage medium is used to store executable instructions;

[0030] The processor is used to read executable instructions stored in the computer-readable storage medium and execute the above-described real-time detection and positioning method for low-altitude unmanned aerial vehicles.

[0031] Compared with the prior art, the above-described technical solutions conceived in this invention can achieve the following beneficial effects.

[0032] (1) Compared with traditional low-altitude UAV detection and positioning technology, the solution provided by this invention adopts fiber optic distributed acoustic wave sensing technology to demodulate and recover the high-sensitivity acoustic wave sensing optical cable in a fully distributed manner, thereby realizing passive acoustic detection and positioning of low-altitude UAVs within the detection range. This system is not subject to electromagnetic interference, is easy to deploy in a large-scale fully distributed manner, and can realize the detection of low-altitude UAVs in a large-scale fully distributed manner.

[0033] (2) The low-altitude UAV detection and positioning scheme provided by the present invention can achieve all-weather detection, unaffected by weather and environment, and can achieve low-altitude UAV detection and positioning under conditions such as low visibility, extreme weather, and large obstacles.

[0034] (3) The low-altitude UAV detection and positioning scheme provided by the present invention adopts the fully distributed acoustic wave information on the high-sensitivity acoustic wave sensing optical cable, which contains more sensing units and their acoustic wave information. By dividing the sensing network into multiple sensing arrays, the positioning error of low-altitude UAV can be reduced and the positioning accuracy can be improved. Attached Figure Description

[0035] Figure 1 This is a flowchart of a real-time detection and positioning method for low-altitude unmanned aerial vehicles provided by the present invention;

[0036] Figure 2 This invention provides a common deployment method for a low-altitude unmanned aerial vehicle (UAV) real-time detection and positioning method in both land and sea applications.

[0037] Figure 3 This is a schematic diagram of a continuous partitioning method for a sensor array provided by the present invention;

[0038] Figure 4 This is a schematic diagram of the sensing channel division method for the sensing array provided by the present invention;

[0039] Figure 5 This is a schematic diagram of the acoustic signal segmentation method provided by the present invention;

[0040] Figure 6 This is a schematic diagram of a low-altitude UAV positioning method using a single sensor array in an embodiment of the present invention;

[0041] Figure 7 This is the positioning result of a low-altitude UAV using a single sensor array in an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0043] like Figure 1 As shown, the high-sensitivity acoustic wave sensing optical cable 4 is first deployed in the land or sea area 1 to be monitored, and then connected to the distributed acoustic wave sensing system 2 using the connecting optical cable 3.

[0044] Specifically, the high-sensitivity acoustic wave sensing optical cable includes single-mode optical cable, multi-mode optical cable, scattering-enhanced microstructure optical cable, and spiral-wound sensitized optical cable. The detection network deployment methods include single-line type (including straight line and curve) 4-1 and overlapping type (including overlapping at any angle) 4-2.

[0045] Furthermore, the low-altitude UAV detection and positioning method based on fiber optic distributed acoustic sensing technology provided by this invention is applicable to both marine and terrestrial environments. Specific deployment methods are as follows: Figure 2 As shown.

[0046] Specifically, Figure 3 This is a schematic diagram of a continuous partitioning method for a sensor array provided by the present invention, including single-line and overlapping types. In the embodiment provided by the present invention, the high-sensitivity acoustic wave sensing optical cable is a spirally wound sensitizing optical cable, wherein the length ratio of the optical fiber to the sensing optical cable is 5:1, and the optical cable is deployed in a single-line linear distribution, which can realize two-dimensional positioning of the low-altitude UAV 5.

[0047] The deployed high-sensitivity acoustic wave sensing optical cable 4 is divided into several sensing arrays 6. Each sensing array 6 contains multiple continuous and uniform sensing channels 7. Specifically, the sensing arrays 6 of the high-sensitivity acoustic wave sensing optical cable 4 do not overlap spatially (including continuous and non-continuous divisions). Figure 4 This is a schematic diagram of the sensing channel division method for the sensing array provided by the present invention, which includes three division methods: the length of sensing channel 7 is equal to the sensing channel interval 8, the length of sensing channel 7 is less than the sensing channel interval 8, and the length of sensing channel 7 is greater than the sensing channel interval 8.

