A method and system for cooperative jamming induction control of a multi-device cross-region cross-level networking of a UAV defense based on a GIS base

Abstract

The application relates to the technical field of data acquisition control, and discloses a method and system for cooperative jamming induction control of multi-device cross-region and cross-level networking of a UAV defense based on a GIS base, which comprises the following steps: constructing a GIS base based on three-dimensional geographic information data and device node registration information, outputting a unified spatial reference system and high-precision reference point coordinates; obtaining initial GIS coordinates based on the above coordinates and detecting a deviation field, outputting deviation prior constraints; performing distance and clock difference joint adjustment based on the deviation prior, outputting fine-target GIS coordinates and equivalent clock difference parameters; performing common viewing and two-way comparison calibration based on the fine-target GIS coordinates and the equivalent clock difference parameters, outputting a nanosecond-level time stamp detection data packet; and performing point trace registration and weighted intersection based on the detection data packet, outputting a beam pointing angle and a delay compensation amount. Due to the adoption of the space reference, the deviation constraint, the joint adjustment and the time service fusion connection, the application improves the fusion positioning accuracy and the cooperative jamming reliability.

Description

Technical Field

[0001] This invention relates to the field of data acquisition and control technology, and in particular to a method and system for cross-regional and cross-level networked collaborative interference induction and control of unmanned aerial vehicles (UAVs) based on a GIS platform. Background Technology

[0002] Currently, in GIS-based drone defense systems, to support multi-source target fusion positioning and coordinated jamming induction in cross-regional and cross-level networking, it is typically necessary to deploy radar, radio detection equipment, and jamming induction nodes over a wide area. Existing technical approaches generally involve operators carrying real-time dynamic differential positioning equipment to measure the precise coordinates of each device's installation location point by point and manually inputting the data into the GIS platform for spatial calibration. Each device relies on its built-in satellite positioning module for coarse single-point positioning and operates independently using a local crystal oscillator clock. After receiving control commands via wired or wireless networks, it reports target detection data and attempts to achieve loose time synchronization between multiple devices using network time protocols. This model relies on manual operation and a distributed clock architecture. While it can establish spatial data correlation to some extent, it is difficult to adapt to the actual deployment needs of complex terrain in the defense area, a large number of devices, and frequent changes in location. In particular, it cannot solve the spatiotemporal inconsistency problem caused by differences in device coordinates and time bases.

[0003] For example, when the defense area has significant terrain undulations or equipment is installed in inaccessible locations such as rooftops, towers, or high-altitude supports, manual point-by-point calibration is not only inefficient and time-consuming, but the accumulated errors during the measurement benchmark transfer process also significantly increase the relative coordinate deviation between devices. Without precise correction, the initial single-point positioning of each device can only achieve meter-level accuracy or even worse. At the same time, differences in the internal crystal oscillator characteristics and temperature drift of each device cause clock frequency offsets, and the time deviation of different devices for the same UAV signal recording can reach millisecond levels. When mapping such multi-source points with spatial positioning errors and inconsistent time stamps to a unified GIS base map for spatial registration, obvious target point misalignment and ghosting will occur. In severe cases, it will cause false tracks, target ghosting, or track breaks, directly leading to errors in the multi-sensor fusion positioning results. This, in turn, makes the subsequent calculation of beam pointing and signal transmission delay induced by cooperative interference inaccurate. Existing technologies cannot fully meet the requirements of high precision and fully automatic spatiotemporal unification.

[0004] Therefore, there is an urgent need for a method that can automatically calibrate the GIS coordinates of equipment and achieve time synchronization of collected data. This would enable radar and radio detection equipment deployed in different areas to ensure that the spatial alignment error of the collected UAV signals on the GIS base map is less than 1 meter, even under complex terrain and inconvenient installation conditions. This would improve the accuracy of multi-source data fusion positioning and the reliability of collaborative interference induction. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, the purpose of this invention is to propose a method for multi-device cross-regional and cross-level networked collaborative interference induction and control for UAV defense based on a GIS base. This method aims to solve the technical problem that in the existing technology, the manual method is used for GIS coordinate calibration and time synchronization of multiple devices. Especially under the conditions of large installation deviations of cross-regional networked devices and inconsistent clock references, manual calibration is inefficient and cannot be synchronized in real time, resulting in target positioning errors due to spatiotemporal deviations when fusioning multi-source data.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for multi-device cross-regional and cross-level networked collaborative interference induction and control of UAV defense based on GIS base.

[0007] A method for multi-device cross-regional and cross-level networked collaborative interference induction and control for UAV defense based on a GIS platform includes:

[0008] Step S10: Obtain 3D geographic information data of the defense area and registration information of defense equipment nodes; perform GIS base construction task based on CGCS2000 ellipsoidal coordinate frame and surveying benchmark anchoring method; output unified spatial reference system and high-precision benchmark coordinates.

[0009] Step S20: Based on the unified spatial reference system and the high-precision reference point coordinates, obtain the initial GIS coordinates of each device after power-on through single-point positioning, and perform the system spatial deviation field detection task by using the collaborative UAV track spatiotemporal interpolation comparison method, and output the deviation prior constraints of the initial coordinates of each device.

[0010] Step S30: Based on the prior constraints of the deviation of the initial coordinates of each device, obtain the arrival time of the calibration signal of the set of devices participating in the calibration and the combination of reference points, and perform the device coordinate fine calibration task by using the joint adjustment method of distance observation and clock error parameters, and output the finely calibrated GIS coordinates and equivalent clock error parameters of each device;

[0011] Step S40: Based on the precise GIS coordinates of each device and the equivalent clock difference parameters, the clock synchronization task of all network devices is performed using a complementary clock calibration method of satellite common view and two-way comparison, and a probe data packet labeled with a unified nanosecond-level timestamp and precise GIS coordinates is output.

[0012] Step S50: Based on the detection data packet labeled with a unified nanosecond-level timestamp and precise GIS coordinates, perform multi-source point spatial registration and weighted least squares intersection calculation, and output the beam pointing angle and signal transmission delay compensation of each interference-inducing device.

[0013] Preferably, step S10, which involves acquiring three-dimensional geographic information data of the defense area and registration information of defense equipment nodes, performing a GIS base construction task based on the CGCS2000 ellipsoidal coordinate frame and the mapping benchmark anchoring method, and outputting a unified spatial reference system and high-precision benchmark coordinates, specifically includes:

[0014] Step S101: Acquire digital elevation model, orthophoto and 3D building model, and use CGCS2000 ellipsoidal coordinate system as unified spatial reference system to generate GIS base containing 3D landform, building outline and height attributes.

