A method for signal detection and accurate positioning of a high-altitude platform unmanned aerial vehicle

Abstract

The present application relates to a kind of unmanned aerial vehicle signal detection and accurate positioning method of ascending platform, deploy unmanned aerial vehicle platform, real-time acquisition global electromagnetic signal and output multi-channel original signal data and carry out time-frequency domain joint analysis, extract frequency hopping period, modulation mode and pseudo-random code feature, identify target unmanned aerial vehicle signal, and separate out control instruction link and signal of picture transmission link;Establish the time difference of arrival and the phase difference of arrival of target unmanned aerial vehicle signal are converted to dynamic space coordinate system, construct three-dimensional positioning equation set, and the real-time spatial position coordinates of target unmanned aerial vehicle are obtained by solving;Extract the feature parameter of the emission end of control instruction link signal, construct control signal propagation path model in combination with the real-time position of target unmanned aerial vehicle, and the geographic coordinates of unmanned aerial vehicle control source are positioned by reverse tracking signal emission direction and attenuation law.Effectively avoid the influence of terrain shelter, improve target identification accuracy and positioning accuracy, while realizing unmanned aerial vehicle control source tracing.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of unmanned aerial vehicle signal detection, and particularly relates to a method for unmanned aerial vehicle signal detection and accurate positioning of an airborne platform. BACKGROUND

[0002] The existing unmanned aerial vehicle signal detection and positioning technology mainly includes detection and positioning methods based on radio frequency signals, detection and positioning methods based on vision, and detection and positioning methods based on acoustics. The existing unmanned aerial vehicle signal detection and positioning technology is as follows:

[0003] Radar detection technology: Radar detects unmanned aerial vehicles by emitting electromagnetic waves and receiving echoes reflected by targets. When the electromagnetic waves emitted by the radar encounter unmanned aerial vehicles, part of the electromagnetic waves will be reflected back to the radar, and the radar determines the distance, speed and direction of the unmanned aerial vehicles according to the time delay, frequency change and other parameters of the echoes. However, due to the reflection and scattering of buildings and other objects in urban environments, a large amount of clutter interference is generated, which affects the accurate identification and positioning of unmanned aerial vehicle signals; at the same time, radar equipment is usually large in size, high in cost, and complex in deployment and maintenance.

[0004] Sound wave recognition technology: uses the sound wave characteristics generated by the unmanned aerial vehicle during flight for detection and positioning. The propeller rotation and motor operation of the unmanned aerial vehicle during flight will generate sound waves with specific frequencies and characteristics. By arranging multiple microphone arrays, the sound wave signals in the environment are collected, and then these signals are analyzed and processed to extract the sound wave characteristics related to the unmanned aerial vehicle, so as to determine the existence and position of the unmanned aerial vehicle. However, sound wave recognition technology is easily affected by environmental noise. In noisy environments, environmental noise may mask the sound wave signals generated by the unmanned aerial vehicle, resulting in a decrease in the accuracy of detection and positioning; moreover, sound waves are affected by distance and obstacles during propagation, and the propagation distance is limited, so the detection ability for long-distance unmanned aerial vehicles is weak.

[0005] Radio reception detection technology: uses spectrum analyzers and other devices to monitor and analyze the radio signals in the air, identifies the frequency, modulation method, signal strength and other characteristics of the unmanned aerial vehicle communication signals, and determines the existence and position of the unmanned aerial vehicle. However, in complex electromagnetic environments such as city centers, near communication base stations, etc., there are a large number of other radio signal interferences, which easily lead to misjudgment and missed judgment of the unmanned aerial vehicle signals; in addition, some advanced unmanned aerial vehicles may use encryption communication technology, increasing the difficulty of signal identification and cracking.

[0006] Infrared detection technology: During flight, the engine, motor and other components of the UAV will generate heat and emit infrared radiation. The infrared detector captures these infrared radiation signals to detect the presence and location of the UAV. However, the detection distance of the infrared detection technology is relatively short, and is greatly affected by the weather. In rainy, foggy and other bad weather conditions, the propagation of the infrared signal will be severely attenuated, resulting in a decline in detection effect. At the same time, it has limited recognition ability for UAVs, and it is difficult to accurately distinguish different types of UAVs.

[0007] Therefore, the present application aims to construct a three-dimensional detection network to realize the global three-dimensional capture of UAV signals, overcome the problem that signals are easily disturbed in complex environments, and improve the reliability of signal detection. SUMMARY

[0008] The present application aims to provide a UAV signal detection and accurate positioning method for an airborne platform, which solves the technical problems existing in the prior art.

[0009] To solve the above technical problems, the technical solution adopted by the present application is as follows:

[0010] A UAV signal detection and accurate positioning method for an airborne platform, comprising the following steps:

[0011] S1: Deploying multiple UAV platforms carrying detection equipment, the detection equipment comprising multiple sub-array multi-band array antennas, which capture real-time global electromagnetic signals and output multi-channel raw signal data;

[0012] S2: Jointly analyzing the multi-channel raw signal in time and frequency domains, extracting the frequency hopping period, modulation mode and pseudo-random code features of the signal, matching with the preset UAV signal feature library to identify the target UAV signal, and separating the control instruction link and the image transmission link signal of the target signal;

[0013] S3: Establishing a dynamic space coordinate system, converting the time difference of arrival (TDoA) and phase difference of arrival (PDoA) of the identified target UAV signal between multiple sub-arrays to the coordinate system, constructing a three-dimensional positioning equation set, and solving to obtain the real-time spatial position coordinates of the target UAV;

[0014] S4: For the control instruction link signal, extracting its transmission end feature parameters, combining the real-time spatial position coordinates of the target UAV, establishing a control signal propagation path model, and locating the geographic coordinates of the UAV control source by reverse tracking the signal transmission direction and attenuation law.

[0015] Preferably, the specific process of step S1 is as follows:

[0016] S11: Launching platform deployment: Launch the airborne equipment equipped with multi-band adaptive array antenna to the preset height, complete the platform attitude calibration through the ground control system, and complete the initial spatial coordinate anchoring based on GNSS positioning data;

[0017] S12: Array antenna start and frequency band configuration: Activate the multi-band adaptive array antenna, which contains 3 or more sub-arrays, to realize seamless coverage of 1.2-6GHz frequency band through frequency band overlap design between sub-arrays;

[0018] S13: Beamforming cooperation: Each sub-array independently generates controllable beams through phased array technology, and the ground control unit dynamically adjusts the beam pointing angle and gain distribution between sub-arrays based on real-time electromagnetic environment monitoring data;

[0019] S14: Signal acquisition and multi-channel output: The array antenna identifies the signal coming direction through spatial spectrum estimation, drives the corresponding sub-array beam to focus on the signal direction to enhance the receiving gain, and outputs to the signal processing unit in real time according to the sub-array channel number, forming a multi-channel original data set containing time stamp, signal strength and phase information.

[0020] Preferably, the time-frequency domain joint analysis of the multi-channel original signal in step S2 is as follows:

[0021] S20: Multi-channel signal preprocessing: Synchronize and calibrate the multi-channel original digital signal output by each sub-array, and filter out environmental noise and co-frequency interference through wavelet threshold denoising;

[0022] S21: Time-frequency domain joint analysis:

[0023] Perform short-time Fourier transform on the preprocessed signal to generate a time-frequency distribution map, identify the energy distribution characteristics of the signal in the time-frequency plane, and locate the frequency hopping time and corresponding carrier frequency value of the frequency hopping signal;

[0024] For the steady-state signal segment in the time-frequency distribution map, extract the transient feature through continuous wavelet transform, calculate the instantaneous frequency and amplitude combined with Hilbert transform, and determine the frequency hopping period;

[0025] S22: Modulation mode identification:

[0026] For non-frequency hopping signal segments or single-frequency resident segments of frequency hopping signals, extract the amplitude histogram, phase change rate and spectrum correlation characteristics of the signal;

[0027] Match the features through the support vector machine classifier to identify the modulation mode;

[0028] S23: Pseudo-random code feature extraction:

[0029] For the signals identified as spread spectrum modulation, the pseudo-random code period is determined by sliding correlation calculation;

[0030] Based on the signal phase flip point within the code period, the code sequence characteristics are extracted, compared with the common UAV pseudo-random code in the preset feature library, and the code type and code length are determined.

[0031] Preferably, the specific process of matching the extracted features with the preset UAV signal feature library to identify the target UAV signal in step S2 is as follows:

[0032] S24: Standardization processing and feature library calling: standardization processing is performed on the extracted target signal features; a preset UAV signal feature library is called, which contains a reference feature set of known UAV models, and each parameter is associated with a corresponding weight coefficient;

[0033] S25: Multi-dimensional feature matching calculation:

[0034] For frequency hopping period: calculate the deviation rate of the target signal frequency hopping period and the reference period of each type in the feature library, deviation rate = |target value-reference value| / reference value, when the deviation rate≤5%, it is determined as preliminary matching;

[0035] For modulation mode: type matching method is adopted, if the target signal modulation mode is completely consistent with a certain type in the feature library, the score is 100% of the corresponding weight value of this parameter, if it is a compatible type, the score is 60% of the corresponding weight value;

[0036] S26: Matching degree comprehensive evaluation and target recognition: the multi-dimensional matching scores are weighted and summed to calculate the comprehensive matching degree of the target signal and each UAV model in the feature library, and the UAV model is matched according to the comprehensive matching degree.

