Grid-based drone detection system, drone detection single station, and detection method

Abstract

The present invention discloses a gridded UAV detection system, a UAV detection station and a detection method, and relates to the technical field of UAV monitoring and early warning. Based on the problem, scanning data of a test UAV is collected by each UAV detection station deployed in a monitoring area, and a UAV feature data sequence of each UAV detection station is obtained. The detection features of the UAV detection station are constructed based on the feature data of the test UAV of the UAV detection station, and the UAV detection station is marked as an abnormal UAV detection station and isolated. According to the number and distribution of the isolated UAV detection stations, the terrain shielding loss rate of the detection network corresponding to the UAV trajectory is obtained. According to the terrain shielding loss rate of the detection network of different UAV trajectories, the detection risk of different UAV trajectories is obtained and stored in a database. According to the similarity between the target UAV trajectory and the UAV trajectory in the database, the predicted detection risk of the target UAV is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) monitoring and early warning, and in particular to a gridded UAV detection system, a UAV detection station, and a detection method. Background Art

[0002] With the rapid development of drone technology, its widespread use in civil, commercial, and military fields has brought severe security and regulatory challenges. Traditional grid-based drone detection methods mainly rely on single-point monitoring or single sensors, which have the following limitations:

[0003] Traditional methods acquire data from a single detection station, making them susceptible to terrain obscuration, electromagnetic interference, and sensor blind spots, leading to missed or false positives. Most systems use fixed thresholds to identify anomalies, making them incapable of adapting to changes in drone flight status or the dynamic environment. Failure to consider the impact of complex terrain on detection network performance makes it difficult to quantify monitoring blind spots caused by terrain obscuration, resulting in inaccurate risk predictions. Inadequate fusion of multi-source sensor data leads to low information utilization and makes it difficult to address miniaturized and stealthy drones. Summary of the Invention

[0004] The present invention aims to overcome the shortcomings of the prior art and provide a grid-based drone detection method, comprising the following steps:

[0005] Step 1: Scan and collect data of the test drone through each drone detection station deployed in the monitoring area to obtain the drone feature data sequence of each drone detection station;

[0006] Step 2: Based on the test drone feature data of the drone detection station, the detection features of the drone detection station are constructed. The relative change value of the detection features of each drone detection station is calculated respectively. If the relative change value is less than the preset dynamic threshold, the detection consistency of the drone detection station is determined to be consistent, and step 3 is entered. Otherwise, it is marked as an abnormal drone detection station and isolated.

[0007] Step 3: Align the test drone trajectory data of each drone detection station with consistent detection consistency according to the spatial coordinates of the drone detection station, generate the consistency test drone trajectory of the corresponding drone detection station, and calculate the global spatial consistency coefficient. If the global spatial consistency coefficient is greater than the preset threshold, proceed to step 5; otherwise, proceed to step 4.

[0008] Step 4: Obtain the deviation between the test drone trajectory and the standard test drone trajectory of each drone detection station, and locate and isolate the abnormal detection drone detection station based on the deviation;

[0009] Step 5: Based on the number and distribution of isolated drone detection stations, the terrain masking loss rate of the detection network corresponding to the drone trajectory is obtained; based on the terrain masking loss rate of the detection network for different drone trajectories, the detection risk of different drone trajectories is obtained and stored in the database;

[0010] Step 6: Based on the similarity between the target UAV trajectory and the UAV trajectories in the database, the predicted detection risk of the target UAV is obtained.

[0011] Furthermore, the method of collecting data from the test drone using a drone detection station deployed in the monitoring area to obtain a drone feature data sequence of each drone detection station includes:

[0012] The drone characteristic data includes the azimuth, elevation and relative distance of the test drone; the drone characteristic data sequence is obtained according to the set collection time and collection interval; the relative distance is the distance between the test drone and the drone detection station.