[0015] Step S102: Select multiple mapping coordinates as reference points within the defense area covered by the GIS base. Each reference point is continuously and statically observed for at least 72 hours using BeiDou and GPS dual-frequency signals. The observation data is then connected to the National Geodetic Control Network for static precise single-point positioning calculation to obtain high-precision reference point coordinates with a mean error of better than 0.02 meters for the plane coordinate component and a mean error of better than 0.03 meters for the elevation component.

[0016] Step S103: The high-precision reference point coordinates are pre-set as absolute constraints on the GIS base, and according to the defense equipment node registration information, the detection equipment and interference inducement equipment are respectively configured with satellite positioning module, broadband collaborative signal transceiver front end, high-stability crystal oscillator clock module and wired and wireless dual-link communication module to construct a cross-regional and cross-level dual-channel networking structure.

[0017] Preferably, step S20, based on the unified spatial reference system and the high-precision reference point coordinates, involves obtaining the initial GIS coordinates of each device after power-on via single-point positioning, performing the system spatial deviation field detection task using a collaborative UAV trajectory spatiotemporal interpolation comparison method, and outputting the deviation prior constraints of the initial coordinates of each device. Specifically, this includes:

[0018] Step S201: After each device is powered on, the satellite positioning module is started, and standard single-point positioning is performed using broadcast ephemeris and single-frequency pseudorange to obtain initial GIS coordinates. The initial GIS coordinates are then uploaded to the collaborative control center via wired or wireless link.

[0019] Step S202: The dynamic differential positioning module is mounted on the collaborative drone, which traverses the defense area at a fixed altitude and speed along the intersecting grid route, and collects a three-dimensional real track sequence at a frequency of not less than 10Hz during the flight.

[0020] Step S203: Obtain the detection tracks formed by each device for the collaborative UAV, align the detection tracks with the three-dimensional real track sequence by timestamp interpolation, calculate the overall spatial offset vector of the detection track points of each device, and use the overall spatial offset vector as the deviation prior constraint of the initial GIS coordinates of the corresponding device.

[0021] Preferably, in step S202, the dynamic differential positioning module adopts an antenna RTK and inertial navigation combined navigation system, and performs positioning through RTK differential correction data from a network of four continuously operating reference stations set up at the boundary of the defense area, so that the real-time positioning accuracy of the cooperative UAV meets the requirements of no more than 2 meters in the horizontal direction and no more than 3 meters in the vertical direction.

[0022] Preferably, in step S30, based on the prior constraints of the deviation of the initial coordinates of each device, the arrival time of the calibration signal of the set of devices participating in the calibration and the combination of reference points is obtained, and the device coordinate fine calibration task is performed by using a joint adjustment method of distance observation and clock error parameters, and the finely calibrated GIS coordinates and equivalent clock error parameters of each device are output. Specifically, this includes:

[0023] Step S301: The collaborative control center schedules each device to send a calibration signal modulated by radio frequency spread spectrum code in a time division multiple access manner. The device and the reference point in the receiving state record the arrival time of the calibration signal and convert the arrival time into a distance observation between the device and the reference point and a distance observation between the devices.

[0024] Step S302: Define the initial coordinates of device i as follows The coordinate correction vector of device i is The high-precision coordinates of reference point j are: The initial coordinates of device k are The coordinate correction vector for device k is The equivalent clock error parameter of device i, expressed in terms of distance, is: The equivalent clock error parameter of device k, expressed in terms of distance, is: The distance between device i and reference point j is measured as follows: The observed distance between device i and device k is The observation error between device i and reference point j is The observation error between device i and device k is The following observation equation is established:

[0025]

[0026]

[0027] in, Indicates Euclidean distance;

[0028] Step S303: Perform a first-order Taylor expansion and linearize all observation equations at the initial coordinates of each device. Use the prior deviation constraints of the initial coordinates of each device as the initial range and regularization constraints of the coordinate correction vector, and use the high-precision reference point coordinates as the weights higher than... The virtual observations are combined into a normal equation, which is then solved using robust weighted least squares iteration with equivalent weight functions; where m represents the observation number. This represents the standardized residual of the m-th observation. This represents the equivalent weight of the m-th observation. Denotes the constant of the first piecewise segment. Represents the second piecewise constant; when When, maintain the original weight of the m-th observation; when When the m-th observation is reduced, the equivalent weight is reduced according to the following formula:

[0029]

[0030] when When the m-th observation is set to zero, the equivalent weight of the m-th observation is reset to zero; where, , Iterate until the Euclidean norm of the coordinate correction component in the state vector composed of each coordinate correction vector and each equivalent clock error parameter is less than 0.01 meters, and output the precise GIS coordinates and equivalent clock error parameters of each device.

[0031] Preferably, step S40, based on the precise GIS coordinates of each device and the equivalent clock difference parameters, employs a complementary clock calibration method combining satellite common-view and two-way comparison to perform a network-wide device clock synchronization task, and outputs a probe data packet labeled with a unified nanosecond-level timestamp and precise GIS coordinates. This step specifically includes:

[0032] Step S401: Each device simultaneously receives the same second pulse from at least three common-view satellites in the zenith direction within a unified preset period, and records the local arrival time of device A. and the local arrival time of device B The propagation delay of device A, calculated based on precise ephemeris, ionospheric models, and tropospheric models, is also considered. and the propagation delay of device B Calculate the common-view time difference between device A and device B. :

[0033]

[0034] Step S402: Within the unified preset period, device A and device B exchange timestamp messages via a directional wireless link to record the sending time of device A. Device B receiving time Device B's sendback time and the final receiving time of device A Calculate the estimated propagation delay of the bidirectional link between device A and device B. :

[0035]

[0036] And calculate the bidirectional comparison time difference between device A and device B based on the estimated bidirectional link propagation delay. :

[0037]

[0038] Step S403: Establish a Kalman filter with inter-device clock difference and frequency offset as state variables. Use the common-view time difference and the bidirectional comparison time difference as observations for fusion filtering. Dynamically estimate and distribute the time difference calibration amount step by step. Perform physical layer calibration of the clock of each device through voltage-controlled crystal oscillator calibration and digital phase compensation. Mark each frame of detection data with a unified nanosecond-level timestamp according to the calibrated clock. At the same time, write the corresponding device's precise GIS coordinates into the data packet. Output the detection data packet marked with the unified nanosecond-level timestamp and precise GIS coordinates.

[0039] Preferably, step S50, which involves performing multi-source point spatial registration and weighted least squares intersection calculation based on the detection data packet labeled with a unified nanosecond-level timestamp and precise GIS coordinates, and outputting the beam pointing angle and signal transmission delay compensation amount for each interference-inducing device, specifically includes:

[0040] Step S501: Based on the detection data packet labeled with a unified nanosecond-level timestamp and precise GIS coordinates, perform spatial registration on the multi-source target points uploaded by each detection node, convert the multi-source target points to a unified GIS base coordinate system, and use the unified nanosecond-level timestamp to complete time alignment, and output the registered point set.