[0048] Specifically, the length of the sensing channels 7 of the sensing array 6 is greater than or equal to the minimum spatial resolution that the fiber optic distributed acoustic wave sensing system 2 can resolve, and the spacing 8 between adjacent sensing channels is greater than or equal to the minimum spatial sampling resolution of the fiber optic distributed acoustic wave sensing system 2.

[0049] Specifically, in the embodiments provided by the present invention, the single-line linearly distributed high-sensitivity acoustic wave sensing optical cable 4 is divided into a sensing array.

[0050] Specifically, in the embodiments provided by the present invention, the minimum spatial resolution is 0.6m (determined by the probe light pulse of 30ns and the winding ratio of the optical fiber in the sensitizing optical cable of 5:1), the length of the divided sensing channel 7 is 1m, and the interval between adjacent sensing channels 8 is 1m.

[0051] Real-time detection and positioning of low-altitude unmanned aerial vehicles (UAVs) includes the following steps:

[0052] S1. Acoustic signals from each sensing channel 7 on each sensing array 6 of the sensing optical cable 4 are acquired using fiber optic distributed acoustic wave sensing technology.

[0053] Specifically, in the embodiments provided by the present invention, phase-sensitive optical time-domain reflectometry is used to acquire the acoustic wave signals of each sensing channel of the sensing optical cable.

[0054] S2. Denoise the acoustic signal acquired by each sensing channel 7 on each sensing array 6, and divide each denoised signal 9 into N continuous segmented signals with equal time lengths. Where a is the sequence number of sensor array 6, b a This represents the number of sensing channels 7 in the sensing array. Figure 5 This is a schematic diagram of the acoustic signal segmentation method provided by the present invention, which includes three segmentation methods: the length of segmented signal 10 is equal to the time interval 11, the length of segmented signal 10 is less than the time interval 11, and the length of segmented signal 10 is greater than the time interval 11.

[0055] Specifically, in the embodiments provided by the present invention, a bandpass filter of 20Hz to 100Hz is used to reduce the noise of the acoustic wave signal detected by each sensing channel, and further divides it into N continuous segmented signals 10 with a time length of 2s each, and the time interval 11 between the connected segmented signals is 2s, that is, the acoustic wave signal is continuously segmented into segments with a length of segmented signal 10 equal to the time interval 11.

[0056] S3. Take the nth time segment signal 10 of each sensing channel 7 on the a-th sensing array 6, and construct the signal time delay matrix 14, which is as follows:

[0057] S31. The nth time segment signal 10 of the acoustic wave signal detected by each sensing channel 7 can be expressed as: Find the average energy of the nth time segment signal 10 on each sensing channel 7 in the sensing array 6. Based on previous experience, an energy threshold E was set for the low-altitude UAV signal. T And the sensor channel segment signal 10 with an average energy greater than the energy threshold is identified as the effective sensor channel segment signal 15. Assume the number of effective sensor channel segment signals 15 is... The effective sensing channel segment signal 15 of the sensing array can then be represented as: Among them l m The sequence number of sensor channel 7 is the segment signal 15 of the m-th valid sensor channel.

[0058] Specifically, the energy threshold E T It must be greater than the noise energy, but lower than the maximum average energy. in The sequence number of the sensor channel 7 with the highest average energy among the effective sensor channel segment signals 15 on the sensor array 6, and the number of effective sensor channel segment signals 15. Greater than or equal to 3.

[0059] Specifically, in the embodiment provided by the present invention, the fourth time segment signal 10 of each sensing channel 7 is taken, and the number of effective sensing channel segment signals 15 can be obtained by setting the energy threshold according to previous experience, which is 15, from the first sensing channel to the 15th sensing channel.