[0037] Preferably, the specific process of separating the control instruction link and the image transmission link signal of the target signal in step S2 is as follows:

[0038] S27: Pre-set control instruction link and image transmission link distinguishing feature library;

[0039] S28: Signal framing and parameter extraction: frame synchronization processing is performed on the identified target UAV signal, the continuous signal is segmented into independent data frames, and the key parameters of each frame signal are extracted;

[0040] S29: Link type classification and separation: a decision tree classification model is used to determine the link type of the framed signal.

[0041] Preferably, the specific process of establishing a dynamic space coordinate system based on the GNSS real-time positioning data of the ascending platform and the attitude parameters output by the inertial navigation system in step S3 is as follows:

[0042] S31: Reference coordinate anchoring: taking the centroid of the aerial platform as the origin O of the coordinate system, the geodetic coordinates (L, B, H) of the platform are obtained in real time by the GNSS module, L is the longitude, B is the latitude, and H is the altitude, which are converted into absolute coordinates (X0, Y0, Z0) in the WGS-84 geocentric rectangular coordinate system, serving as the spatial anchor point of the dynamic coordinate system;

[0043] Setting the initial pointing of the coordinate system: taking the origin O as the starting point, the vector from the center of the Earth to the O point is the Z axis, the geodetic coordinates are converted into plane coordinates by Gauss projection, the initial eastward axis (X axis) and northward axis (Y axis) are determined, and the initial ENU coordinate system framework is formed;

[0044] S32: Attitude parameter fusion and dynamic coordinate system correction:

[0045] Real-time collection of attitude parameters output by the inertial navigation system (INS), including pitch angle θ, roll angle φ, and heading angle ψ; Construction of a rotation matrix based on attitude angles: according to the Euler angle transformation formula, the initial ENU coordinate system is rotated around the Z axis, Y axis, and X axis in turn to generate a dynamic rotation matrix;

[0046] Real-time correction of the initial ENU coordinate system through the rotation matrix: multiplying the axis vectors (X0, Y0, Z0) of the initial ENU coordinate system by the rotation matrix to obtain the corrected instantaneous coordinate axis pointing direction (X', Y', Z');

[0047] S33: Time synchronization and coordinate updating mechanism:

[0048] Using the PPS signal of GNSS as the time reference, the attitude parameters output by the INS and the GNSS positioning data are synchronized by timestamp;

[0049] Coordinate system update is performed once every specified time: combining the latest GNSS absolute coordinates (X0', Y0', Z0') and INS attitude angles (ψ', θ', φ'), the rotation matrix is recalculated and the coordinate system origin position and axis pointing direction are corrected to compensate for platform drift.

[0050] Preferably, in S3, the time difference of arrival and the phase difference of arrival of the target unmanned aerial vehicle signal between multiple sub-arrays are converted to the coordinate system, and a three-dimensional positioning equation set is constructed to solve the real-time spatial position coordinates of the target unmanned aerial vehicle as follows:

[0051] S34: Multi-subarray spatial coordinate calibration:

[0052] In the established dynamic spatial coordinate system, the relative coordinates of each subarray antenna are calibrated in advance: let the coordinates of subarray i be (xᵢ, yᵢ, zᵢ), i = 1, 2,..., n, n ≥ 3, the physical distance between subarrays is obtained by laser ranging, and converted into three-dimensional coordinate values in the coordinate system to form a subarray position matrix;

[0053] S35: Spatiotemporal synchronization conversion between TDoA and PDoA:

[0054] TDoA Conversion: For target signals received by multiple subarrays, using subarray 1 as a reference, calculate the arrival time difference Δtᵢ1 between the remaining subarrays i and subarray 1. Combining this with the electromagnetic wave propagation speed c, convert it into a distance difference Δdᵢ1 = Δtᵢ1 × c, resulting in the relationship: [(X-xᵢ)² + (Y-yᵢ)² + (Z-zᵢ)²] 1 / 2 -[(X-x1)²+(Y-y1)²+(Z-z1)²] 1 / 2 =Δdᵢ1, where (X, Y, Z) are the coordinates of the target in the dynamic coordinate system to be solved;

[0055] PDoA Conversion: For signals with the same carrier frequency f, calculate the phase difference Δφᵢ1 between subarray i and subarray 1, and convert it into the distance difference Δd'ᵢ1=(Δφᵢ1×λ) / (2π), where λ is the signal wavelength and λ=c / f. Based on the baseline length bᵢ1 between subarrays, the supplementary relationship is obtained: [(X-xᵢ)²+(Y-yᵢ)²+(Z-zᵢ)²] 1 / 2 -[(X-x1)²+(Y-y1)²+(Z-z1)²] 1 / 2 =Δd'ᵢ1+kᵢ1×λ, where kᵢ1 is an integer, and the range of values ​​for kᵢ1 is constrained by the TDoA result.

[0056] S36: Construction and Solution of Three-Dimensional Positioning Equations

[0057] Select three or more subarray combinations and construct a nonlinear equation system containing three unknowns (X, Y, Z) based on the distance difference relationship between TDoA and PDoA conversions mentioned above.

[0058] The nonlinear equation system is linearized using the Taylor series expansion method: taking the initial position estimate of the target as the starting point of the iteration, the Jacobian matrix is ​​calculated and the equation system is transformed into the linear form Ax=b;

[0059] The linear equations are solved by the least squares method to obtain the target coordinate correction. The correction is iteratively updated until the correction is ≤0.1m. The converged (X, Y, Z) is output as the real-time spatial coordinates of the target in the dynamic coordinate system.

[0060] S37: Coordinate system transformation and absolute position output:

[0061] Transform the target coordinates (X, Y, Z) in the dynamic coordinate system to the WGS-84 geocentric rectangular coordinate system: using the rotation matrix R in step S3. -1 The attitude is calculated using the inverse of the rotation matrix to eliminate the influence of the platform attitude on the coordinates and obtain the absolute offset of the target relative to the origin of the launch platform.

[0062] Superimposed GNSS absolute coordinates of the platform (X0, Y0, Z0) of the target unmanned aerial vehicle WGS-84 geodetic coordinates (L, B, H) are obtained, and the signal time difference / phase difference to geographic coordinate is solved and closed.

[0063] Preferably, the specific process of step S4 is as follows:

[0064] S41: Control instruction link transmitting end feature parameter extraction:

[0065] Carrier frequency drift rate extraction: carrier frequency tracking is performed on the control instruction link signal, and the carrier frequency within a specified time is continuously collected. The drift rate of the carrier frequency with time is calculated by linear fitting;

[0066] Power fluctuation period extraction: the signal receiving power is sampled in real time, the power spectral density is analyzed by Fourier transform, the frequency value corresponding to the main peak is identified, and the reciprocal of the frequency value is the power fluctuation period. At the same time, the fluctuation amplitude range is recorded;

[0067] Encoding format analysis: decode the control instruction frame data, extract the frame header structure, check mode, data frame length and bit synchronization mode, and generate the encoding feature code;

[0068] S42: Control signal propagation path model construction:

[0069] Basic path model: taking the solved real-time position coordinates of the unmanned aerial vehicle (X1, Y1, Z1) as the relay point, assuming that the control signal directly reaches the unmanned aerial vehicle from the control source (X0, Y0, Z0), combining the relative position of the unmanned aerial vehicle and the lift-off platform (X p , Y p , Z p ), a two-section propagation model is established: control source→unmanned aerial vehicle→lift-off platform;

[0070] Attenuation law correction: based on the received power P r of the control instruction signal, combining the free space attenuation formula P r =P t -20lg(4πd / λ), Pt is the transmission power, d is the propagation distance, λ is the signal wavelength, and the environmental attenuation coefficient is introduced. The terrain shielding information scanned by the laser radar of the lift-off platform is corrected in real time. The shielding area coefficient is increased by 5-20dB, and the propagation distance d 01 from the control source to the unmanned aerial vehicle is inversely calculated.

[0071] S43: Control source direction reverse tracking:

[0072] Arrival angle calculation: based on the PDoA measurement value of the control instruction signal by the multi-subarray antenna of the aerial platform, combined with dynamic spatial coordinate conversion, the arrival angle of the signal from the unmanned aerial vehicle to the aerial platform is obtained, azimuth angle a1, elevation angle b1;

[0073] Reverse direction derivation: according to the position (X1, Y1, Z1) and motion state (speed, heading) of the unmanned aerial vehicle, the incident direction of the control signal when it reaches the unmanned aerial vehicle is calculated, assuming that the unmanned aerial vehicle is omnidirectional reception, the azimuth angle a0 of the control source relative to the unmanned aerial vehicle is a1+180°±a, and a is the attitude correction amount of the unmanned aerial vehicle; the elevation angle b0 is calculated by the signal incident elevation angle and the altitude difference of the unmanned aerial vehicle;

[0074] Trajectory intersection positioning: taking the position of the unmanned aerial vehicle as the vertex, a ray is constructed along the reverse direction a0, b0, combined with the propagation distance d 01 , the candidate coordinates (X0', Y0', Z0') of the control source at that moment are obtained;

[0075] S44: Multi-time data fusion and coordinate solution:

[0076] Collect 5-10 candidate coordinates of the control source at different times, with a time interval of 0.5-2s, synchronized with the positioning update period of the unmanned aerial vehicle;

[0077] Use Kalman filter to fuse multiple-time candidate coordinates: take the control source moving speed ≤10m / s as the state constraint, dynamically correct the coordinate drift, and get the filtered control source coordinates (X0, Y0, Z0);

[0078] Combined with the altitude constraint of the digital map on the ground, Z0 is usually the ground height ±5m, and the WGS-84 geodetic coordinates of the control source are finally output.