[0041] Step S502: Perform point association and track initiation on the registered point set, construct an observation equation set with the target's three-dimensional coordinates as unknowns, assign weights according to the signal-to-noise ratio or confidence level of each point, and use the weighted least squares method to solve the target's three-dimensional position.

[0042] Step S503: Based on the target's three-dimensional position and the precise GIS coordinates of each interference-inducing device, determine the horizontal projection direction and vertical height difference of the target relative to each interference-inducing device. Calculate the beam azimuth angle of each interference-inducing device pointing towards the target based on the horizontal projection direction. Calculate the beam elevation angle of each interference-inducing device pointing towards the target based on the vertical height difference and the horizontal projection distance. Based on the spatial propagation distance between the target's three-dimensional position and the precise GIS coordinates of each interference-inducing device, combined with a preset reference propagation distance and the radio wave propagation speed, calculate the signal transmission delay compensation amount of each interference-inducing device. Output the beam pointing angle and signal transmission delay compensation amount of each interference-inducing device.

[0043] This invention also provides a multi-device cross-regional and cross-level networked collaborative interference induction and control system for UAV defense based on a GIS platform, comprising:

[0044] The GIS base construction module is used to acquire three-dimensional geographic information data of the defense area and registration information of defense equipment nodes. Based on the CGCS2000 ellipsoidal coordinate frame and the surveying benchmark anchoring method, it performs the GIS base construction task and outputs a unified spatial reference system and high-precision benchmark coordinates.

[0045] The spatial deviation field detection module is used to obtain the initial GIS coordinates of each device after power-on through single-point positioning based on the unified spatial reference system and the high-precision reference point coordinates. It performs the system spatial deviation field detection task by using a collaborative UAV track spatiotemporal interpolation comparison method and outputs the deviation prior constraints of the initial coordinates of each device.

[0046] The equipment coordinate fine calibration module is used to obtain the arrival time of the calibration signal of the set of equipment participating in the calibration and the combination of reference points based on the a priori constraints of the deviation of the initial coordinates of each equipment, and to perform the equipment coordinate fine calibration task by using a joint adjustment method of distance observation and clock error parameters, and output the fine calibrated GIS coordinates and equivalent clock error parameters of each equipment.

[0047] The network-wide clock synchronization module is used to perform the network-wide device clock synchronization task based on the precision-calibrated GIS coordinates of each device and the equivalent clock difference parameters, using a complementary clock calibration method of satellite common view and two-way comparison, and outputs a probe data packet labeled with a unified nanosecond-level timestamp and precision-calibrated GIS coordinates;

[0048] The collaborative interference-induced solution module is used to perform multi-source point spatial registration and weighted least squares intersection solution based on the detection data packets labeled with unified nanosecond-level timestamps and precise GIS coordinates, and output the beam pointing angle and signal transmission delay compensation of each interference-induced device.

[0049] The present invention also provides a GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control device. The GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control device includes: a memory, a processor, and a GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control program stored in the memory and executable on the processor. When the GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control program is executed by the processor, it implements the above-mentioned method.

[0050] The present invention also provides a computer program product, the computer program product including a UAV defense multi-device cross-regional and cross-level network collaborative interference induction control program based on GIS base, the UAV defense multi-device cross-regional and cross-level network collaborative interference induction control program based on GIS base is executed by a processor to implement the above method.

[0051] The beneficial effects of this invention are as follows:

[0052] This invention establishes a unified spatial reference framework by deploying high-precision benchmarks within the defense area and incorporating them into the national geodetic control network through static precision single-point positioning and measurement. This avoids the transmission of coordinate deviations caused by inconsistent benchmarks in traditional engineering surveying. Furthermore, it utilizes a collaborative UAV equipped with a real-time dynamic differential positioning system to provide centimeter-level flight paths as a spatiotemporal reference. This system detects and constrains the initial coordinates of each device, providing reliable prior information for subsequent precision calibration. As a result, the entire calibration process can be completed automatically without requiring manual intervention at the device installation point.

[0053] By jointly adjusting distance observations and equivalent clock bias parameters, and introducing robust weighted least squares iterative solutions, robust device-calibrated coordinates and clock bias parameters can still be obtained even under conditions of NLOS errors or random gross errors. This effectively eliminates spatial registration errors caused by inaccurate coordinates and clock asynchrony among multiple devices. Based on this, Kalman filtering is used to fuse clock synchronization using the common-view method and the two-way comparison method, achieving nanosecond-level time uniformity across all network devices. This ensures that the probe data packets uploaded by different devices carry a unified high-precision timestamp and calibrated GIS coordinates, guaranteeing accurate spatiotemporal alignment of multi-source target points in the GIS base. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating the first embodiment of a multi-device cross-regional and cross-level networked collaborative interference induction and control method for UAV defense based on a GIS base, according to the present invention. Detailed Implementation

[0055] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0056] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Example 1: As Figure 1 The diagram shown is a flowchart of the first embodiment of a multi-device cross-regional and cross-level networked collaborative interference induction and control method for UAV defense based on a GIS base, according to the present invention.

[0058] In the first embodiment, a method for multi-device cross-regional and cross-level networked collaborative interference induction and control for UAV defense based on a GIS platform includes:

[0059] Step S10: Obtain 3D geographic information data of the defense area and registration information of defense equipment nodes; perform GIS base construction task based on CGCS2000 ellipsoidal coordinate frame and surveying benchmark anchoring method; output unified spatial reference system and high-precision benchmark coordinates.

[0060] In this step, the 3D geographic information data of the defense area includes digital elevation models, orthophotos, and 3D building models, which describe the regional topography, building outlines, and height attributes. The defense equipment node registration information includes the numbers, deployment areas, communication access methods, and equipment types of the detection and interference-inducing equipment. The system uses the CGCS2000 ellipsoidal coordinate frame as a unified spatial reference and combines this with a surveying benchmark anchoring method to construct the GIS base. First, a GIS base covering the defense area is generated. Then, the high-precision benchmark coordinates obtained through continuous static observations of BeiDou and GPS connected to the national geodetic control network are written into the GIS base as absolute constraints. The unified spatial reference system and high-precision benchmark coordinates output in this step directly provide a common coordinate benchmark for comparing the initial GIS coordinates of each device and detecting the system's spatial deviation field in step S20.

