On-orbit interference source positioning method and system integrating Doppler direction finding and map registration

Abstract

The invention discloses a Doppler direction finding and map registration fused in-orbit interference source positioning method and system, and belongs to the technical field of satellite communication positioning. The method comprises the following steps: S1, acquiring real-time positioning and attitude of a dynamic satellite; s2, receiving an interference signal through a satellite single antenna, calculating frequency deviation, and calculating an interference source rough trajectory through Doppler inversion in combination with satellite attitude trajectory parameters; s3, obtaining a corresponding ground remote sensing image according to the rough trajectory; s4, identifying suspected interference equipment in the ground remote sensing image through an image identification algorithm, and classifying and positioning the suspected interference equipment; s5, performing feature point matching on the remote sensing image and a preset high-precision map according to a result of the S4, and realizing image geometric registration by solving an image-map transformation matrix; and S6, mapping an image recognition result to a geographic coordinate, and fusing a coarse positioning result to output a target position. The interference source positioning precision can be remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of satellite communication and positioning technology, specifically relating to an on-orbit interference source localization method and system that integrates Doppler direction finding and map registration. Background Technology

[0002] With the widespread application of satellite navigation and wireless communication, illegal ground-based interference has become increasingly serious, posing a direct threat to navigation systems such as BeiDou. Traditional Doppler direction finding methods utilize the frequency shift of ground interference signals received by low-Earth orbit satellites to invert ground position. While this method offers advantages such as global coverage and rapid response, it suffers from the following drawbacks:

[0003] 1) Positioning accuracy is limited, constrained by Doppler estimation errors and orbital parameter uncertainties;

[0004] 2) Unable to identify the shape of the target device, lacking sensing capabilities;

[0005] 3) The positioning results are difficult to align with the high-precision map, and the spatial landing point error is large.

[0006] Furthermore, in existing technologies, the localization of radio frequency interference sources generally relies on multi-antenna arrays or multi-point station architectures, for example:

[0007] Direction of attack (AOA) / direction of attack (DOA) based methods require satellites or platforms to carry multiple antenna arrays with known spatial spacing. The incident angle is calculated by the phase difference of the received signals, thereby determining the direction of the interference source. However, this method has extremely high requirements for the structural rigidity of the platform, antenna consistency, and phase synchronization, making it difficult to implement on miniaturized, low-cost satellites.

[0008] The multi-base station time difference of arrival (TDOA) method relies on multiple spatially distributed receiving nodes (such as multiple cooperating satellites) to simultaneously receive interference signals and calculate the time difference of arrival for positioning. This method has high positioning accuracy, but it has extremely high requirements for inter-satellite time synchronization, complex system construction, high cost, and slow response speed.

[0009] Interferometer-based phase direction finding: This method obtains extremely high directional accuracy through high-precision phase interferometry and is typically used in heavy military reconnaissance satellites. However, this method requires multiple long-baseline antennas, coherent receivers, and complex mechanical structures, which greatly increases the system complexity and cost.

[0010] While existing image recognition technologies have recognition capabilities, their spatial positioning capabilities are weak, and they rely on ground or low-altitude platforms, making it difficult to achieve full coverage.

[0011] For the reasons mentioned above, there is an urgent need for a method that integrates Doppler radio frequency direction finding and remote sensing image recognition to achieve high-precision positioning. Summary of the Invention

[0012] The present invention aims to at least partially solve one of the technical problems in the aforementioned related technologies.

[0013] Therefore, the purpose of this invention is to provide an on-orbit interference source localization method and system that integrates Doppler direction finding and map registration, which can significantly improve the localization accuracy of interference sources and meet the practical needs of navigation anti-interference.

[0014] To solve the above-mentioned technical problems, the present invention is implemented as follows:

[0015] This invention provides an on-orbit interference source localization method based on Doppler direction finding and map registration fusion, the method comprising:

[0016] S1. Obtain the real-time positioning and attitude of dynamic satellites;

[0017] S2. Receive interference signals through a single satellite antenna, calculate the frequency offset in real time, combine the satellite attitude trajectory parameters, and calculate the coarse trajectory of the interference source using the Doppler inversion algorithm to obtain the coarse positioning result;

[0018] S3. Based on the coarse positioning results, control the gimbal camera to image the target area and obtain a ground remote sensing image of the target area.

[0019] S4. Process the ground remote sensing images using image recognition algorithms to identify suspected interference devices in the images, and classify and locate them.