[0013] Furthermore, the signal data sequence of the drone detection station is used to construct the detection feature of the drone detection station, and the relative change value of the signal feature of each drone detection station is calculated respectively, including:

[0014] The detection characteristics of the drone detection single station are: the relative change of the selected drone feature data within the set collection interval; based on the difference between the relative change amounts of each drone detection single station, the relative change value of the drone detection single station signal characteristics is obtained.

[0015] Furthermore, the test drone trajectory data of each drone detection station with consistent detection consistency are aligned according to the spatial coordinates of the drone detection station to generate consistency test drone trajectories corresponding to the drone detection station, including:

[0016] The test drone trajectory data of the drone detection single station is the test drone trajectory data based on the spatial position of the drone detection single station; the test drone trajectory data of each drone detection single station is converted to the same trajectory space to obtain the consistency test drone trajectory of the corresponding drone detection single station.

[0017] Furthermore, the calculation of the global spatial consistency coefficient includes:

[0018] The global spatial consistency coefficient is: the ratio of the coincidence rate between the consistency test drone trajectory of each corresponding drone detection station and the standard trajectory of the test drone is greater than the coincidence rate threshold.

[0019] Furthermore, obtaining the deviation between the test drone trajectory and the test drone standard trajectory at each drone detection station includes:

[0020] The non-overlap rate between the trajectory of the consistency test drone and the standard trajectory of the test drone corresponding to the drone detection single station is the deviation between the trajectory of the test drone and the standard trajectory of the test drone at the drone detection single station.

[0021] Furthermore, the terrain shielding loss rate of the detection network corresponding to the drone trajectory is obtained based on the number and distribution of the isolated drone detection stations, including:

[0022] The terrain shielding loss rate of the detection network corresponding to the drone trajectory is obtained based on the ratio of the sum of the products of each isolated drone detection station and the corresponding loss weight to the sum of the products of the drone detection stations deployed in the monitoring area and the corresponding loss weight.

[0023] Furthermore, the detection risk of the target drone is obtained based on the similarity between the target drone trajectory and the drone trajectories in the database, including:

[0024] Obtain the target UAV trajectory, match the target UAV trajectory with the most similar UAV trajectory in the database, and obtain the terrain masking loss rate of the corresponding detection network; the terrain masking loss rate of the corresponding detection network is the terrain masking loss rate of the predicted detection network of the target UAV trajectory, and according to the predicted terrain masking loss rate of the detection network, the detection risk of the target UAV is obtained.

[0025] A gridded drone detection station, applying the gridded drone detection method, includes a drone monitoring module, a power module, a positioning module, a communication module, and a data processing module;

[0026] The drone monitoring module, power module, positioning module, and communication module are respectively connected to the data processing module.

[0027] A gridded drone detection system, using the drone detection station, includes multiple gridded drone detection stations and a drone detection control server; the multiple drone detection stations are respectively connected to the drone detection control server in communication;

[0028] The drone detection station is used to collect drone data;

[0029] The drone detection control server is used to perform drone monitoring and early warning based on drone data collected by the drone detection single station;

[0030] The UAV detection and control server includes a UAV detection single-station networking control module and a database module;

[0031] The UAV detection single station networking control module is used to control the UAV detection single station to form a network;

[0032] The database module is used to store the terrain shielding loss rate and corresponding detection risk of the detection network of different UAV trajectories.

[0033] The present invention has the following beneficial effects: by testing the detection consistency of each drone detection station, it can promptly identify and isolate abnormal detection stations, reducing detection errors caused by individual station failures or environmental factors. Furthermore, when calculating the global spatial consistency coefficient, the overlap rate between the trajectory of the consistency test drone and the standard trajectory of each station is taken into account, further improving detection accuracy.

[0034] By calculating the relative change in detection characteristics of each drone detection station and setting dynamic thresholds for consistency, the detection stability of each station can be effectively guaranteed. Even if some stations fail or are interfered with, they can be promptly detected and isolated, ensuring the reliability of the entire detection system.

[0035] This paper introduces the concept of terrain obscuration loss rate. Based on the number and distribution of isolated drone detection stations, the terrain obscuration loss rate of the detection network for different drone trajectories is calculated to assess detection risk. This risk assessment method is more comprehensive and accurate, providing a more effective decision-making basis for drone supervision and disposal.