[0061] By using the CGCS2000 ellipsoidal coordinate framework, anchoring of surveying benchmarks, and construction of a GIS base, an absolute reference framework consistent with the national geodetic control network was obtained within the defense area. The high-precision benchmark coordinates far exceed the meter-level accuracy of single-point positioning of equipment, and the benchmarks exhibit strong relative geometric constraints, effectively constructing a stable spatial skeleton within the GIS base. This unified spatial reference system ensures that all subsequent spatial calculations are performed within the same coordinate system, eliminating system distortions caused by inconsistent regional projections and the accumulation of local control point errors. For detection and interference-inducing equipment deployed across regions, even if their initial GIS coordinates have a deviation of several meters, this reference framework can separate and quantify the deviation for correction, thus laying a reliable foundation for the spatiotemporal interpolation comparison of collaborative UAV tracks and the generation of prior constraints for deviations in step S20.

[0062] Traditional methods typically rely on control points left over from the construction phase or temporary RTK reference stations. These control points may shift in location due to disrepair or construction disturbances, making it impossible to guarantee their coordinate accuracy. This is especially problematic in cross-regional, terrain-complex defense systems, where reference points in different areas often do not fall within the same coordinate framework, leading to meter-level or even greater inconsistencies during stitching. Directly using the equipment's own single-point positioning results as a reference, due to the inherent limitations of broadcast ephemeris and single-frequency pseudorange, the accuracy is usually only 5 to 10 meters, far from meeting the target fusion requirements of multi-device collaborative work. This step constructs a GIS base using 3D geographic information data of the defense area, equipment node registration information, and surveying reference point anchoring. It also utilizes long-term dual-frequency static observations and national geodetic network surveys to obtain high-precision reference point coordinates, reducing reference transfer errors and ensuring a reliable starting point for subsequent calibration processes.

[0063] For example, in the construction of a cross-city drone defense system, the area spans approximately 25 kilometers east to west and 18 kilometers north to south. More than ten radars, radio detection equipment, and jamming-inducing devices are deployed in locations difficult to maintain, such as mountain towers, coastal lighthouses, and rooftops in the city. This step involves first acquiring the area's digital elevation model, orthophotos, and 3D building model, and then reading the registration information of each device node to create a unified spatial environment in the GIS base, encompassing topography, building outlines, and device nodes. Subsequently, four geologically stable mapping coordinates are selected within the area as reference points P1 to P4. Each reference point receives dual-frequency signals from BeiDou and GPS for 72 consecutive hours, and the observation data is connected to the National Geodetic Control Network for static precise single-point positioning calculations, yielding high-precision reference point coordinates with a planar coordinate error of approximately 0.013 meters and an elevation error of approximately 0.022 meters. These coordinates are written into the GIS base and locked, and all initial GIS coordinates uploaded by all devices are subsequently verified against this unified spatial reference system.

[0064] Step S20: Based on the unified spatial reference system and the high-precision reference point coordinates, obtain the initial GIS coordinates of each device after power-on through single-point positioning, and perform the system spatial deviation field detection task by using the collaborative UAV track spatiotemporal interpolation comparison method, and output the deviation prior constraints of the initial coordinates of each device.

[0065] The initial GIS coordinates in this step refer to the position results obtained by standard single-point positioning using broadcast ephemeris and single-frequency pseudorange after each device is powered on and the satellite positioning module is activated. The error of such coordinates is usually on the order of meters, and direct use for target rendezvous will produce significant deviations. The collaborative UAV is equipped with a dynamic differential positioning module, adopts an antenna RTK and inertial navigation combined navigation system, and uses RTK differential correction data from a network of four continuously operating reference stations set up at the boundary of the defense area for positioning. This allows the collaborative UAV to traverse the defense area at a fixed altitude and speed along the intersecting grid flight path, and collect a three-dimensional real track sequence at a frequency of not less than 10Hz during flight. The collaborative control center aligns the UAV tracks detected by each device with the three-dimensional real track sequence at the same time by timestamp interpolation, calculates the overall spatial offset vector of the track points detected by each device, and outputs this overall spatial offset vector as the deviation prior constraint of the initial GIS coordinates of the corresponding device to step S30.

[0066] By utilizing the continuous 3D real-world flight path sequence provided by collaborative UAVs, a dynamic reference trajectory that can be repeatedly compared is essentially deployed in the 3D space of the defense area. Although the overall shift of the detected target points caused by the initial GIS coordinate deviation of each device is due to this deviation, the local relative detection error is relatively small. By aligning the points using timestamp interpolation and calculating the spatial difference for the same flight path point, the systematic coordinate deviation can be separated from random noise, resulting in a stable prior constraint on the deviation. This constraint limits the approximate direction and magnitude of the device coordinate corrections, allowing the joint adjustment of distance observations and clock error parameters in step S30 to be performed within a small and reasonable search range. This avoids divergence when there are differences in the condition numbers of the normal equations or when there are local optima, thus improving the stability and convergence speed of the device coordinate calibration.

[0067] Traditionally, obtaining equipment coordinate deviations involves manually carrying an RTK rover to the equipment installation point for measurement. However, many devices are installed on tower tops, rooftops, or steep slopes, making them difficult for personnel to access. Even when accessible, centering errors and antenna phase center consistency issues exist, and point-by-point measurements are extremely time-consuming, making implementation impractical when there are many devices or frequent location adjustments. Another method relies on long-term averaging of the equipment's own statistical positioning results. Due to multipath effects and satellite geometric variations, the stability of the average positioning results is poor, making it difficult to obtain accurate systematic deviations. This step involves collecting a three-dimensional real-world flight path sequence through a collaborative UAV flight and automatically calculating the overall spatial offset vector of all devices using spatiotemporal interpolation comparison. The process requires no personnel contact with the equipment, and the output deviation prior constraints directly support subsequent joint adjustment.

[0068] For example, in the defense area of ​​the previous example, the initial GIS coordinates reported by the 8 radars and 6 detection devices after powering on have a system offset ranging from 4 to 9 meters due to obstruction by buildings near each installation point and single-point positioning errors. The control center launches a collaborative UAV equipped with a dynamic differential positioning module, planning a cross-grid flight path interval of 200 meters, maintaining a flight altitude of 120 meters, a speed of 10 meters / second, and collecting a three-dimensional real track sequence at a frequency of no less than 10 Hz. For one of the radars installed on the top of the tower, the control center aligns the time series of the UAV points detected by it to the same moment of the collaborative UAV's real track sequence through linear interpolation. It is found that the UAV detected by this radar is offset by about 7.1 meters in the horizontal direction to the southeast and about 3.6 meters in the vertical direction to the lower position. Therefore, the overall spatial offset vector is calculated. This vector is passed to step S30 as a priori constraint of the initial GIS coordinates of this radar to limit the initial search range of the coordinate correction.