[0020] S5. Based on the results of S4, feature point matching is performed between the remote sensing image and the preset high-precision map, and then the image-map transformation matrix is ​​solved to achieve geometric registration of the image.

[0021] S6. Map the pixel positions of the identified interfering devices to the real geographic coordinate system, fuse the coarse positioning results, and output the target position.

[0022] In addition, the on-orbit interference source localization method based on Doppler direction finding and map registration fusion according to the present invention may also have the following additional technical features:

[0023] In some implementations, step S2 includes: constructing a frequency-time curve to fit the target trajectory based on the observed frequency shift value and the relative motion speed between the satellite and the ground, and optimizing the position solution using least squares and Bayesian estimation.

[0024] In some implementations, step S4 uses a deep convolutional network to extract semantic features from the image, and then classifies and locates the image based on the extracted semantic features.

[0025] In some implementations, step S5 includes: extracting stable geometric structures from remote sensing images and preset high-precision maps, performing multi-scale feature point matching through local region matching strategies, eliminating mismatched points using spatial transformation matrices and random consistency sampling, and optimizing the spatial transformation relationship between the image and the map by combining geographical prior information.

[0026] In some of these implementations, the stable geometry is any one or more of the following: corner points, road boundaries, and building outlines;

[0027] Multi-scale feature point matching methods are SIFT or ORB;

[0028] Geographic prior information includes known landmark locations and map semantic category constraints.

[0029] This invention also provides an on-orbit interference source localization system that integrates Doppler direction finding and map registration, characterized in that the system comprises:

[0030] The high dynamic satellite positioning module is configured to acquire the satellite's precise position and attitude in real time, providing satellite reference data for locating interference sources;

[0031] The interference source spectrum monitoring and positioning module is configured to receive interference signals through a single satellite antenna, calculate the frequency offset in real time, and combine the satellite attitude trajectory parameters to calculate the coarse trajectory of the interference source through the Doppler inversion algorithm to obtain the initial search area.

[0032] The ground remote sensing image acquisition module is configured to acquire ground remote sensing images of the initial search area;

[0033] The remote sensing image target matching and localization module for interference sources is configured to perform image recognition on ground remote sensing images and register them with a preset map to obtain the mapping relationship between the image-recognized target and its geographic coordinates; and,

[0034] The positioning output module is configured to fuse data from the interference source spectrum monitoring and positioning module and data from the interference source remote sensing image target matching and positioning module to output the location of the interference source with sub-meter accuracy.

[0035] In addition, the on-orbit interference source localization system based on Doppler direction finding and map registration fusion according to the present invention may also have the following additional technical features:

[0036] In some embodiments, the high-dynamic satellite positioning module includes:

[0037] The initialization and self-test submodule is configured to configure the CPU clock frequency, pin functions and baseband chip parameters of the high dynamic satellite positioning module, create functional threads and initialize variables, so that the system enters a stable working state.

[0038] The capture and tracking submodule is configured to perform capture tasks, recapture lost channels, formulate capture strategies, allocate idle channels to achieve adaptive satellite search and recapture.

[0039] The message processing and parsing submodule is configured to receive navigation messages, extract parameters, complete message repair, parsing and availability assessment, and provide raw data for the calculation.

[0040] The observation extraction module is configured to acquire interrupted observation data, convert raw observations into pseudorange, carrier phase, and carrier Doppler, providing standardized observation data for the solution module; and,

[0041] The calculation module is configured to calculate the user terminal's position, velocity, and time information using observations including pseudorange, Doppler shift, and carrier phase, as well as navigation messages.

[0042] In some embodiments, the disturbance source spectrum monitoring and location module includes:

[0043] The initialization and self-test submodule is configured to perform CPU initialization, FPGA hardware initialization, task establishment and initialization of the disturbance source spectrum monitoring and positioning module.

[0044] The monitoring and capture tracking submodule is configured to determine whether to activate the signal capture and tracking function based on the input signal, and to monitor, capture and track the corresponding signal.

[0045] The observation extraction submodule is configured to suppress general interference that is difficult to acquire and track, and obtain the carrier Doppler value through signal domain processing.

[0046] The solution module is configured to use carrier Doppler observations and the spacecraft's real-time precise position, velocity, and time information to calculate the location of ground interference sources and obtain coarse positioning results.

[0047] In some embodiments, the interference source remote sensing image target matching and localization module includes:

[0048] The image search and localization submodule is configured to extract remote sensing image features, compare them with a global reference image feature library, determine the reference image of the target area through geometric verification and screening, and achieve global image localization.