[0036] By storing the detection risks of different drone trajectories and quickly obtaining the predicted detection risk of the target drone based on the similarity between the target drone trajectory and the trajectory in the database, potential drone threats can be discovered in advance, early warnings can be issued in time, and time can be bought for subsequent disposal measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flowchart of the grid-based drone detection method;

[0038] Figure 2 This is a schematic diagram of the principle of grid-based UAV detection of a single station;

[0039] Figure 3 Schematic diagram of the grid-based drone detection system. DETAILED DESCRIPTION

[0040] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0041] The features and performance of the present invention are further described in detail below with reference to the embodiments.

[0042] like Figure 1 As shown, the grid-based drone detection method includes the following steps:

[0043] Step 1: Scan and collect data of the test drone through each drone detection station deployed in the monitoring area to obtain the drone feature data sequence of each drone detection station;

[0044] Step 2: Based on the test drone feature data of the drone detection station, the detection features of the drone detection station are constructed. The relative change value of the detection features of each drone detection station is calculated respectively. If the relative change value is less than the preset dynamic threshold, the detection consistency of the drone detection station is determined to be consistent, and step 3 is entered. Otherwise, it is marked as an abnormal drone detection station and isolated.

[0045] Step 3: Align the test drone trajectory data of each drone detection station with consistent detection consistency according to the spatial coordinates of the drone detection station, generate the consistency test drone trajectory of the corresponding drone detection station, and calculate the global spatial consistency coefficient. If the global spatial consistency coefficient is greater than the preset threshold, proceed to step 5; otherwise, proceed to step 4.

[0046] Step 4: Obtain the deviation between the test drone trajectory and the standard test drone trajectory of each drone detection station, and locate and isolate the abnormal detection drone detection station based on the deviation;

[0047] Step 5: Based on the number and distribution of isolated drone detection stations, the terrain masking loss rate of the detection network corresponding to the drone trajectory is obtained; based on the terrain masking loss rate of the detection network for different drone trajectories, the detection risk of different drone trajectories is obtained and stored in the database;

[0048] Step 6: Based on the similarity between the target UAV trajectory and the UAV trajectories in the database, the predicted detection risk of the target UAV is obtained.

[0049] Specifically, multiple drone detection stations are strategically deployed within the monitoring area. These stations, acting as nodes in the monitoring network, undertake the crucial task of data collection. Each drone detection station scans and collects data from the test drone. The collected drone characteristic data primarily includes the test drone's azimuth, elevation, and relative distance. The azimuth determines the drone's horizontal position, the elevation reflects its vertical angle, and the relative distance reflects the spatial separation between the drone and the detection station. Based on the set collection duration and interval, the system generates a sequence of ordered drone characteristic data. This data is arranged chronologically to form a drone characteristic data sequence, providing the raw data foundation for subsequent analysis. For example, in a large park monitoring scenario, data is collected every 10 seconds for 5 minutes, resulting in 30 sets of drone characteristic data sequences.

[0050] Based on the test drone characteristic data from the drone detection station acquired in the first step, a detection signature is constructed for each detection station. A detection signature is defined here as the relative change in the selected drone characteristic data within a set collection interval. Taking azimuth as an example, if the azimuth changes from 30 degrees to 35 degrees between two consecutive collection intervals, the relative change in azimuth is 5 degrees. By calculating the difference between the relative changes for each drone detection station, the relative change value of the drone detection station's signal characteristic is calculated. This relative change value is then compared with a preset dynamic threshold. If the relative change value is less than the preset dynamic threshold, the detection data from that drone detection station is highly consistent and stable, and the detection consistency is determined to be consistent, allowing the next step to proceed. Otherwise, the drone detection station is marked as an anomaly and isolated to prevent the anomalous data from interfering with subsequent analysis. This step acts as a data quality check, ensuring that the data entering subsequent processing is accurate and valid.