[0069] Step S30: Based on the prior constraints of the deviation of the initial coordinates of each device, obtain the arrival time of the calibration signal of the set of devices participating in the calibration and the combination of reference points, and perform the device coordinate fine calibration task by using the joint adjustment method of distance observation and clock error parameters, and output the finely calibrated GIS coordinates and equivalent clock error parameters of each device;

[0070] In this step, the set of devices participating in the calibration refers to all detection nodes and interference-induced nodes within the defense area whose coordinates require precise calibration. The set of reference points consists of the mapping reference points whose high-precision coordinates have been obtained in step S10. The arrival time of the calibration signal is determined by scheduling each device to transmit a calibration signal modulated by radio frequency spreading code in a time-division multiple access manner. The receiving devices and reference points use a high-precision time measurement unit to record the arrival time of the signal's leading edge, which is then converted to obtain the device's calibration signal arrival time. With reference point Distance observation between and equipment With equipment Distance observation between The defined state vector includes the 3D coordinate corrections for each device. , , and equivalent clock error parameters in meters The observation equation is based on the initial coordinates. Known coordinates of the reference point Build as and ,in For coordinate correction vectors, and To mitigate observation noise, all observation equations are linearized using a first-order Taylor expansion at the initial coordinates, and the reference point coordinates are treated as having a weight greater than [missing information]. The virtual observations are used to construct the normal equations, which are then solved using robust weighted least squares iteration with equivalent weight functions. The weight functions are based on the standardized residuals. Segmented processing: When When the original rights are retained; when When using a weighting factor To reduce the weight, among which , ;when At this point, the weight of the observation drops to zero. After iterating until the norm of the coordinate correction is less than 0.01 meters, the precise GIS coordinates and equivalent clock error parameters of each device are output. The latter is directly provided to step S40 as a pre-compensation amount for clock synchronization.

[0071] By jointly adjusting distance observations and equivalent clock bias parameters, this step simultaneously solves for the 3D spatial coordinate deviations of each device and the equivalent time delay deviations of the transceiver channels within a single model, avoiding the parameter coupling errors introduced by separately handling clock synchronization and coordinate calibration. The clock bias term is explicitly incorporated into the observation equations, ensuring that even if pseudorange deviations caused by clock asynchrony exist in the distance observations between devices and the reference point, and between devices themselves, they can be included as unknowns in the adjustment, thus recovering the true geometric distance. A robust weighted least squares strategy is introduced to automatically detect and suppress anomalous observations caused by non-line-of-sight or multipath reflections during the iteration process, preventing a few gross errors from contaminating the entire solution. The converged, precisely calibrated GIS coordinates and equivalent clock bias parameters are output together, providing initial calibration values ​​and coordinate references for the network-wide device clock synchronization in step S40.

[0072] Traditional equipment coordinate calibration often uses intersection positioning or point-by-point RTK measurement, typically assuming that the clocks of all devices are fully synchronized, or only using simplified time compensation afterward. However, in cross-regional networking, the crystal oscillators of each device have different aging processes, and the initial clock difference can reach microseconds to milliseconds. Directly ignoring this clock difference and performing pure distance intersection will cause the clock difference to be incorrectly absorbed into the coordinate correction values, resulting in system coordinate offset. When these erroneous coordinates are used for subsequent target positioning, they will, together with the residual clock difference, cause a secondary amplification of pseudorange error. In addition, traditional intersection adjustment often uses conventional least squares, which is sensitive to gross errors and is prone to divergence in the presence of obstruction or random interference pulses. This step uses joint adjustment of distance observations and clock difference parameters to explicitly estimate the equivalent clock difference parameters in the model, and uses equivalent weight functions to resist gross errors, making the equipment coordinate calibration results stable and repeatable.

[0073] For example, in a coastal defense system, 14 devices participate in joint adjustment. The control center schedules each device to send calibration signals modulated by radio frequency spreading codes sequentially via time-division multiple access. Four reference points and the remaining devices synchronously record the arrival time of the calibration signals and convert them into distance observations between the devices and reference points, as well as between the devices themselves. Taking a detection device installed on a lighthouse as an example, its initial GIS coordinate deviation prior constraint is an offset of approximately 6.2 meters in the northwest direction. The initial value of the coordinate correction in the state vector is set to zero, and the constraint search range is approximately ±1.5 meters. After the first iteration, it is found that the standardized residual of the distance observation between this device and another device reaches 3.6. The function reduces its weight to zero. Analysis shows that this path is severely multipathed due to container accumulation in the port area. After removing this observation, the coordinate correction norm is reduced to 0.11 meters in the second iteration and to 0.007 meters in the third iteration, reaching convergence. Finally, the precisely calibrated GIS coordinates and equivalent clock error parameters of the device are output.

[0074] Step S40: Based on the precise GIS coordinates of each device and the equivalent clock difference parameters, the clock synchronization task of all network devices is performed using a complementary clock calibration method of satellite common view and two-way comparison, and a probe data packet labeled with a unified nanosecond-level timestamp and precise GIS coordinates is output.

[0075] The common-view time difference in this step is achieved by having device A and device B simultaneously receive the same second pulse from at least three common-view satellites in the zenith direction within a unified preset period, and recording their respective local arrival times. and The satellite-to-ground propagation time was calculated based on precise ephemeris, the Klobuchar ionospheric model, and the Saastamoinen tropospheric model. and ,according to The calculated time difference between the two devices. The two-way time difference comparison is performed through the devices. With equipment They exchange timestamp messages using a 5.8GHz directional antenna to record the transmission time. Reception time Response time and final receiving time Under the assumption of channel symmetry, calculate the one-way link delay. Thus, the time difference for bidirectional comparison can be obtained. A Kalman filter is established with the actual clock difference, frequency offset, and common-line link offset between the two devices as state vectors. The common-line time difference and bidirectional comparison time difference are used as observations for fusion filtering, and the time difference calibration is dynamically estimated and distributed level by level. The calibration is transmitted level by level to all slave devices through a tree topology with the device equipped with a rubidium atomic clock as the main reference. The slave devices use voltage-controlled crystal oscillators or digital phase compensation to perform physical layer clock calibration, and each frame of probe data is labeled with a unified nanosecond-level timestamp. At the same time, the precise GIS coordinates output in step S30 are written into the data packet, so that the time synchronization accuracy between devices in the entire network is better than 5 nanoseconds. These probe data packets will serve as the spatiotemporal alignment basis for multi-source point spatial registration and weighted least squares intersection solution in step S50.