[0079] Preferably, the following processes are also included:

[0080] Deploy multiple unmanned aerial platforms carrying detection equipment, complete dynamic cooperative networking through the preset frequency domain / time domain / code domain joint communication protocol combined with dynamic spectrum sensing technology, and construct a three-dimensional detection network that can avoid building and mountain terrain shielding;

[0081] Through the three-dimensional detection network, the signals of the target unmanned aerial vehicle are captured in a global three-dimensional manner to obtain multi-target signals; the time difference arrival data, frequency difference arrival data of each unmanned aerial platform, and the real-time positioning data generated by the inertial navigation unit carried by each platform are synchronously collected to form a multi-source data set;

[0082] After preprocessing the multi-source data set, the target unmanned aerial vehicle is positioned by a distributed positioning solution method;

[0083] The multi-target signal is introduced into a signal processing unit based on an FPGA hardware acceleration architecture, fast signal sorting is completed through hardware acceleration, frequency hopping / spectrum spreading signal features are extracted, multi-target signal real-time processing data cooperating with positioning results are formed, and subsequent traceability positioning is supported;

[0084] Based on the multi-target signal real-time processing data, the control source of the target unmanned aerial vehicle is identified through signal fingerprint feature matching, multi-unmanned aerial vehicle angle of arrival data of a three-dimensional stereoscopic detection network are called, and geographic space data of a geographic information system are fused, and finally the traceability positioning of the control source is realized.

[0085] The beneficial effects of the present application include:

[0086] 1. By deploying a multi-frequency array antenna of 3 or more sub-arrays, combined with the cooperative networking of multiple unmanned aerial vehicle platforms, a seamless coverage network of 1.2-6GHz unmanned aerial vehicle commonly used frequency bands is constructed, the problem of frequency blind area of traditional single frequency band equipment is solved, and different types of unmanned aerial vehicle control instructions, image transmission and other types of signals can be captured.

[0087] 2. By extracting the frequency hopping period, modulation mode and pseudo-random code features through time-frequency domain joint analysis, combined with multi-dimensional matching, the accurate identification of the unmanned aerial vehicle model is realized. Compared with the traditional single feature identification method, the model identification accuracy is improved, and the unknown signal classification error is reduced.

[0088] 3. A dynamic space coordinate system is established by fusing GNSS and INS, the coordinate compensation platform drift is updated once every 50ms, the coordinate system axis pointing deviation is less than or equal to 0.5°, compared with the fixed coordinate system, the positioning reference error caused by platform tilt and jitter is eliminated, and a stable space framework is provided for TDoA / PDoA conversion. The positioning equation set is strongly constrained, and the solving error is small: combined with the dual constraints of TDoA and PDoA, a three-dimensional nonlinear equation set is constructed, the target unmanned aerial vehicle positioning error is small through Taylor series expansion and least square method iteration, compared with single TDoA positioning, the accuracy is improved; even if the GNSS is temporarily locked, the positioning continuity is maintained through INS track calculation, and the positioning interruption time is shortened to within 0.5s. Distributed positioning calculation realizes parallel processing of multi-target signals, and each platform can complete 10 target positioning updates per second, compared with centralized calculation, the processing efficiency is improved by 3 times, and it is suitable for the scene of multiple unmanned aerial vehicles invading at the same time.

[0089] 4. A two-segment propagation model is established using the UAV's location as a relay point. Combining the free-space attenuation formula and lidar terrain correction, the propagation distance from the control source to the UAV is calculated in reverse. Compared to the model without terrain correction, the accuracy of distance calculation is improved. Through PDoA measurement and reverse direction derivation, a ray is constructed based on the propagation distance to obtain the candidate coordinates of the control source. Then, through Kalman filtering and multi-time fusion, the source tracing error is reduced. Compared to the traditional signal direction of arrival + ground investigation method, the tracing efficiency is effectively improved. Attached Figure Description

[0090] Figure 1 This is a flowchart illustrating the method for signal detection and precise positioning of an unmanned aerial vehicle (UAV) on an airborne platform according to the present invention.

[0091] Figure 2 This is a schematic diagram of the architecture of the multi-band array antenna of the present invention. Detailed Implementation

[0092] The following is in conjunction with the appendix Figures 1-2 The present invention will be further described in detail below:

[0093] Example 1

[0094] See appendix Figure 1 As shown, a method for signal detection and precise positioning of an aerial platform UAV includes the following steps:

[0095] S1: Deploy multiple UAV platforms equipped with detection equipment. The detection equipment includes multi-band array antennas with three or more subarrays. Each subarray covers the commonly used UAV frequency bands from 1.2GHz to 6GHz, and the frequency band overlap between adjacent subarrays is no less than 15%. The spatial spectrum estimation capability is formed through beamforming collaboration between subarrays, and the full-domain electromagnetic signals are captured in real time and multi-channel raw signal data is output.

[0096] S2: Perform joint time-frequency domain analysis on the multi-channel raw signals, extract the frequency hopping period, modulation mode and pseudo-random code features of the signals, match the extracted features with the preset UAV signal feature library to identify the target UAV signals, and separate the control command link and image transmission link signals of the target signals to provide a layered signal source for positioning and tracing.

[0097] S3: Based on the real-time GNSS positioning data of the launch platform and the attitude parameters output by the inertial navigation system, a dynamic spatial coordinate system is established. The time difference of arrival (TDoA) and phase difference of arrival (PDoA) of the identified target UAV signal between multiple subarrays are transformed into this coordinate system. A three-dimensional positioning equation system is constructed and the real-time spatial position coordinates of the target UAV are obtained by solving it.

[0098] S4: For the control command link signal, extract its transmitter characteristic parameters, including carrier frequency drift rate, power fluctuation period and encoding format. Combined with the real-time spatial coordinates of the target UAV, establish a control signal propagation path model. By tracing the signal transmission direction and attenuation law in reverse, locate the geographical coordinates of the UAV control source.

[0099] In another embodiment of this example, UAV signal detection and positioning are achieved through the following process:

[0100] Multiple UAV platforms equipped with detection equipment are deployed, and dynamic collaborative networking is achieved through a pre-defined frequency domain / time domain / code domain joint communication protocol combined with dynamic spectrum sensing technology. This constructs a three-dimensional detection network that can avoid obstructions from buildings, mountains, and other terrain features. The detection equipment includes multi-band receiving antennas, spectrum sensing modules, and communication units. The UAVs are initially deployed in a layered high- and low-altitude + regional grid layout. Low-altitude UAVs can cover an altitude of 10-100 meters, and high-altitude UAVs can cover an altitude of 100-500 meters, ensuring that signal blind spots caused by terrain obstruction are avoided.

[0101] The three-dimensional detection network is used to capture various signals of the target UAV in a three-dimensional manner to obtain multi-target signals; the time difference arrival (TDOA) data and frequency difference arrival (FDOA) data of each UAV platform are collected simultaneously, as well as the real-time positioning data generated by the high-precision inertial navigation unit (IMU+RTK positioning module) on each platform, to form a multi-source dataset.

[0102] After preprocessing the multi-source dataset, the target UAV is located using a distributed positioning algorithm: based on the preprocessed multi-source data, the UAV platform acts as a local node, forming a local observation cluster with 3-5 neighboring UAV nodes to complete the initial positioning in parallel.

[0103] Construct a system of positioning equations:

[0104] TDOA constraint: Let the target coordinates be ( x, y, z ),node i With nodes j The coordinates are ( x i ,y i ,z i ), ( x j ,y j ,z j ),node i With nodes j The coordinates are provided by a high-precision inertial navigation unit, then the signal arrival time difference Δ tij satisfies:

[0105] ;

[0106] wherein, c is the speed of light.

[0107] FDOA constraint: combined with the motion speed of the node i and the node j ( v ix ,v iy ,v iz ), v jx , v jy , v jz ) provided by a high-precision inertial navigation unit, the frequency offset difference Δ f ij satisfies:

[0108]

[0109] wherein, f 0 is the signal center frequency.

[0110] Solve the above equation set by iterative least squares method to obtain the preliminary position of the target in the local coordinate system ( x loc ,y loc ,z loc ), and calculate the residual error as the weight basis for subsequent global fusion. When the residual error is ≤0.5 m, the weight coefficient is ≥0.7.

[0111] Each local node uploads the preliminary positioning result and the residual error to the network center node, such as a high-altitude reference unmanned aerial vehicle, and outputs the final positioning result through a global fusion algorithm:

[0112] The weight distribution center node assigns a weight w k to each preliminary position according to the residual error of each local result:

[0113] The local results are fused by using the weighted average method, and the formula is:

[0114] ;

[0115] The multi-target signal is introduced into a signal processing unit based on an FPGA hardware acceleration architecture, rapid signal sorting is completed through hardware acceleration, frequency hopping / spreading signal features are extracted, multi-target signal real-time processing data cooperating with positioning results are formed, and subsequent traceability positioning is supported;

[0116] Based on the multi-target signal real-time processing data, the control source of the target unmanned aerial vehicle is identified through signal fingerprint feature matching (including radio frequency fingerprint + modulation ripple feature), multi-unmanned aerial vehicle angle of arrival (AOA) data of a three-dimensional stereoscopic detection network are called, and geographic spatial data of a geographic information system (GIS) are fused, traceability positioning of the control source is finally realized, and a complete technical closed loop of signal capture-target positioning-control source traceability is formed.