[0051] The test drone trajectory data from each drone detection station with consistent detection consistency is aligned according to the spatial coordinates of the drone detection station. Due to the different spatial locations of each detection station, the trajectory data collected by each station differs spatially. It is necessary to convert these test drone trajectory data based on the different station spatial locations into the same trajectory space to generate the consistency test drone trajectory for the corresponding drone detection station. After the trajectory data alignment is completed, the global spatial consistency coefficient is calculated. The global spatial consistency coefficient is defined as the ratio of the overlap rate between the consistency test drone trajectory of each corresponding drone detection station and the standard test drone trajectory that is greater than the overlap rate threshold. If the global spatial consistency coefficient is greater than the preset threshold, it indicates that the overall trajectory data is consistent; otherwise, it indicates that there may still be problems with some of the detection station data.

[0052] When the global spatial consistency coefficient falls below the preset threshold, the deviation between the test drone's trajectory and the standard test drone's trajectory is calculated for each drone detection station. Here, the non-overlap ratio between the consistent test drone's trajectory and the standard test drone's trajectory for each drone detection station is calculated as the deviation between the test drone's trajectory and the standard test drone's trajectory for that station. By analyzing these deviations, we can accurately locate and isolate problematic anomalous drone detection stations, further optimizing data quality.

[0053] Based on the number and distribution of isolated drone detection stations, the terrain masking loss rate of the detection network corresponding to the drone trajectory is calculated. The specific calculation method is: the terrain masking loss rate of the detection network corresponding to the drone trajectory is obtained based on the ratio of the sum of the products of each isolated drone detection station and the corresponding loss weight, and the sum of the products of the drone detection stations deployed in the monitoring area and the corresponding loss weight. Different terrain and environmental factors will have different degrees of impact on drone detection, and these impacts can be quantified by setting loss weights. For example, in areas with complex terrain such as mountainous areas, the loss weight can be set higher. Based on the terrain masking loss rate of the detection network for different drone trajectories, the detection risk of different drone trajectories can be assessed, and these risk data can be stored in the database to provide data support for subsequent predictions.

[0054] The target drone's trajectory is obtained and matched to the most similar drone trajectory in the database using a specific algorithm. This results in the corresponding detection network's terrain obscuration loss rate. This corresponding detection network's terrain obscuration loss rate is then used to predict the target drone's trajectory. Based on this predicted detection network's terrain obscuration loss rate, the target drone's detection risk is ultimately determined. This approach enables rapid and accurate prediction of the target drone's detection risk based on historical data, providing a decision-making basis for drone monitoring and management.

[0055] like Figure 2 As shown in the figure, the gridded drone detection station is the hardware foundation for implementing the above detection method. It includes a drone monitoring module, a power module, a positioning module, a communication module, and a data processing module. The drone monitoring module is responsible for scanning drones and collecting data, obtaining key drone feature data. The power module provides a stable power supply for the entire detection station, ensuring the normal operation of all modules. The positioning module accurately determines the spatial coordinates of the detection station, providing location information for operations such as trajectory data alignment. The communication module enables data transmission between the detection station and other devices or servers. The data processing module is the core of the detection station. It is connected to the drone monitoring module, power module, positioning module, and communication module respectively, and is responsible for processing and analyzing the collected data and executing the various algorithms and logical judgments in the detection method.

[0056] like Figure 3 As shown in the figure, the grid-based drone detection system consists of multiple grid-based drone detection stations and a drone detection control server. Each of the multiple drone detection stations is connected to the drone detection control server. The drone detection stations serve as front-end devices, transmitting collected drone data to the drone detection control server in real time. The drone detection control server is used to monitor and warn drones based on the drone data collected by the drone detection stations. It includes a drone detection station networking control module and a database module. The drone detection station networking control module is responsible for controlling the networking of the drone detection stations, optimizing the network structure, and ensuring efficient and stable data transmission. The database module is used to store important data, such as the terrain obscuration loss rate and corresponding detection risk of the detection network for different drone trajectories. This provides data support for detection risk assessment and prediction, enabling the system to continuously optimize its monitoring and warning capabilities based on historical data.