[0049] The image correction and registration submodule is configured to estimate image parameters using an RPC model, perform precise correction and registration of remote sensing images based on reference imagery, and assign accurate geographic coordinate mappings to pixels; and...

[0050] The target recognition and location calibration submodule is configured to identify interfering devices using an interest target recognition algorithm, calibrate the target pixel position, and then output the preliminary geographic coordinates of the target through pixel-geographic coordinate mapping.

[0051] In some of these implementations, the positioning output module is configured to receive the pixel coordinates of the image recognition target, the mapping relationship between the image recognition target and geographic coordinates, and the initial search area. The pixel coordinates are first mapped to geographic coordinates through the mapping relationship, and then fused with the initial search area to output sub-meter latitude and longitude data.

[0052] Compared with the prior art, the present invention has at least the following beneficial effects:

[0053] In this embodiment of the invention, the on-orbit interference source localization method that combines Doppler direction finding and map registration fusion uses only a single-antenna radio frequency receiver, combined with the satellite's own trajectory and velocity, and performs preliminary coarse localization through a Doppler frequency shift inversion algorithm, significantly reducing the complexity and cost of hardware deployment.

[0054] In this embodiment of the invention, the on-orbit interference source localization method that integrates Doppler direction finding and map registration is introduced for the first time to work in collaboration between a controllable gimbal remote sensing imaging device and an AI image recognition module. Under the guidance of coarse localization results, fine localization of the image is performed, thereby achieving closed-loop verification within the system.

[0055] In this embodiment of the invention, the on-orbit interference source localization method that integrates Doppler direction finding and map registration, combined with a high-precision map registration module, transforms image pixel coordinates into real geographic coordinates, effectively eliminating the problems of large error areas and difficulty in implementation of traditional radio frequency direction finding.

[0056] In this embodiment of the invention, the on-orbit interference source localization method that integrates Doppler direction finding and map registration constructs a novel fusion positioning architecture of "single-satellite coarse measurement → image recognition → map fine registration". It can achieve sub-meter level positioning accuracy without the need for multi-satellite coordination or high-precision antenna arrays, and has good practicality and engineering feasibility.

[0057] The on-orbit interference source localization method using the Doppler direction finding and map registration fusion of this invention can significantly improve the localization accuracy of interference sources, meet the practical needs of navigation anti-interference, enhance the ability to identify and confirm targets, and improve interpretability; it can be extended to multiple scenarios such as urban wireless surveillance, battlefield interference suppression, and emergency communication security.

[0058] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0059] Figure 1This is a flowchart of an on-orbit interference source localization method based on Doppler direction finding and map registration fusion, as disclosed in an embodiment of the present invention.

[0060] Figure 2 This is a structural block diagram of a high-dynamic satellite positioning module disclosed in one embodiment of the present invention;

[0061] Figure 3 This is a structural block diagram of an interference source spectrum monitoring and location module disclosed in one embodiment of the present invention;

[0062] Figure 4 The accuracy performance of the multi-epoch Doppler positioning method disclosed in one embodiment of the present invention;

[0063] Figure 5 This is a framework diagram of a remote sensing image target matching and localization module for interference sources, as disclosed in one embodiment of the present invention. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0065] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and specific examples and application scenarios.

[0066] In some embodiments of the present invention, a high-precision positioning method for low-Earth orbit satellite interference sources based on the fusion of radio frequency Doppler direction finding and high-precision map registration of remote sensing images is provided. The positioning steps are as follows:

[0067] Step 1: High Dynamic Satellite Positioning: High-speed satellites dynamically acquire their own positioning and attitude in real time;

[0068] Step 2: Interference source spectrum monitoring and location: Interference signals are received by a single satellite antenna, frequency shift is calculated in real time, and a rough trajectory of the interference source is calculated using a Doppler inversion algorithm in combination with satellite attitude trajectory parameters; Doppler inversion location algorithm is used for location: Based on the observed frequency shift value and the relative motion speed between the satellite and the ground, a frequency-time curve is constructed to fit the target trajectory, and the location solution is optimized using least squares and Bayesian estimation;

[0069] Step 3: High-resolution remote sensing camera and gimbal control: Based on the radio frequency coarse positioning results, control the gimbal camera to image the target area and obtain high-definition ground images with a shooting range of approximately 0.5km × 0.5km.