[0076] The complementary clock calibration method combining satellite common-view and two-way comparison overcomes the inherent limitations of each approach. Satellite common-view utilizes a common satellite clock source to effectively eliminate satellite clock and ephemeris errors, but its accuracy is limited by satellite visibility conditions and antenna directional gain. Two-way comparison directly measures the clock difference between devices by exchanging timestamp messages via directional wireless links, achieving high short-term stability, but it is susceptible to fluctuations in the wireless propagation environment in cross-regional networking. This step fuses and filters the common-view time difference and the two-way comparison time difference as observations, dynamically estimates and progressively distributes the time difference calibration, and pre-compensates using the equivalent clock difference parameters output in step S30. After physical layer calibration, the clock deviations between devices are compressed to the nanosecond level. As a result, the detection data recorded by different devices for the same target can be strictly aligned under a unified nanosecond-level timestamp, and the corresponding device's precisely calibrated GIS coordinates are carried with the data packet, providing a spatiotemporally consistent data foundation for the spatial registration of points in step S50.

[0077] In traditional UAV defense system networking, devices typically use network time protocols or second pulse synchronization, with time synchronization accuracy generally ranging from milliseconds to hundreds of microseconds. Assuming a target flight speed of 100 meters per second, a 1-millisecond time deviation will result in a 0.1-meter spatial misalignment. However, due to crystal oscillator frequency drift and network latency jitter, the time deviation between devices can accumulate to tens of milliseconds, causing the same target's traces on different devices to be misaligned by several meters or even tens of meters on the GIS base map. This easily leads to errors in track association and false tracks. This step uses complementary calibration through satellite common-view and two-way comparison, and utilizes progressively distributed time difference calibration quantities to perform physical layer calibration on the device clocks. This ensures that the detection data packets output by all network devices simultaneously carry a unified nanosecond-level timestamp and precisely calibrated GIS coordinates, thereby guaranteeing the reliability of subsequent multi-source fusion positioning and coordinated interference induction.

[0078] For example, in the sample system, a radar equipped with a high-stability crystal oscillator clock module is selected as the main reference for the entire network. Within a unified preset period, device A and device B simultaneously receive the same second pulse from at least three common-view satellites in the zenith direction, record their respective local arrival times, and calculate the propagation delay by combining precise ephemeris, ionospheric models, and tropospheric models to obtain the common-view time difference between the two devices. Within the same period, device A and device B exchange timestamp messages through a directional wireless link, recording the transmission, reception, retransmission, and final reception times, and calculate the estimated two-way link propagation delay and the two-way comparison time difference. A Kalman filter fuses the two types of observations to estimate the true time difference, and the calibration value is distributed step by step to the voltage-controlled crystal oscillator or digital phase compensation unit of each device. Subsequently, each frame of detection data is labeled with a unified nanosecond-level timestamp and simultaneously written to the corresponding device's precise GIS coordinates.

[0079] Step S50: Based on the detection data packet labeled with a unified nanosecond-level timestamp and precise GIS coordinates, perform multi-source point spatial registration and weighted least squares intersection calculation, and output the beam pointing angle and signal transmission delay compensation of each interference-inducing device.

[0080] In this step, the registration point set refers to the spatiotemporally consistent dataset formed by unifying the probe data packets uploaded by each probe node, which are labeled with unified nanosecond-level timestamps and precise GIS coordinates, to the GIS base coordinate system through coordinate transformation, and then aligning them using unified nanosecond-level timestamps. Subsequently, the registration point set is associated with points and tracked, and an observation equation system is constructed with the target's 3D coordinates as unknowns. Weights are assigned to each point based on its signal-to-noise ratio or confidence level, and the target's 3D position is calculated using weighted least squares. Based on the calculated target's 3D position and the precise GIS coordinates of each interference-induced device, the horizontal projection direction and vertical height difference of the target relative to each interference-induced device are determined, and the beam azimuth and beam elevation angles are calculated. Then, based on the spatial propagation distance between the target's 3D position and the precise GIS coordinates of each interference-induced device, combined with a preset reference propagation distance and radio wave propagation speed, the signal transmission delay compensation for each interference-induced device is calculated, and finally, the beam pointing angle and signal transmission delay compensation are output.

[0081] After multi-source point registration and weighted least squares intersection calculation, target detection points from different geographical locations and different types of sensors are first precisely spatiotemporally aligned under a unified GIS base coordinate system and a unified nanosecond-level timestamp, eliminating registration errors previously caused by inaccurate device coordinates and clock asynchrony. During weighted least squares positioning, weights are allocated based on signal-to-noise ratio or confidence level, allowing high-quality points to play a dominant role in the positioning calculation and suppressing the bias of low-quality or interfered points in the target's 3D position estimation. Based on this target's 3D position, and in conjunction with the precisely calibrated GIS coordinates of each interference-induced device, the beam pointing angle and signal transmission delay compensation are calculated. This ensures that each interference-induced signal forms a predetermined spatiotemporal coordination relationship in the target's airspace, avoiding beam pointing deviation and time mismatch caused by spatiotemporal reference errors in traditional methods.

[0082] In traditional methods, target locations are typically determined by directly intersecting the individual device coordinates with local timestamps. However, due to potential errors of several meters to tens of meters in device coordinates, and clock deviations exceeding milliseconds, the fused target location often exhibits significant spatial dispersion, sometimes even creating the illusion of multiple targets. Furthermore, traditional cooperative interference command generation often ignores the differences in propagation distances from different devices to the target, or uses only fixed delay compensation without accurately calculating the real-time propagation time of each device. When the distance difference between devices and the target is large, it can lead to undesirable time differences when the interference signal arrives at the target, weakening the cooperative interference effect. This step uses probe data packets labeled with uniform nanosecond-level timestamps and precisely calibrated GIS coordinates to complete point registration and weighted intersection, compressing the target positioning error to a smaller range. It also calculates the signal transmission delay compensation for each interference-inducing device, ensuring the accuracy of beam pointing and timing coordination in cooperative interference induction.

[0083] For example, in a defense mission, three radars and two radio detection devices simultaneously detected an intruding quadcopter drone. The detection data packets uploaded by each device carried a unified nanosecond-level timestamp and precise GIS coordinates. The control center converted the five tracking points to a unified GIS base coordinate system and, after time alignment according to the unified nanosecond-level timestamps, confirmed that these tracking points belonged to the same target and formed a registered tracking point set. After assigning weights based on the signal-to-noise ratio or confidence level of each tracking point, the weighted least squares method was used to calculate the target's three-dimensional position. Subsequently, based on the target's three-dimensional position and the precise GIS coordinates of the second and fifth jamming-induced devices, the control center calculated the beam azimuth, beam elevation, and spatial propagation distance, and combined this with a preset reference propagation distance and radio wave propagation speed to obtain the signal transmission delay compensation. If the spatial propagation delay from device 2 to the target is approximately 1749 nanoseconds and that of device 5 is approximately 994 nanoseconds, then a compensation delay of approximately 755 nanoseconds can be set for device 2 to ensure that the two interference signals coordinate in the target airspace according to a predetermined timing sequence.