[0057] Example:

[0058] Drone detection in urban commercial centers

[0059] A grid-based drone detection system was deployed in a commercial center, encompassing approximately 5 square kilometers of surrounding office buildings, shopping malls, and plazas. This area is densely populated, has complex terrain, and experiences high traffic volume. Monitoring illegal drone flights aims to ensure public safety and the smooth operation of commercial activities. Twenty drone detection stations were deployed throughout the area to form a monitoring network.

[0060] Data collection

[0061] The collection period is set to 9:00 AM - 10:00 PM on weekdays, with a collection interval of 3 seconds. Each detection station collects the drone's azimuth, elevation, and relative distance data in real time. For example, detection station E records a drone's azimuth of 135°, elevation of 20°, and relative distance of 1500 meters at a certain moment. This data is continuously recorded to form a data sequence.

[0062] Detection feature analysis and anomaly detection

[0063] Construct detection signatures for each detection station and calculate relative changes. For example, for detection station F, the elevation angle changes from 18° to 25° within adjacent acquisition intervals, a relative change of 7°. Calculate the difference in relative changes for all stations, set a preset dynamic threshold of 8°, and flag and isolate abnormal detection stations that exceed this threshold.

[0064] Trajectory data alignment and global consistency assessment

[0065] The trajectory data from the single-station detection with good consistency is converted into spatial coordinates to generate the trajectory of the consistency test drone. The overlap rate threshold is set to 75%, and the global spatial consistency coefficient is calculated to determine whether to proceed to the next step.

[0066] Anomaly detection single-station positioning and isolation

[0067] If the global spatial consistency coefficient does not meet the standard, the trajectory mismatch rate is calculated to locate the abnormal detection station. For example, if the mismatch rate of the detection station G is high, it will be isolated to purify the data.

[0068] Terrain shielding loss rate calculation and detection risk assessment

[0069] Given the densely built-up area, detection stations in different locations have different loss weights. Detection stations near tall buildings have a loss weight of 2, while others have a loss weight of 1. Assuming four detection stations are isolated, two with a loss weight of 2 and two with a loss weight of 1, the terrain obscuration loss rate = (2 × 2 + 2 × 1) ÷ (18 × 1 + 2 × 2) × 100% ≈ 18.2%. Detection risk is assessed and the data is stored.

[0070] Target drone detection risk prediction

[0071] When a target drone appears, its trajectory is obtained and then matched with similar trajectories in the database. Based on the terrain obscuration loss rate of the matched historical trajectory, the predicted detection risk of the target drone is determined so that relevant departments can respond in a timely manner and maintain the safety and order of the city's commercial center.

Claims

1. A grid-based drone detection method, characterized in that: The steps include: Step 1: Scan and collect data of the test drone through each drone detection station deployed in the monitoring area to obtain the drone feature data sequence of each drone detection station; Step 2: Based on the test drone feature data of the drone detection station, the detection features of the drone detection station are constructed. The relative change value of the detection features of each drone detection station is calculated respectively. If the relative change value is less than the preset dynamic threshold, the detection consistency of the drone detection station is determined to be consistent, and step 3 is entered. Otherwise, it is marked as an abnormal drone detection station and isolated. Step 3: Align the test drone trajectory data of each drone detection station with consistent detection consistency according to the spatial coordinates of the drone detection station, generate the consistency test drone trajectory of the corresponding drone detection station, and calculate the global spatial consistency coefficient. If the global spatial consistency coefficient is greater than the preset threshold, proceed to step 5; otherwise, proceed to step 4. Step 4: Obtain the deviation between the test drone trajectory and the standard test drone trajectory of each drone detection station, and locate and isolate the abnormal detection drone detection station based on the deviation; Step 5: Based on the number and distribution of isolated drone detection stations, the terrain masking loss rate of the detection network corresponding to the drone trajectory is obtained; based on the terrain masking loss rate of the detection network for different drone trajectories, the detection risk of different drone trajectories is obtained and stored in the database; Step 6: Based on the similarity between the target UAV trajectory and the UAV trajectories in the database, the predicted detection risk of the target UAV is obtained; The method of constructing the detection features of the drone detection station based on the signal data sequence of the drone detection station and calculating the relative change value of the signal features of each drone detection station respectively includes: The detection characteristics of the drone detection single station are: the relative change of the selected drone feature data within the set collection interval; the relative change value of the drone detection single station signal characteristics is obtained based on the difference between the relative changes of each drone detection single station; The calculation of the global spatial consistency coefficient includes: The global spatial consistency coefficient is: the ratio of the coincidence rate between the consistency test drone trajectory of each corresponding drone detection station and the standard trajectory of the test drone is greater than the coincidence rate threshold.