[0060] S32. Select the signal with the largest average energy from the 15 segmented signals of the effective sensing channel. Then, the time delay matrix between the segmented signals of other sensing channels and this signal is obtained sequentially. in

[0061] Specifically, in the embodiment provided by the present invention, the sensing channel with the highest energy in the effective sensing channel segment signal 15 within the time segment is the 9th sensing channel, and the time delay between the remaining signals in the effective sensing channel segment signal 15 within the time segment and the signal is calculated sequentially using the generalized cross-correlation method.

[0062] S33. The time delay matrix 14 of the entire detection network can be represented as follows:

[0063] S4. Based on the spatial matrix of the sensor array 6 in the nth time segment 10, a theoretical time delay function 12 is constructed, which is as follows:

[0064] S41. Based on the spatial position of the effective sensing channel 7 determined in step S51, the spatial matrix of the sensing array in the nth time segment 10 can be constructed as follows: in Among them l m This is the sequence number of sensor channel 7.

[0065] S42. Assume that the spatial position of UAV 5 in time segment n, 10, is F(X). n ,Y n Z n If the spatial distance between the UAV in the nth time segment 10 and each effective sensor channel 7 on the ath sensor array 6 can be expressed as: in For the drone at time segment n, 10 and the lth sensor on the a-th sensor array 6 m The distance between each of the 7 sensor channels (the m-th valid sensor channel 7), and Theoretically, within this time segment of 10, the signal from the drone 5 will reach the lth sensor on the a-th sensor array 6. m The 7th sensing channel and the one with the largest average signal energy The time delay of 14 for each of the 7 sensing channels can be calculated as follows: in in v s This refers to the speed of sound wave propagation.

[0066] S43. That is, the time delay function of the entire detection network 12.

[0067] Specifically, such as Figure 6 As shown, in the embodiment provided by the present invention, on the fourth time segment signal 10, the time delay function 12 of the entire detection network can be expressed as:

[0068]

[0069] S5. Obtain the spatial location estimate of UAV 5 within this time segment 10, specifically as follows:

[0070] Using ΔT n Time delay function 12-weighted fitting Δt n Time delay matrix 14, and weighting coefficients of the effective sensing channels 7 on each sensing array 6 within n time segments 10. It is negatively correlated with the average energy of its nth time segment 10, and the fitting error of the entire sensor network can be expressed as When the fitting error is minimized, its corresponding position parameter (X) n ,Y n Z n This is the optimal spatial position estimate of UAV 5 in time segment 10 at time n.

[0071] Specifically, in the embodiments provided by the present invention, the weighting coefficient of each effective sensing channel 7 is set to 1. For example... Figure 7 As shown, the fitting error of UAV 5 is smallest at the spatial position (5.144, 8.609). Therefore, (5.144, 8.609) is the best spatial position estimate of UAV 5 in the fourth time segment 10.

[0072] S6. Further obtaining the spatial position estimate of UAV 5 in any time segment 10 will enable real-time tracking of the low-altitude UAV position.