[0117] The radio frequency fingerprint includes carrier frequency offset, phase noise and power spectral density of the signal, and the modulation ripple feature includes modulation depth, symbol interference pattern and symbol timing deviation of the signal. The control source of the target unmanned aerial vehicle is identified by matching the "radio frequency fingerprint + modulation ripple feature" of the unmanned aerial vehicle control source in the preset fingerprint library.

[0118] The calibrated AOA data of each detected unmanned aerial vehicle is combined with global coordinates of the detected unmanned aerial vehicle itself provided by a high-precision inertial navigation unit, X i ,Y i ,Z i The spatial direction line pointing from the unmanned aerial vehicle to the control source is generated, and the expression is:

[0119] ;

[0120] Wherein θ i is the global azimuth angle of the first i unmanned aerial vehicle, Φ i is the global elevation angle, and X, Y and Z are global coordinates of any point on the direction line.

[0121] The spatial straight line intersection method is used to calculate the preliminary candidate position of the control source: each two AOA direction lines form an intersection area in space, the intersection of all intersection areas is calculated to obtain a preliminary candidate positioning area;

[0122] In the preliminary candidate area, the distance X c ,Y c ,Z c from each candidate point to each unmanned aerial vehicle is calculated d c ,

[0123] By theoretical received power P r,calc With actual received power of UAV P r,meas Compare, keep P r,calc - P r,meas | Candidate points within 2dB, based on GIS data, eliminate physically inaccessible points in candidate areas, including building interiors, lake surfaces, steep mountains, and keep candidate points in open areas such as squares, rooftops, and roadside ground.

[0124] For the screened candidate points, use weighted least squares method to calculate the optimal positioning coordinates (x, y, h) X final ,Y final ,Z final ), where the control source is mostly operated by ground personnel, Z final Usually take the ground elevation.

[0125] Example 2

[0126] Based on Example 1, see Figure 2 , the specific process of step S1 is as follows:

[0127] S11: Ascending platform deployment: deploy the ascending equipment equipped with multi-band adaptive array antenna to the preset height of 50-300 meters, complete platform attitude calibration through the ground control system, ensure that the array antenna is in a stable state with a horizontal reference plane error ≤0.5°, and complete the initial spatial coordinate anchoring based on GNSS positioning data;

[0128] S12: Array antenna activation and frequency band configuration: activate the multi-band adaptive array antenna, which contains 3 or more sub-arrays, such as sub-array A covering 1.2-2.4GHz, sub-array B covering 2.0-3.8GHz, and sub-array C covering 3.4-6GHz. Through the frequency band overlap design between sub-arrays, the overlapping frequency bands of adjacent sub-arrays are ≥15%, such as sub-array A and B overlapping in 2.0-2.4GHz and sub-array B and C overlapping in 3.4-3.8GHz, to achieve seamless coverage of 1.2-6GHz frequency band;

[0129] S13: Beamforming coordination: each sub-array independently generates controllable beams through phased array technology. The ground control unit dynamically adjusts the beam pointing angle and gain distribution between sub-arrays based on real-time electromagnetic environment monitoring data, so that the adjacent sub-array beams form spatial coverage complementation in the overlapping frequency band. Through the coherent synthesis of signals between sub-arrays, a global spatial spectrum estimation model is constructed to realize spatial domain scanning of electromagnetic signals within 360° azimuth angle and -30° to +30° elevation angle range.

[0130] S14: Signal capture and multi-channel output: When the target signal enters the scanning range, the array antenna identifies the signal direction of arrival through spatial spectrum estimation, drives the corresponding subarray beam to focus on the signal direction (beam width ≤ 3°) to enhance the receiving gain, and at the same time starts the multi-channel AD sampling module to convert the raw radio frequency signals captured by each subarray into digital signals, and outputs them to the signal processing unit in real time according to the subarray channel number, such as channel 1 corresponding to subarray A and channel 2 corresponding to subarray B, to form a multi-channel raw data set containing time stamp, signal strength and phase information.

[0131] The specific process of extracting the frequency hopping period, modulation mode and pseudo-random code characteristics of the signal in step S2 of performing time-frequency domain joint analysis on the multi-channel raw signal is as follows:

[0132] S20: Multi-channel signal preprocessing: Synchronize and calibrate the multi-channel raw digital signals output by each subarray, correct the time deviation based on the time stamp, and filter out environmental noise and co-frequency interference through wavelet threshold denoising to retain the effective signal components;

[0133] S21: Time-frequency domain joint analysis:

[0134] Perform short-time Fourier transform on the preprocessed signal to generate a time-frequency distribution graph, identify the energy distribution characteristics of the signal on the time-frequency plane, and locate the frequency hopping time and corresponding carrier frequency value of the frequency hopping signal;

[0135] For the steady-state signal segment in the time-frequency distribution graph, extract the transient characteristics such as the signal rising / falling edge slope during frequency hopping switching through continuous wavelet transform, calculate the instantaneous frequency and amplitude by Hilbert transform, and determine the frequency hopping period;

[0136] S22: Modulation mode identification:

[0137] For non-frequency hopping signal segments or single-frequency resident segments of frequency hopping signals, extract the amplitude histogram, phase change rate and spectrum correlation characteristics of the signal;

[0138] Match the characteristics through a support vector machine (SVM) classifier to identify the modulation mode, including AM, FM, FSK, PSK and QAM.

[0139] S23: Pseudo-random code feature extraction:

[0140] For signals identified as spread spectrum modulation, determine the pseudo-random code period through sliding correlation calculation;

[0141] Extract the code sequence characteristics based on the signal phase reversal points within the code period, compare them with the common drone pseudo-random codes in the preset feature library, and determine the code type and code length.

[0142] The specific process of matching the extracted features with the preset unmanned aerial vehicle signal feature library in step S2 to identify the target unmanned aerial vehicle signal is as follows:

[0143] S24: Standardization processing and feature library calling: the target signal features (frequency hopping period, modulation mode, pseudo-random code features) extracted in step 2 are standardized and converted into a numerical format consistent with the reference parameters of the feature library, such as converting the frequency hopping period into a millisecond-level value and mapping the modulation mode into an enumeration type code in the feature library; the preset unmanned aerial vehicle signal feature library is called, which contains a reference feature set of known unmanned aerial vehicle models, including frequency hopping period range, modulation mode type, pseudo-random code type / code length, carrier frequency fluctuation threshold, etc. Each parameter is associated with a corresponding weight coefficient, and the weight proportion of the modulation mode and the pseudo-random code features is ≥60%;

[0144] S25: Multi-dimensional feature matching calculation:

[0145] For frequency hopping period: calculate the deviation rate of the target signal frequency hopping period from the reference period of each model in the feature library, deviation rate = |target value-reference value| / reference value, when the deviation rate ≤5%, it is determined as preliminary matching;

[0146] For modulation mode: type matching method is adopted, if the target signal modulation mode is completely consistent with a certain type in the feature library, the score is 100% of the weight value of this parameter, if it is a compatible type, such as 4FSK and 2FSK belonging to FSK family, the score is 60% of the weight value;

[0147] For pseudo-random code features: through code sequence correlation calculation, when the correlation coefficient ≥0.85, it is determined as high matching, combined with code length deviation comprehensive score, the higher the correlation coefficient and the smaller the code length deviation, the higher the score.

[0148] S26: Matching degree comprehensive evaluation and target identification: the multi-dimensional matching scores are weighted and summed to calculate the comprehensive matching degree (full score is 100 points) of the target signal and each unmanned aerial vehicle model in the feature library; set the matching threshold (≥70 points), when the comprehensive matching degree of a certain model exceeds the threshold, it is determined that the target signal is the signal of the corresponding model; if there are multiple models with matching degree exceeding the threshold, the model with the highest score is selected as the identification result, and the second highest score model is marked as the alternative; if the matching degrees of all models are lower than the threshold, the signal is classified as an unknown unmanned aerial vehicle signal, and the feature library updating mechanism is triggered.

[0149] The specific process of separating the control instruction link and the image transmission link signal of the target signal in step S2 is as follows:

[0150] S27: Pre-set control instruction link and image transmission link distinction feature library: control instruction link usually has the characteristics of low data rate (≤1 Mbps), short frame length (≤256 bytes), high real-time (transmission interval ≤100 ms), FSK / PSK modulation; image transmission link usually has the characteristics of high data rate (≥2 Mbps), long frame length (≥1024 bytes), periodic burst transmission (frame interval 10-50 ms), QAM / OFDM modulation, and image transmission signal often contains video coding features;

[0151] S28: Signal framing and parameter extraction: frame synchronization processing is performed on the identified target unmanned aerial vehicle signal by detecting the pre-set synchronization header or frame period characteristics, and the continuous signal is divided into independent data frames; key parameters of each frame signal are extracted, including data rate, frame length, modulation mode, transmission interval (time difference between adjacent frames);

[0152] S29: Link type classification and separation: a decision tree classification model is used to determine the link type of the framed signal:

[0153] If the signal frame meets the data rate ≤1 Mbps, frame length ≤256 bytes, transmission interval ≤100 ms, and modulation mode FSK / PSK, it is determined as a control instruction link;

[0154] If the signal frame meets the data rate ≥2 Mbps, frame length ≥1024 bytes, transmission interval 10-50 ms, and modulation mode QAM / OFDM, it is determined as an image transmission link;

[0155] The two types of link signals are physically separated, and independent signal buffer areas are allocated to store the frame data of the control instruction link and the image transmission link, respectively, while recording the timestamp association relationship of the two types of links.