2. The grid-based drone detection method according to claim 1, characterized in that: The method of collecting data from the test drone using a drone detection station deployed in the monitoring area to obtain a drone feature data sequence from each drone detection station includes: The drone characteristic data includes the azimuth, elevation and relative distance of the test drone; the drone characteristic data sequence is obtained according to the set collection time and collection interval; the relative distance is the distance between the test drone and the drone detection station.

3. The grid-based drone detection method according to claim 1, characterized in that: The method of aligning the test drone trajectory data of each drone detection station with consistent detection consistency according to the spatial coordinates of the drone detection station to generate consistency test drone trajectories corresponding to the drone detection station includes: The test drone trajectory data of the drone detection single station is the test drone trajectory data based on the spatial position of the drone detection single station; the test drone trajectory data of each drone detection single station is converted to the same trajectory space to obtain the consistency test drone trajectory of the corresponding drone detection single station.

4. The grid-based drone detection method according to claim 3, characterized in that: The method of obtaining the deviation between the test drone trajectory and the standard trajectory of the test drone at each drone detection station includes: The non-overlap rate between the trajectory of the consistency test drone and the standard trajectory of the test drone corresponding to the drone detection single station is the deviation between the trajectory of the test drone and the standard trajectory of the test drone at the drone detection single station.

5. The grid-based drone detection method according to claim 1, characterized in that: The terrain shielding loss rate of the detection network corresponding to the drone trajectory is obtained based on the number and distribution of isolated drone detection stations, including: The terrain shielding loss rate of the detection network corresponding to the drone trajectory is obtained based on the ratio of the sum of the products of each isolated drone detection station and the corresponding loss weight to the sum of the products of the drone detection stations deployed in the monitoring area and the corresponding loss weight.

6. The grid-based drone detection method according to claim 1, characterized in that: The detection risk of the target drone is obtained based on the similarity between the target drone trajectory and the drone trajectories in the database, including: Obtain the target UAV trajectory, match the target UAV trajectory with the most similar UAV trajectory in the database, and obtain the terrain masking loss rate of the corresponding detection network; the terrain masking loss rate of the corresponding detection network is the terrain masking loss rate of the predicted detection network of the target UAV trajectory, and according to the predicted terrain masking loss rate of the detection network, the detection risk of the target UAV is obtained.

7. Grid-based drone detection single station, characterized by: The grid-based drone detection method according to any one of claims 1 to 6 includes a drone monitoring module, a power module, a positioning module, a communication module, and a data processing module; The drone monitoring module, power module, positioning module, and communication module are respectively connected to the data processing module.

8. Grid-based drone detection system, characterized by: The grid-based drone detection station described in claim 7 includes multiple drone detection stations and a drone detection control server; the multiple drone detection stations are respectively connected to the drone detection control server in communication; The drone detection station is used to collect drone data; The drone detection control server is used to perform drone monitoring and early warning based on drone data collected by the drone detection single station; The UAV detection and control server includes a UAV detection single-station networking control module and a database module; The UAV detection single station networking control module is used to control the UAV detection single station to form a network; The database module is used to store the terrain shielding loss rate and corresponding detection risk of the detection network of different UAV trajectories.

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