[0084] Example 2: Furthermore, the present invention provides a GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction control system, which employs a GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction control method from the above embodiments. This solves the technical problem of GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction control. The beneficial effects of the GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction control system provided by the present invention are the same as those of the GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction control method from the above embodiments. Other technical features of the GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction control system are the same as those disclosed in the above embodiments, and will not be repeated here.

[0085] Example 3: This invention provides a GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control device. This device includes at least one processor and a memory communicatively connected to the processor. The memory stores instructions executable by the processor, which are then executed to enable the processor to perform the GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control method described in Example 1. The GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control device in this invention can include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle-mounted terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. This GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control device is merely an example and should not limit the functionality or scope of this invention. A GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative jamming and induction control device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) that can execute various appropriate actions and processes according to a program stored in read-only memory or a program loaded from a storage device into random access memory. The random access memory also stores various programs and data required for the operation of the GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative jamming and induction control device. The processing unit, read-only memory, and random access memory are interconnected via a bus. I / O interfaces are also connected to the bus. Typically, the following systems can be connected to the I / O interface: input devices including touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices including liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices including magnetic tapes, hard disks, etc.; and communication devices. The communication device allows the GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative jamming and induction control device to exchange data with other devices wirelessly or via wired communication. While a multi-device, cross-regional, and cross-level networked collaborative interference-inducing control device for drone defense based on a GIS platform has been described, it should be understood that implementation of all the systems described is not required.It can be implemented alternatively or with more or fewer systems.

[0086] Example 4: This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for cross-regional and cross-level networked collaborative interference induction and control of unmanned aerial vehicles (UAVs) based on a GIS-based architecture. The computer program product provided by this invention can solve the technical problem of cross-regional and cross-level networked collaborative interference induction and control of UAVs based on a GIS-based architecture. Compared with the prior art, the beneficial effects of the computer program product provided by this invention are the same as those of the above-described method for cross-regional and cross-level networked collaborative interference induction and control of UAVs based on a GIS-based architecture, and will not be repeated here.

[0087] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a read-only memory. When the computer program is executed by a processing device, it performs the functions defined in the methods of the embodiments disclosed in this invention.

[0088] It should be understood that the various parts disclosed in this invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0089] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the present invention and its equivalents, the present invention also intends to include these modifications and variations.

Claims

1. A method for inducing and controlling cross-regional and cross-level networked collaborative interference in UAV defense based on a GIS platform, characterized in that, The methods include: Step S10: Obtain 3D geographic information data of the defense area and registration information of defense equipment nodes; perform GIS base construction task based on CGCS2000 ellipsoidal coordinate frame and surveying benchmark anchoring method; output unified spatial reference system and high-precision benchmark coordinates. Step S20: Based on the unified spatial reference system and the high-precision reference point coordinates, obtain the initial GIS coordinates of each device after power-on through single-point positioning, perform the system spatial deviation field detection task using a cooperative UAV trajectory spatiotemporal interpolation comparison method, and output the deviation prior constraints of the initial coordinates of each device; Step S20 specifically includes: Step S201: After each device is powered on, the satellite positioning module is started, and standard single-point positioning is performed using broadcast ephemeris and single-frequency pseudorange to obtain initial GIS coordinates. The initial GIS coordinates are then uploaded to the collaborative control center via wired or wireless link. Step S202: The dynamic differential positioning module is mounted on the collaborative drone, which traverses the defense area at a fixed altitude and speed along the intersecting grid route, and collects a three-dimensional real track sequence at a frequency of not less than 10Hz during the flight. Step S203: Obtain the detection tracks formed by each device for the collaborative UAV, align the detection tracks with the three-dimensional real track sequence by timestamp interpolation, calculate the overall spatial offset vector of the detection track points of each device, and use the overall spatial offset vector as the deviation prior constraint of the initial GIS coordinates of the corresponding device. Step S30: Based on the prior constraints of the initial coordinate deviations of each device, obtain the arrival time of the calibration signal of the set of devices participating in the calibration and the combination of reference points. Perform the device coordinate fine calibration task using a joint adjustment method of distance observations and clock error parameters, and output the finely calibrated GIS coordinates and equivalent clock error parameters of each device. Step S30 specifically includes: Step S301: The collaborative control center schedules each device to send a calibration signal modulated by radio frequency spread spectrum code in a time division multiple access manner. The device and the reference point in the receiving state record the arrival time of the calibration signal and convert the arrival time into a distance observation between the device and the reference point and a distance observation between the devices. Step S302: Define the initial coordinates of device i as follows The coordinate correction vector of device i is The high-precision coordinates of reference point j are: The initial coordinates of device k are The coordinate correction vector for device k is The equivalent clock error parameter of device i, expressed in terms of distance, is: The equivalent clock error parameter of device k, expressed in terms of distance, is: The distance between device i and reference point j is measured as follows: The observed distance between device i and device k is The observation error between device i and reference point j is The observation error between device i and device k is The following observation equation is established: in, Indicates Euclidean distance; Step S303: Perform a first-order Taylor expansion and linearize all observation equations at the initial coordinates of each device. Use the prior deviation constraints of the initial coordinates of each device as the initial range and regularization constraints of the coordinate correction vector, and use the high-precision reference point coordinates as the weights higher than... The virtual observations are combined into a normal equation, which is then solved using robust weighted least squares iteration with equivalent weight functions; where m represents the observation number. This represents the standardized residual of the m-th observation. This represents the equivalent weight of the m-th observation. Denotes the constant of the first piecewise segment. Represents the second piecewise constant; when When, maintain the original weight of the m-th observation; when When the m-th observation is reduced, the equivalent weight is reduced according to the following formula: when When the m-th observation is set to zero, the equivalent weight of the m-th observation is reset to zero; where, , Iterate until the Euclidean norm of the coordinate correction component in the state vector composed of each coordinate correction vector and each equivalent clock error parameter is less than 0.01 meters, and output the precise GIS coordinates and equivalent clock error parameters of each device; Step S40: Based on the precise GIS coordinates of each device and the equivalent clock difference parameters, a network-wide device clock synchronization task is performed using a complementary clock calibration method combining satellite common-view and two-way comparison, outputting a probe data packet labeled with a unified nanosecond-level timestamp and precise GIS coordinates; Step S40 specifically includes: Step S401: Each device simultaneously receives the same second pulse from at least three common-view satellites in the zenith direction within a unified preset period, and records the local arrival time of device A. and the local arrival time of device B The propagation delay of device A, calculated based on precise ephemeris, ionospheric models, and tropospheric models, is also considered. and the propagation delay of device B Calculate the common-view time difference between device A and device B. : Step S402: Within the unified preset period, device A and device B exchange timestamp messages via a directional wireless link to record the sending time of device A. Device B receiving time Device B's sendback time and the final receiving time of device A Calculate the estimated propagation delay of the bidirectional link between device A and device B. : And calculate the bidirectional comparison time difference between device A and device B based on the estimated bidirectional link propagation delay. : Step S403: Establish a Kalman filter with the inter-device clock difference and frequency offset as state variables, fuse and filter the common-view time difference and the bidirectional comparison time difference as observations, dynamically estimate and distribute the time difference calibration amount step by step, perform physical layer calibration of the clock of each device through voltage-controlled crystal oscillator calibration and digital phase compensation, and mark each frame of detection data with a unified nanosecond-level timestamp according to the calibrated clock, while writing the corresponding device's precise GIS coordinates into the data packet, and output the detection data packet marked with the unified nanosecond-level timestamp and precise GIS coordinates; Step S50: Based on the detection data packet labeled with a unified nanosecond-level timestamp and precise GIS coordinates, perform multi-source point spatial registration and weighted least squares intersection calculation, and output the beam pointing angle and signal transmission delay compensation of each interference-induced device; Step S50 specifically includes: Step S501: Based on the detection data packet labeled with a unified nanosecond-level timestamp and precise GIS coordinates, perform spatial registration on the multi-source target points uploaded by each detection node, convert the multi-source target points to a unified GIS base coordinate system, and use the unified nanosecond-level timestamp to complete time alignment, and output the registered point set. Step S502: Perform point association and track initiation on the registered point set, construct an observation equation set with the target's three-dimensional coordinates as unknowns, assign weights according to the signal-to-noise ratio or confidence level of each point, and use the weighted least squares method to solve the target's three-dimensional position. Step S503: Based on the target's three-dimensional position and the precise GIS coordinates of each interference-inducing device, determine the horizontal projection direction and vertical height difference of the target relative to each interference-inducing device. Calculate the beam azimuth angle of each interference-inducing device pointing towards the target based on the horizontal projection direction. Calculate the beam elevation angle of each interference-inducing device pointing towards the target based on the vertical height difference and the horizontal projection distance. Based on the spatial propagation distance between the target's three-dimensional position and the precise GIS coordinates of each interference-inducing device, combined with a preset reference propagation distance and the radio wave propagation speed, calculate the signal transmission delay compensation amount of each interference-inducing device. Output the beam pointing angle and signal transmission delay compensation amount of each interference-inducing device.