[0156] Link consistency verification: verify whether the two types of separated link signals come from the same unmanned aerial vehicle: by comparing the frequency drift trend (drift difference ≤5 kHz), transmission power fluctuation correlation (correlation coefficient ≥0.7), and spatial orientation angle deviation (deviation ≤1°) of the two, if they all meet the consistency conditions, the separation result is confirmed to be valid; if not, the framing and classification process is re-executed until the consistency requirements are met.

[0157] Embodiment 3

[0158] On the basis of embodiment 1 or embodiment 2, the specific process of establishing a dynamic spatial coordinate system based on GNSS real-time positioning data and attitude parameters output by the inertial navigation system of the ascending platform in step S3 is as follows:

[0159] S31: Reference coordinate anchoring:

[0160] Taking the center of mass of the aerial platform as the coordinate system origin O, the GNSS module is used to obtain the geodetic coordinates (L, B, H) of the platform in real time, which are converted into absolute coordinates (X0, Y0, Z0) in the WGS-84 geocentric rectangular coordinate system as the spatial anchor point of the dynamic coordinate system;

[0161] Setting the initial pointing of the coordinate system: taking the origin O as the starting point, the vector of the center of the earth pointing to the O point is the Z axis (the skyward axis, pointing to the normal direction outside the earth), the geodetic coordinates are converted into plane coordinates through Gauss projection to determine the initial eastward axis (X axis) and northward axis (Y axis), forming the initial ENU (East-North-Sky) coordinate system framework.

[0162] S32: attitude parameter fusion and dynamic correction of coordinate system:

[0163] Real-time collection of INS output attitude parameters, including pitch angle (θ, rotation angle around X axis), roll angle (φ, rotation angle around Y axis), and heading angle (ψ, rotation angle around Z axis), with a sampling frequency ≥100 Hz;

[0164] Construction of rotation matrix based on attitude angle: according to the Euler angle transformation formula, the initial ENU coordinate system is rotated around the Z axis, Y axis, and X axis in turn to generate the dynamic rotation matrix R(ψ, θ, φ);

[0165] Real-time correction of the initial coordinate system through the rotation matrix: multiplying the axis vectors (X0, Y0, Z0) of the initial ENU coordinate system by the rotation matrix R to obtain the corrected instantaneous coordinate axis pointing direction (X', Y', Z'), ensuring that the coordinate system always remains consistent with the real-time attitude of the aerial platform. When the platform tilts, the coordinate system tilts synchronously with it.

[0166] S33: time synchronization and coordinate updating mechanism:

[0167] Using the PPS (pulse per second) signal of GNSS as the time reference, the attitude parameters output by INS and the GNSS positioning data are time-stamped synchronized, with a synchronization error controlled within 1 ms;

[0168] Coordinate system update is performed every 50 ms: combining the latest GNSS absolute coordinates (X0', Y0', Z0') and INS attitude angles (ψ', θ', φ'), the rotation matrix is recalculated and the coordinate system origin position and axis pointing direction are corrected to compensate for platform drift;

[0169] When the GNSS signal is temporarily lost (≤3s), the track prediction data of INS is used to temporarily update the coordinate system origin position, and the position offset is predicted through forward difference to maintain the continuity of the coordinate system.

[0170] S3 converts the time difference of arrival (TDoA) and the phase difference of arrival (PDoA) of the target UAV signal among the multiple sub-arrays to the coordinate system, constructs a three-dimensional positioning equation set, and the specific process of solving the real-time spatial position coordinates of the target UAV is as follows:

[0171] S34: Multi-sub-array spatial coordinate calibration:

[0172] In the established dynamic spatial coordinate system (origin is the center of mass O of the airborne platform, and the axis system is X', Y', Z'), the relative coordinates of each sub-array antenna are calibrated in advance: let the coordinates of sub-array i be (xᵢ, yᵢ, zᵢ), i = 1, 2,..., n, n ≥ 3, the physical distance between the sub-arrays is obtained by laser ranging, and is converted into three-dimensional coordinate values in the coordinate system to form a sub-array position matrix.

[0173] Record the phase center offset of each sub-array (≤0.05m) for error compensation in subsequent PDoA calculation.

[0174] S35: Time-space synchronization conversion of TDoA and PDoA:

[0175] TDoA conversion: for the target signal received by the multiple sub-arrays, take sub-array 1 as the reference, calculate the time difference Δtᵢ1 between the remaining sub-array i and sub-array 1 (extract the time difference of the signal front by the cross-correlation algorithm), combine the electromagnetic wave propagation speed c (3 × 10 8 m / s), convert it into a distance difference Δdᵢ1 = Δtᵢ1 × c, and obtain the relationship: [(X-xᵢ)²+(Y-yᵢ)²+(Z-zᵢ)²] 1 / 2 -[(X-x1)²+(Y-y1)²+(Z-z1)²] 1 / 2 = Δdᵢ1, where (X, Y, Z) is the coordinate to be solved in the dynamic coordinate system;

[0176] PDoA conversion: for the signal of the same carrier frequency f, calculate the phase difference Δφᵢ1 between sub-array i and sub-array 1, convert it into a distance difference Δd'ᵢ1 = (Δφᵢ1 × λ) / (2π), λ is the signal wavelength, λ = c / f, and consider the baseline length bᵢ1 between the sub-arrays (≤ λ / 2 to avoid phase ambiguity), to obtain the supplementary relationship: [(X-xᵢ)²+(Y-yᵢ)²+(Z-zᵢ)²] 1 / 2 -[(X-x1)²+(Y-y1)²+(Z-z1)²] 1 / 2 = Δd'ᵢ1 + kᵢ1 × λ, kᵢ1 is an integer, and the value range of kᵢ1 is restricted by the TDoA result.

[0177] S36: Construction and solution of three-dimensional positioning equation set:

[0178] Select 3 or more sub-array combinations, based on the above TDoA and PDoA conversion distance difference relationship, construct a nonlinear equation set containing 3 unknowns (X, Y, Z);

[0179] Linearize the nonlinear equation set by Taylor series expansion method: take the initial position estimate of the target (the rough positioning result based on the signal direction angle) as the iteration starting point, calculate the Jacobian matrix, and convert the equation set to linear form Ax=b;

[0180] Solve the linear equation set by least squares method to get the target coordinate correction, and iterate until the correction is ≤0.1m, and output the converged (X, Y, Z) as the real-time spatial coordinates of the target in the dynamic coordinate system.

[0181] S37: Coordinate system conversion and absolute position output:

[0182] Convert the target coordinates (X, Y, Z) in the dynamic coordinate system to the WGS-84 geocentric rectangular coordinate system: through the rotation matrix R -1 (inverse matrix of rotation matrix) in step 3, the attitude solution is obtained, which eliminates the influence of platform attitude on coordinates, and the absolute offset of the target relative to the origin of the platform is obtained;

[0183] Superimpose the GNSS absolute coordinates (X0, Y0, Z0) of the airborne platform to obtain the WGS-84 geodetic coordinates of the target UAV, and complete the calculation closed loop from signal time difference / phase difference to geographic coordinates.

[0184] The specific process of step S4 is as follows:

[0185] S41: Control instruction link transmitter feature parameter extraction:

[0186] Carrier frequency drift rate extraction: carrier frequency tracking is performed on the control instruction link signal, phase-locked loop technology is used, tracking bandwidth ≤1kHz, carrier frequency values within 100ms are continuously collected, sampling interval 1ms, carrier frequency drift rate with time is calculated through linear fitting, unit: Hz / s;

[0187] Power fluctuation period extraction: real-time sampling of signal receiving power is performed, sampling rate 100Hz, power spectral density is analyzed through Fourier transform, the frequency value corresponding to the main peak is identified, and its reciprocal is the power fluctuation period, and the fluctuation amplitude range is also recorded;

[0188] Encoding format analysis: decode the control instruction frame data, extract the frame header structure (synchronization code sequence), check method (such as CRC16 / 32), data frame length and bit synchronization method (such as Manchester encoding, differential encoding), and generate encoding feature code (used to distinguish different control sources of the same model).

[0189] S42: Control signal propagation path model construction:

[0190] Basic path model: Taking the real-time position coordinates (X1, Y1, Z1) of the UAV calculated in step S3 as the relay point, assuming that the control signal directly reaches the UAV from the control source (X0, Y0, Z0) (line-of-sight propagation), and combining the relative position (X p , Y p , Z p ) of the UAV and the lift-off platform, a two-section propagation model is established: control source → UAV → lift-off platform;

[0191] Decay law correction: based on the received power P r of the control instruction signal, combining the free space attenuation formula P r =P t -20lg(4πd / λ), Pt is the transmission power, d is the propagation distance, λ is the signal wavelength, and an environmental attenuation coefficient is introduced, which is corrected in real time by the terrain shielding information scanned by the laser radar of the lift-off platform, the shielding area coefficient is increased by 5-20 dB, and the propagation distance d 01 from the control source to the UAV is inversely calculated.

[0192] Characteristic constraint supplement: taking the carrier frequency drift rate and power fluctuation period as the model constraint conditions, the carrier frequency drift rate of the same control source changes at a rate of ≤10% within 1 km, and the power fluctuation period is strongly related to the antenna vibration characteristics of the control source, such as the fluctuation period of a handheld remote controller, which is mostly 0.5-2 s, which is used to eliminate false paths.