[0073] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for real-time detection and positioning of low-altitude unmanned aerial vehicles (UAVs), comprising deploying an acoustic wave sensing optical cable in the area to be monitored, wherein the acoustic wave sensing optical cable is divided into several sensor arrays containing multiple sensing channels, forming a detection network, characterized in that... Includes the following steps: S1. Acquire the acoustic signal of each sensing channel of each sensing array. Where a is the sensor array number, b a This represents the number of sensing channels in the sensing array; S2. Denoise the acoustic signal of each sensing channel in each sensing array to obtain... The denoised signal is then divided into N continuous segments of equal duration, i.e. S3. Segment the signal at time n. The signal time delay matrix is ​​constructed as follows: S31. Obtain the nth time segment signal of the acoustic wave signal detected by each sensor channel. Find the average energy of the signal in the nth time segment of each sensing channel in the sensing array. Setting an energy threshold E for low-altitude UAV signals T And the sensor channel segment signals with an average energy greater than the energy threshold are identified as valid sensor channel segment signals. Assuming the number of valid sensor channel segment signals is... The effective sensing channel segment signal of the sensing array is then: Among them l m The sensor channel number is the segment number of the m-th valid sensor channel signal. S32. Obtain the segmented signal of the effective sensing channel on the sensing array. The signal with the highest average energy in The sensor channel number with the highest average energy among the effective sensor channel segment signals of the sensor array is determined, and the time delay matrix between the other sensor channel segment signals and this signal is calculated sequentially. in S33. The time delay matrix of the entire detection network can be represented as follows: S4. Construct a theoretical time delay function based on the spatial matrix of the sensor array in the nth time segment, specifically as follows: S41. Based on the spatial locations of the effective sensing channels determined in step S31, construct the spatial matrix of the sensing array for the nth time segment. in S42. Assume the spatial position of the UAV in the nth time segment is F(X). n ,Y n Z n If the spatial distance between the UAV in the nth time segment and each effective sensor channel on the ath sensor array is expressed as: in For the UAV at time segment n and on the a-th sensor array, the l-th time segment... m The distance between each sensor channel, and Theoretically, within this time segment, the drone signal reaches the lth sensor on the a-th sensor array. m The sensor channel with the highest average signal energy. Time delay of each sensing channel in in v s The speed of sound wave propagation; S43. This is the theoretical time delay function of the entire detection network; S5. Obtain the spatial location estimate of the UAV for this time segment, specifically as follows: Using the theoretical time delay function ΔT n Weighted fitting Δt n The time delay matrix has a fitting error of . in The weighting coefficients are the positional parameters (X) corresponding to the minimum fitting error. n ,Y n Z n This is the optimal spatial position estimate for the UAV; S6. Further obtaining the spatial position estimate of the UAV in any time segment will enable real-time tracking of the low-altitude UAV position.

2. The method according to claim 1, characterized in that, The acoustic sensing optical cable includes single-mode optical cable, multi-mode optical cable, scattering-enhancing microstructure optical cable, and spiral-wound sensitizing optical cable, and its deployment methods include single-line type and overlapping type.

3. The method according to claim 2, characterized in that, The sensing arrays divided by the acoustic wave sensing optical cable do not overlap with each other in space.

4. The method according to claim 1, characterized in that, In step S2, the length of the sensing channels of the acoustic wave sensing optical cable is greater than or equal to the minimum spatial resolution that acoustic wave sensing can resolve, and the interval between adjacent sensing channels is greater than or equal to the minimum spatial sampling resolution of acoustic wave sensing.

5. The method according to claim 4, characterized in that, In step S2, the methods for denoising the acoustic signals of each sensing channel of each sensing array include filtering, empirical mode decomposition, wavelet decomposition, adaptive filtering, and machine learning.

6. The method according to claim 1, characterized in that, In step S2, the interval between each time segment signal is greater than or equal to the minimum time sampling interval of the acoustic wave sensor for the acoustic wave signal.

7. The method according to claim 1, characterized in that, In step S31, the energy threshold E T Greater than noise energy, lower than maximum average energy And the number of effective sensing channel segmented signals Greater than or equal to 3.

8. The method according to claim 1, characterized in that, In step S32, the methods for calculating the time delay matrix include the generalized cross-correlation method, the high-order cross-correlation method, the short-time energy delay estimation method, the fractional delay estimation method, and the adaptive filtering delay estimation method.

9. The method according to claim 1, characterized in that, The relationship between the weighting coefficients of each sensing channel and their average energy in step S5 is a negatively correlated polynomial. Form, where i = 0, 1, 2, 3, ..., k i ≥0 and not all of them are 0.

10. A real-time detection and positioning system for low-altitude unmanned aerial vehicles (UAVs), characterized in that, include: Computer-readable storage media and processors; The computer-readable storage medium is used to store executable instructions; The processor is used to read executable instructions stored in the computer-readable storage medium and execute the low-altitude unmanned aerial vehicle real-time detection and positioning method according to any one of claims 1 to 9.

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