2. The method for multi-device cross-regional and cross-level networked collaborative interference induction and control of unmanned aerial vehicles (UAVs) based on a GIS platform as described in claim 1, characterized in that, Step S10 involves acquiring 3D geographic information data of the defense area and registration information of defense equipment nodes, performing a GIS base construction task based on the CGCS2000 ellipsoidal coordinate frame and the mapping benchmark anchoring method, and outputting a unified spatial reference system and high-precision benchmark coordinates. Specifically, this includes: Step S101: Acquire digital elevation model, orthophoto and 3D building model, and use CGCS2000 ellipsoidal coordinate system as unified spatial reference system to generate GIS base containing 3D landform, building outline and height attributes. Step S102: Select multiple mapping coordinates as reference points within the defense area covered by the GIS base. Each reference point is continuously and statically observed for at least 72 hours using BeiDou and GPS dual-frequency signals. The observation data is then connected to the National Geodetic Control Network for static precise single-point positioning calculation to obtain high-precision reference point coordinates with a mean error of better than 0.02 meters for the plane coordinate component and a mean error of better than 0.03 meters for the elevation component. Step S103: The high-precision reference point coordinates are pre-set as absolute constraints on the GIS base, and according to the defense equipment node registration information, the detection equipment and interference inducement equipment are respectively configured with satellite positioning module, broadband collaborative signal transceiver front end, high-stability crystal oscillator clock module and wired and wireless dual-link communication module to construct a cross-regional and cross-level dual-channel networking structure.

3. The method for multi-device cross-regional and cross-level networked collaborative interference induction and control of unmanned aerial vehicles (UAVs) based on a GIS platform as described in claim 1, characterized in that, In step S202, the dynamic differential positioning module adopts an antenna RTK and inertial navigation combined navigation system. It uses RTK differential correction data from a network of four continuously operating reference stations set up at the boundary of the defense area to achieve positioning, so that the real-time positioning accuracy of the cooperative UAV meets the requirements of no more than 2 meters in the horizontal direction and no more than 3 meters in the vertical direction.

4. A GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control system, applied to the GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control method as described in any one of claims 1 to 3, characterized in that, The system includes: The GIS base construction module is used to acquire three-dimensional geographic information data of the defense area and registration information of defense equipment nodes. Based on the CGCS2000 ellipsoidal coordinate frame and the surveying benchmark anchoring method, it performs the GIS base construction task and outputs a unified spatial reference system and high-precision benchmark coordinates. The spatial deviation field detection module is used to obtain the initial GIS coordinates of each device after power-on through single-point positioning based on the unified spatial reference system and the high-precision reference point coordinates. It performs the system spatial deviation field detection task by using a collaborative UAV track spatiotemporal interpolation comparison method and outputs the deviation prior constraints of the initial coordinates of each device. The equipment coordinate fine calibration module is used to obtain the arrival time of the calibration signal of the set of equipment participating in the calibration and the combination of reference points based on the a priori constraints of the deviation of the initial coordinates of each equipment, and to perform the equipment coordinate fine calibration task by using a joint adjustment method of distance observation and clock error parameters, and output the fine calibrated GIS coordinates and equivalent clock error parameters of each equipment. The network-wide clock synchronization module is used to perform the network-wide device clock synchronization task based on the precision-calibrated GIS coordinates of each device and the equivalent clock difference parameters, using a complementary clock calibration method of satellite common view and two-way comparison, and outputs a probe data packet labeled with a unified nanosecond-level timestamp and precision-calibrated GIS coordinates; The collaborative interference-induced solution module is used to perform multi-source point spatial registration and weighted least squares intersection solution based on the detection data packets labeled with unified nanosecond-level timestamps and precise GIS coordinates, and output the beam pointing angle and signal transmission delay compensation of each interference-induced device.

5. A multi-device cross-regional and cross-level networked collaborative interference induction and control device for UAV defense based on a GIS platform, characterized in that, The GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control device includes: a memory, a processor, and a GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control program stored in the memory and executable on the processor. When the GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control program is executed by the processor, it implements the GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control method according to any one of claims 1 to 3.

6. A computer program product, characterized in that, The computer program product includes a GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control program. When the GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control program is executed by the processor, it implements the GIS-based UAV defense multi-device cross-regional and cross-level networked collaborative interference induction and control method according to any one of claims 1 to 3.

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