[0193] S43: Control source direction reverse tracking:

[0194] Azimuth angle calculation: based on the PDoA measurement value of the control instruction signal by the multi-subarray antenna of the lift-off platform, and combining the dynamic space coordinate system conversion, the azimuth angle α1 and the elevation angle β1 of the signal from the UAV to the lift-off platform are obtained.

[0195] Reverse direction derivation: according to the position (X1, Y1, Z1) and motion state (speed, heading) of the UAV, the incident direction of the control signal when it reaches the UAV is calculated, assuming that the UAV is omnidirectional reception, the azimuth angle α0 of the control source relative to the UAV is α1+180°±Δα, Δα is the attitude correction of the UAV, ≤2°, and the elevation angle β0 is calculated by the signal incident elevation angle and the altitude difference of the UAV.

[0196] Trajectory intersection positioning: taking the position of the UAV as the vertex, constructing a ray along the reverse direction α0, β0, combining the propagation distance d 01 inverted in step 2, the candidate coordinates (X0', Y0', Z0') of the control source at this moment are obtained.

[0197] S44: Multi-time data fusion and coordinate calculation:

[0198] Continuous acquisition of 5-10 time control source candidate coordinates, time interval 0.5-2s, synchronization with the positioning update cycle of the unmanned aerial vehicle;

[0199] Kalman filter is used to fuse the candidate coordinates at multiple times: taking the control source moving speed ≤10m / s as the state constraint, dynamically correcting the coordinate drift, and obtaining the filtered control source coordinates (X0, Y0, Z0);

[0200] Combined with the ground digital map for altitude constraint, Z0 is usually the ground height ±5m, and the WGS-84 geodetic coordinates of the control source are finally output.

[0201] Example 4

[0202] On the basis of example 1 or example 2 or example 3, a consumer unmanned aerial vehicle of a certain model appears in a certain area, which is a certain brand M1, and its signal characteristics are preset as follows:

[0203] Control instruction link: FSK modulation, carrier frequency 1.8GHz, pseudo-random code M sequence (code length 32bit), data rate 0.6Mbps, frame length 192 bytes;

[0204] Figure transmission link: QAM modulation, carrier frequency 2.7GHz, frequency hopping period 600μs, data rate 8Mbps, frame length 2048 bytes, containing H.265 video encoding synchronization code.

[0205] The specific implementation process of the signal detection and accurate positioning method of the ascending platform unmanned aerial vehicle using the application is as follows:

[0206] Deploy 3 detection unmanned aerial vehicles (numbered U1, U2, U3) to form an ascending platform, the core configuration is:

[0207] Multi-band array antenna: 3 sub-arrays are sub-array A: 1.2-2.4GHz; sub-array B: 2.0-3.8GHz; sub-array C: 3.4-6GHz, the adjacent sub-array frequency band overlap degree is 20%;

[0208] GNSS module: supporting WGS-84 coordinate system;

[0209] INS module: sampling frequency 100Hz;

[0210] AD sampling module: sampling rate 250MSps, quantization bits 16bit.

[0211] Detection platform deployment height: 180m;

[0212] Average ground elevation in the area (used for control source altitude constraint later): 52.3m;

[0213] Electromagnetic environment: No strong interference source, terrain mainly flat open land, local low building, laser radar scan confirms the shielding coefficient correction amount 5dB.

[0214] First, platform deployment and calibration: 3 detection unmanned aerial vehicles are launched to 180m height, and attitude calibration is completed through the ground control system to ensure that the horizontal reference plane error of the array antenna is less than or equal to 0.3°. The initial anchor coordinates are obtained based on GNSS:

[0215] U1: (L1=116.412358°E, B1=39.920145°N, H1=180m)

[0216] U2: (L2=116.413892°E, B2=39.921567°N, H2=180m)

[0217] U3: (L3=116.411203°E, B3=39.922018°N, H3=180m)

[0218] Antenna start and frequency band configuration: activate the multi-frequency array antenna, and 3 sub-arrays cooperatively realize 1.2-6GHz seamless coverage, sub-array A and B overlap the frequency band 2.0-2.4GHz, and sub-array B and C overlap the frequency band 3.4-3.8GHz.

[0219] Beamforming cooperation: the ground control unit monitors the electromagnetic environment, dynamically adjusts the sub-array beam pointing angle, and the antennas of the 3 unmanned aerial vehicles cooperatively form a full domain scanning range of 360° azimuth angle and -30°~+30° pitch angle, and the spatial spectrum estimation accuracy is improved through coherent synthesis.

[0220] Signal capture and output: after the target unmanned aerial vehicle signal enters the scanning range, the array antenna identifies the direction through spatial spectrum estimation, drives the corresponding sub-array beam to focus, and the beam width is 2.5°. The AD sampling module starts, converts the captured 1.8GHz and 2.7GHz signals into digital signals, and outputs to the signal processing unit according to the channel to form a multi-channel original data set containing time stamp, signal strength and phase information.

[0221] Then, signal feature extraction and target identification:

[0222] Signal preprocessing: synchronize and calibrate the multi-channel original signal based on the time stamp, and use the soft and hard threshold compromise function for wavelet threshold denoising, and the threshold value of the low frequency band (1.8GHz) is set to T=0.02V, and the threshold value of the high frequency band (2.7GHz) is set to 1.3T=0.026V, and the environmental noise is filtered out.

[0223] Time-frequency domain analysis and feature extraction:

[0224] Short-time Fourier transform: Hann window, window length = carrier frequency period x 8, generate time-frequency distribution map, locate the frequency hopping time and carrier frequency value of the 2.7 GHz signal, extract the transient feature through continuous wavelet transform, and calculate the frequency hopping period as 598 μs through Hilbert transform;

[0225] Modulation mode recognition: the amplitude histogram and phase change rate feature of the 1.8 GHz signal are extracted, and the SVM classifier is used to identify FSK modulation; the 2.7 GHz signal is identified as QAM modulation;

[0226] Pseudo-random code extraction: sliding correlation calculation is performed on the 1.8 GHz FSK signal, the correlation window length = code length range x 1.5, the pseudo-random code period 1 ms is determined, the code sequence feature is extracted and compared with the preset library, and it is determined as M sequence with code length 32 bits.

[0227] Target recognition:

[0228] Standardization: convert the frequency hopping period (598 μs), modulation mode (FSK / QAM), and pseudo-random code feature (M sequence 32 bits) into a feature library compatible format, call the preset feature library, and the modulation mode weight is 40%, the pseudo-random code weight is 30%, and the frequency hopping period weight is 30%;

[0229] Multi-dimensional matching: frequency hopping period deviation rate = |598-600| / 600≈0.33%, ≤5%, 30 points; modulation mode complete match, 70 points; pseudo-random code correlation coefficient 0.93 (≥0.85), 30 points; comprehensive matching degree = 30+70+30=130 points (full score 150 points, far exceeding the threshold 80 points), determined as a certain brand M1 type unmanned aerial vehicle.

[0230] Link separation:

[0231] Preset feature library: control instruction link (≤1 Mbps, ≤256 bytes, FSK / PSK), image transmission link (≥2 Mbps, ≥1024 bytes, QAM / OFDM);

[0232] Frame division and parameter extraction: frame the target signal, the 1.8 GHz signal data rate is 0.6 Mbps, the frame length is 192 bytes, and the transmission interval is 60 ms; the 2.7 GHz signal data rate is 8 Mbps, the frame length is 2048 bytes, and the transmission interval is 35 ms, and the H.265 synchronization code is detected;

[0233] Through the decision tree model, it is determined that 1.8 GHz is the control instruction link and 2.7 GHz is the image transmission link; the carrier frequency drift difference of the two is ≤2 kHz, the power fluctuation correlation coefficient is 0.85, and the coming angle deviation is ≤0.5°, confirming that the separation is effective.

[0234] Further precise positioning of the target unmanned aerial vehicle:

[0235] Dynamic spatial coordinate system establishment:

[0236] Reference anchoring: Taking the centroid of U1 as the origin O, its GNSS coordinates are converted into WGS-84 geocentric rectangular coordinates (X0=12938546.32 m, Y0=4875623.15 m, Z0=4145892.78 m), and an initial ENU coordinate system (X east, Y north, Z sky) is established;

[0237] Attitude correction: The INS attitude parameters of U1 are collected, the pitch angle θ = 0.4°, the roll angle φ = 0.2°, and the heading angle ψ = 35°, and the rotation matrix R(ψ, θ, φ) is constructed in the order of Z→Y→X to correct the initial axis vector to obtain the instantaneous coordinate axis (X', Y', Z');

[0238] Time synchronization and update: The PPS signal of GNSS is used to synchronize the INS and GNSS data, and the coordinate system is updated every 50 ms to compensate for the platform drift.

[0239] Subarray coordinate calibration: In the dynamic coordinate system, the relative coordinates of the 3 subarrays of the U1 array antenna are calibrated: subarray A (0.6 m, 0 m, 0 m), subarray B (0 m, 0.6 m, 0 m), and subarray C (-0.6 m, 0 m, 0 m), with a phase center offset of ≤0.03 m.

[0240] TDoA and PDoA conversion:

[0241] TDoA conversion: Taking subarray A as the reference, the time differences Δt B1 =12 ns, Δt C1 =15 ns between subarrays B and C and A are calculated, which are converted into distance differences Δd B1 =3.6 m, Δd C1 =4.5 m, and the relationship is:

[0242] [(X-0)²+(Y-0.6)²+(Z-0)²] 1 / 2 -[(X-0.6)²+(Y-0)²+(Z-0)²] 1 / 2 =3.6;

[0243] [(X+0.6)²+(Y-0)²+(Z-0)²] 1 / 2 -[(X-0.6)²+(Y-0)²+(Z-0)²] 1 / 2 =4.5;

[0244] PDoA conversion: For a 1.8 GHz signal, the wavelength λ = 0.167 m, and the phase differences Δφ B1 =2π×3.2, Δφ C1= 2π x 4.1 (unwound), converted to distance difference Δd' B1 = 0.534 m, Δd' C1 = 0.684 m, combined with TDoA constraint k value, k B1 = 21, k C1 = 26, resulting in a supplementary relationship.

[0245] Positioning equation set solving: select the relationship of subarrays A-B, A-C to construct a nonlinear equation set, take the initial position of the signal direction angle rough estimate (X0=500 m, Y0=300 m, Z0=100 m) as the iteration starting point, calculate the Jacobian matrix linearization equation set, and solve by least squares method. When the coordinate correction is ≤0.08 m, convergence is obtained, and the target coordinates in the dynamic coordinate system are obtained (X=523.4 m, Y=315.6 m, Z=98.2 m).

[0246] Absolute coordinate output: eliminate the influence of platform posture by inverse matrix R⁻¹ of rotation matrix, get the absolute offset of target relative to U1 origin; superimpose the GNSS geocentric rectangular coordinates of U1, convert to WGS-84 geodetic coordinates:

[0247] Longitude: Lt=116.417892°E

[0248] Latitude: Bt=39.923456°N

[0249] Altitude: Zt=112.5 m

[0250] After verification, the positioning error is ≤3 m.

[0251] Control source tracing:

[0252] Transmitting end feature extraction:

[0253] Carrier frequency drift rate: for 1.8 GHz control signal, use phase-locked loop tracking with bandwidth 0.8 kHz, collect carrier frequency values within 100 ms, sampling interval 1 ms, linear fitting drift rate 18 Hz / s;

[0254] Power fluctuation period: collect received power at 100 Hz sampling rate, Fourier transform analysis gets power spectrum density main peak frequency 0.9 Hz, corresponding fluctuation period 1.11 s, fluctuation amplitude ±1.8 dB;

[0255] Encoding format: decode control frame, extract synchronization code sequence, CRC16 check, frame length 192 bytes, Manchester encoding, generate encoding feature code.

[0256] Propagation path model construction: take the target unmanned aerial vehicle coordinates (L t , B t , Z t) is a relay point, and a two-section model of control source→drone→U1 is established. Based on the received power Pr=-58dBm, combined with the free space attenuation formula, and introducing the environmental attenuation coefficient of 5dB corrected by laser radar, the propagation distance d 01 =950m from the control source to the drone is back calculated.

[0257] Control source direction tracking:

[0258] Azimuth angle calculation: based on the PDoA measurement value, the azimuth angle α1=75° and the pitch angle β1=6° of the signal from the drone to U1 are obtained;

[0259] Reverse direction derivation: the azimuth angle α0=75°+180°±1°=254°~256° and the pitch angle β0=3° (calculated by altitude difference) of the control source relative to the drone are obtained;

[0260] Candidate coordinate calculation: taking the drone position as the vertex, a ray is constructed along α0 and β0, and combined with d 01 =950m, the candidate coordinates (Lc1=116.408921°E, Bc1=39.918765°N, Hc1=89.3m) are calculated.

[0261] Multi-time data fusion and coordinate output: the candidate coordinates at 8 time points are collected at an interval of 1s, and Kalman filtering is used for fusion, with the control source moving speed ≤10m / s constraint and dynamic drift correction. Combined with the digital ground map (the ground altitude in this area is 52.3m), the altitude is corrected according to the ground height +2m constraint, and finally the WGS-84 geodetic coordinates of the control source are output:

[0262] Longitude: Lc=116.409123°E

[0263] Latitude: Bc=39.918978°N

[0264] Altitude: Hc=54.3m

[0265] The source error is ≤7m, and the complete positioning of the target drone and the control source is realized.

[0266] The final result is:

[0267] The WGS-84 geodetic coordinates of the target drone are: 116.417892°E, 39.923456°N, 112.5m;

[0268] The WGS-84 geodetic coordinates of the control source are: 116.409123°E, 39.918978°N, 54.3m.

Claims

1. An aerial platform UAV signal detection and accurate positioning method, characterized in that, The method comprises the following steps: S1: deploying multiple airborne unmanned aerial vehicle platforms, the airborne unmanned aerial vehicle platforms being equipped with detection equipment, the detection equipment comprising a multi-frequency array antenna of at least three sub-arrays, capturing global electromagnetic signals in real time and outputting multi-channel raw signal data; S2: performing joint time-frequency domain analysis on the multi-channel raw signal, extracting the frequency hopping period, modulation mode and pseudo-random code features of the signal, matching with a preset unmanned aerial vehicle signal feature library to identify the target unmanned aerial vehicle signal, and separating the control instruction link and the image transmission link signal of the target signal; S3: establishing a dynamic space coordinate system, converting the time difference of arrival TDoA and the phase difference of arrival PDoA of the identified target unmanned aerial vehicle signal between multiple sub-arrays to the coordinate system, constructing a three-dimensional positioning equation set, and solving to obtain the real-time spatial position coordinates of the target unmanned aerial vehicle; S4: for the control instruction link signal, extracting the transmission end feature parameters thereof, combining the real-time spatial position coordinates of the target unmanned aerial vehicle, establishing a control signal propagation path model, and positioning the geographical coordinates of the unmanned aerial vehicle control source by reversely tracking the signal transmission direction and the attenuation law.

2. The method of claim 1, wherein the method further comprises: The specific process of step S1 is as follows: S11: airborne platform deployment: the airborne equipment equipped with a multi-frequency adaptive array antenna is launched to a preset height, the platform attitude is calibrated through a ground control system, and the initial spatial coordinate anchoring is completed based on GNSS positioning data; S12: array antenna startup and frequency band configuration: the multi-frequency adaptive array antenna is activated, which contains 3 or more sub-arrays, and the seamless coverage of 1.2-6GHz frequency band is realized through the frequency band overlap design between sub-arrays; S13: beamforming cooperation: each sub-array independently generates a controllable beam through phased array technology, and the ground control unit dynamically adjusts the beam pointing angle and gain distribution between sub-arrays based on real-time electromagnetic environment monitoring data; S14: signal capture and multi-channel output: the array antenna identifies the signal coming direction through spatial spectrum estimation, drives the corresponding sub-array beam to focus on the signal coming direction to enhance the receiving gain, and outputs to the signal processing unit in real time according to the sub-array channel number, forming a multi-channel raw data set containing time stamp, signal strength and phase information.

3. The method of claim 1, wherein the method further comprises: The specific process of step S2 for joint time-frequency domain analysis of the multi-channel raw signal to extract the frequency hopping period, modulation mode and pseudo-random code features is as follows: S20: multi-channel signal preprocessing: the multi-channel raw digital signal output by each sub-array is synchronized and calibrated, and environmental noise and co-frequency interference are filtered out through wavelet threshold denoising; S21: joint time-frequency domain analysis: Performing short-time Fourier transform on the preprocessed signal to generate a time-frequency distribution map, identifying the energy distribution characteristics of the signal on the time-frequency plane, and locating the frequency hopping time and corresponding carrier frequency value of the frequency hopping signal; For the steady-state signal segment in the time-frequency distribution map, the transient feature is extracted through continuous wavelet transform, the instantaneous frequency and amplitude are calculated combined with Hilbert transform, and the frequency hopping period is determined; S22: modulation mode identification: For non-frequency hopping signal segments or single-frequency resident segments of frequency hopping signals, the amplitude histogram, phase change rate and spectrum correlation features of the signal are extracted; The features are matched through a support vector machine classifier to identify the modulation mode; S23: Pseudo-random code feature extraction: For the signals identified as spread spectrum modulation, the pseudo-random code period is determined by sliding correlation calculation; Based on the signal phase flip point within the code period, the code sequence features are extracted and compared with the common drone pseudo-random code in the preset feature library to determine the code type and length.

4. The method of claim 3, wherein the method further comprises: The specific process of matching the extracted features with the preset drone signal feature library in step S2 to identify the target drone signal is as follows: S24: Standardization processing and feature library calling: standardize the extracted target signal features; call the preset drone signal feature library, which contains a reference feature set of known drone models, and each parameter is associated with a corresponding weight coefficient; S25: Multi-dimensional feature matching calculation: For frequency hopping period: calculate the deviation rate of the target signal frequency hopping period and the reference period of each type in the feature library, deviation rate = |target value-reference value| / reference value, when the deviation rate ≤5%, it is determined as preliminary matching; For modulation mode: use type matching method, if the target signal modulation mode is completely consistent with a certain type in the feature library, the score is 100% of the corresponding weight value, if it is a compatible type, the score is 60% of the corresponding weight value; S26: Matching degree comprehensive evaluation and target recognition: weighted sum of multi-dimensional matching scores, calculate the comprehensive matching degree of target signal and each drone model in the feature library, and match the drone model according to the comprehensive matching degree.

5. The method of claim 4, wherein the method further comprises: The specific process of separating the control instruction link and the image transmission link signal of the target signal in step S2 is as follows: S27: Pre-set control instruction link and image transmission link distinguishing feature library; S28: Signal framing and parameter extraction: frame synchronization processing is performed on the identified target drone signal, and the continuous signal is segmented into independent data frames; the key parameters of each frame signal are extracted; S29: Link type classification and separation: use decision tree classification model to determine the link type of the framed signal.

6. The method of claim 1, wherein the method further comprises: The specific process of establishing a dynamic space coordinate system based on the GNSS real-time positioning data and the attitude parameters output by the inertial navigation system in step S3 is as follows: S31: Reference coordinate anchoring: taking the centroid of the airborne platform as the origin O of the coordinate system, the platform's geodetic coordinates are obtained in real time through the GNSS module, and converted into absolute coordinates (X0, Y0, Z0) in the WGS-84 geocentric rectangular coordinate system, which are used as the spatial anchor point of the dynamic coordinate system; Set the initial pointing of the coordinate system: taking the origin O as the starting point, the vector from the center of the earth to the O point is the Z axis, the geodetic coordinates are converted into plane coordinates through Gauss projection, the initial east axis X and north axis Y are determined, and the initial ENU coordinate system framework is formed; S32: Attitude parameter fusion and coordinate system dynamic correction: Real-time collection of attitude parameters output by the inertial navigation system INS, including pitch angle θ, roll angle φ, and heading angle ψ; rotation matrix construction based on attitude angle: according to the Euler angle transformation formula, the initial ENU coordinate system is rotated around Z axis, Y axis, and X axis in turn to generate a dynamic rotation matrix; Real-time correction of the initial ENU coordinate system by rotation matrix: multiply the axis vector (X0, Y0, Z0) of the initial ENU coordinate system by the rotation matrix to obtain the corrected instantaneous coordinate axis direction (X', Y', Z'); S33: Time synchronization and coordinate updating mechanism: Use the PPS signal of GNSS as the time reference to synchronize the time stamps of the attitude parameters output by INS and the GNSS positioning data; Update the coordinate system every specified time: combine the latest GNSS absolute coordinates (X0', Y0', Z0') and INS attitude angles (ψ', θ', φ') to recalculate the rotation matrix and correct the coordinate system origin position and axis direction, compensating for platform drift.

7. The method of claim 6, wherein the method further comprises: The specific process of converting the time difference of arrival and phase difference of arrival of the target UAV signal among multiple sub-arrays to the coordinate system in S3 to construct a three-dimensional positioning equation set and solve for the real-time spatial position coordinates of the target UAV is as follows: S34: Multi-subarray spatial coordinate calibration: In the established dynamic spatial coordinate system, the relative coordinates of each subarray antenna are calibrated in advance: let the coordinates of subarray i be (xᵢ, yᵢ, zᵢ), i = 1, 2,..., n, n ≥ 3, the physical distance between subarrays is obtained by laser ranging and converted to three-dimensional coordinate values in the coordinate system to form a subarray position matrix; S35: Spatio-temporal synchronization conversion of TDoA and PDoA: TDoA conversion: the target signal received by multiple sub-arrays is converted into a distance difference Δdᵢ1=Δtᵢ1×c, combined with the electromagnetic wave propagation speed c, to obtain the relationship: [(X-xᵢ)²+(Y-yᵢ)²+(Z-zᵢ)²] 1 / 2 -[(X-x1)²+(Y-y1)²+(Z-z1)²] 1 / 2 =Δdᵢ1, where (X, Y, Z) is the coordinate to be solved of the target in the dynamic coordinate system; PDoA conversion: for the signal of the same carrier frequency f, the phase difference Δφᵢ1 of the subarray i and the subarray 1 is calculated, which is converted into the distance difference Δd'ᵢ1=(Δφᵢ1×λ) / (2π), λ is the signal wavelength, λ=c / f, based on the baseline length bᵢ1 between the subarrays, a supplementary relationship is obtained: [(X-xᵢ)²+(Y-yᵢ)²+(Z-zᵢ)²] 1 / 2 -[(X-x1)²+(Y-y1)²+(Z-z1)²] 1 / 2 =Δd'ᵢ1+kᵢ1×λ, kᵢ1 is an integer; S36: Construction and solution of three-dimensional positioning equation set: Select three or more subarrays, and based on the distance difference relationship formula converted by TDoA and PDoA, construct a nonlinear equation set containing three unknowns (X, Y, Z); Linearize the nonlinear equation set using Taylor series expansion: take the initial position estimate of the target as the iteration starting point, calculate the Jacobian matrix, and convert the equation set to linear form Ax = b; Solve the linear equation set by least squares method to obtain the target coordinate correction, and iteratively update until the correction is ≤0.1m, and output the converged (X, Y, Z) as the real-time spatial coordinates of the target in the dynamic coordinate system; S37: Coordinate system conversion and absolute position output: Convert the target coordinates (X, Y, Z) in the dynamic coordinate system to the WGS-84 geocentric rectangular coordinate system: through the rotation matrix R -1 Perform attitude solution to eliminate the influence of platform attitude on coordinates and obtain the absolute offset of the target relative to the origin of the airborne platform. Superimpose the GNSS absolute coordinates (X0, Y0, Z0) of the ascending platform to obtain the WGS-84 geodetic coordinates of the target UAV.

8. The method of claim 7, wherein the method further comprises: The specific process of step S4 is as follows: S41: Feature parameter extraction of control instruction link transmission end: Carrier frequency drift rate extraction: track the carrier frequency of the control instruction link signal, continuously collect the carrier frequency within a specified time, and calculate the drift rate of the carrier frequency over time by linear fitting; Power fluctuation period extraction: real-time sample the signal receiving power, analyze the power spectral density by Fourier transform, identify the frequency value corresponding to the main peak, and its reciprocal is the power fluctuation period, and record the fluctuation amplitude range; Encoding format analysis: decode the control instruction frame data, extract the frame header structure, check method, data frame length, and bit synchronization method, and generate an encoding feature code; S42: Control signal propagation path model construction: The basic path model: taking the calculated real-time position coordinates (X1, Y1, Z1) of the unmanned aerial vehicle as a relay point, assuming that the control signal is directly from the control source (X0, Y0, Z0) to the unmanned aerial vehicle, combining the relative position (X p , Y p , Z p ) of the unmanned aerial vehicle and the take-off platform, a two-section propagation model is established: control source→unmanned aerial vehicle→take-off platform; Attenuation law correction: based on the received power P r of the control instruction signal, combined with the free space attenuation formula P r =P t -20lg(4πd / λ), P t is the transmit power, d is the propagation distance, λ is the signal wavelength, the environmental attenuation coefficient is introduced, and the terrain shielding information scanned by the airborne platform laser radar is used for real-time correction. The shielding area coefficient increases by 5-20dB, and the propagation distance d 01 from the control source to the unmanned aerial vehicle is inversely calculated. S43: Control source direction reverse tracking: Arrival angle calculation: based on the PDoA measurement value of the control instruction signal by the multi-subarray antenna of the airborne platform, combined with dynamic spatial coordinate conversion, the arrival angle of the signal from the UAV to the airborne platform is obtained, azimuth angle α1, pitch angle β1; Reverse direction derivation: according to the position (X1, Y1, Z1) and motion state of the UAV, the incident direction of the control signal when it reaches the UAV is calculated, assuming that the UAV is omnidirectional reception, the azimuth angle of the control source relative to the UAV α0=α1+180°±Δα, Δα is the attitude correction amount of the UAV; the pitch angle β0 is calculated by the signal incident elevation angle and the altitude difference of the UAV; Trajectory intersection positioning: taking the position of the UAV as the vertex, constructing a ray along the reverse direction α0, β0, combining the back-propagated distance d 01 , to obtain the candidate coordinates (X0', Y0', Z0') of the control source at this moment; S44: multi-time data fusion and coordinate solution: Collect 5-10 time control source candidate coordinates synchronously with UAV positioning update period; Use Kalman filter to fuse multi-time candidate coordinates: take the control source moving speed ≤10m / s as the state constraint, dynamically correct the coordinate drift, and get the filtered control source coordinates (X0, Y0, Z0); Combined with the altitude constraint of the digital map, Z0 is the ground height ±5m, and finally output the WGS-84 geodetic coordinates of the control source.

9. The method of claim 1, wherein the method further comprises: Also includes the following process: Deploy multiple UAV platforms carrying detection equipment, complete dynamic cooperative networking through the preset frequency domain / time domain / code domain joint communication protocol combined with dynamic spectrum sensing technology, and build a three-dimensional detection network that can avoid building and mountain terrain shielding; Through the three-dimensional detection network, the target UAV's various signals are captured to obtain multi-target signals; simultaneously collect the time difference arrival data, frequency difference arrival data of each UAV platform, and the real-time positioning data generated by the inertial navigation unit of each platform to form a multi-source data set; After preprocessing the multi-source data set, the target UAV is located by distributed positioning solution; Import multi-target signals into signal processing units based on FPGA hardware acceleration architecture, first complete signal rapid sorting through hardware acceleration, then extract frequency hopping / spreading signal features, form multi-target signal real-time processing data coordinated with positioning results, and support subsequent traceability positioning; Based on multi-target signal real-time processing data, identify the control source of the target UAV through signal fingerprint feature matching, simultaneously call the multi-UAV arrival angle data of the three-dimensional detection network, and fuse geographic spatial data of the geographic information system, finally realize the traceability positioning of the control source.

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