[0070] Step 4: AI Image Recognition: Use deep convolutional networks to extract semantic features from images, classify and locate images; use trained deep learning models (such as YOLOv8) to automatically identify suspected interference devices in images, such as vehicle-mounted transmitters, portable antennas, illegal radio stations, etc., and output the image pixel coordinates and recognition category of the target;

[0071] Step 5: Image-High-Precision Map Registration: Feature point matching is performed between the remote sensing image and the pre-set high-precision map (including vector buildings and orthophotos). The image-map transformation matrix is ​​solved using the RANSAC algorithm to achieve geometric registration. Specifically: Stable geometric structures (such as corners, road boundaries, building outlines, etc.) are extracted from the remote sensing image and the pre-set high-precision map. Multi-scale feature point matching is performed through local region sub-matching strategies (using SIFT, ORB, etc.). Mismatched points are eliminated using spatial transformation matrix and Random Consistency Sampling (RANSAC). Furthermore, geographic prior information such as known landmark locations and map semantic category restrictions are combined to optimize the spatial transformation relationship between the image and the map, achieving accurate mapping from pixel coordinates to geographic coordinates.

[0072] Step 6: High-precision positioning output: The identified interference target pixel positions are registered and mapped to the real geographic coordinate system, and the preliminary radio frequency positioning results are fused to output the target latitude and longitude position with sub-meter accuracy.

[0073] During satellite operation, real-time precise positioning (i.e., step 1) is achieved using a high-dynamic satellite positioning module. This module's sub-modules include: an initialization and self-test module, an acquisition and tracking module, a message processing and parsing module, an observation extraction module, and a solution module. Figure 1 As shown.

[0074] In some embodiments of the present invention, the initialization and self-test module mainly implements CPU initialization, task creation and initialization, and local clock initialization to put the system in a known state. Its workflow includes: 1) configuring the CPU's PLL to make the CPU work at an appropriate clock frequency; because the CPU pins are multi-functional multiplexed pins and can be configured as input or output pins, the pin functions and input / output need to be configured according to the actual usage; 2) configuring the baseband chip's observation interrupt frequency to 1Hz, and configuring parameters such as baud rate and stop bits for each serial port; 3) creating threads for each functional module and allocating stack space for them, and initializing the variables of each module.

[0075] In some embodiments of the present invention, the functions of the acquisition and tracking module include: meeting the user's adaptive and rapid satellite search and reacquisition needs; automatically determining the acquisition code phase range and Doppler range; possessing hot start, warm start, and cold start functions; managing the power supply of the baseband processing module's loop and acquisition circuit according to usage; and remembering the positioning mode before the last shutdown. The acquisition and tracking module includes two sub-modules: an acquisition execution module and an acquisition strategy module. The acquisition execution module is mainly responsible for completing the acquisition tasks arranged by the acquisition strategy module, acquiring the channels that need to be acquired, reacquiring lost channels, and sending an acquisition task completion event to the acquisition strategy module after all these tasks are completed. Upon receiving this event, the acquisition strategy module searches for other satellites that need to be acquired according to its strategy. If so, and there are still spare unused channels, it binds the spare channels and arranges acquisition tasks again to the acquisition execution module, which then performs the acquisition according to the new tasks. The acquisition strategy module is responsible for the acquisition strategy and acquisition strategy selection.

[0076] In some embodiments of the present invention, the message processing and parsing module is mainly used to complete all message-related reception, processing, and storage. Its functions include: extracting the WN (Write-Nearest) field from the message and verifying it for setting the local time; extracting parameters from the message and placing them into global variables such as ephemeris and almanac; message repair; message parsing; and determining the availability of the message. The message processing and parsing module includes two sub-modules: a message processing module and a message parsing module. When the baseband module generates a message interruption, the baseband interrupt handler in the BSP packet first requests a free memory block from the original message circular queue, then reads the navigation message from the baseband module's message register and places it in the queue, and sends a message reception event to the message processing module. After capturing this event, the message processing module reads the original message from the original message circular queue. If there is a requirement for error testing, the original message is output. If the message was obtained after previous repairs, it must be compared with previously received messages multiple times. After multiple comparisons, if the parameters in the messages are found to be consistent, further processing can proceed. Then, the check digits in the message are removed. The message is then sent to the message parsing module, which parses the message and extracts ephemeris, almanac, satellite clock bias, ionospheric parameters, tropospheric parameters, differential data, etc. from the original message. These parameters are stored in global variables for use by the positioning and velocity calculation module.

[0077] In some embodiments of the present invention, the function of the observation extraction module includes: converting the extracted raw observations into pseudorange, carrier phase, and carrier Doppler; setting the local time; and outputting the pseudorange values ​​of each channel. The workflow of this module is as follows: during baseband processing, observation interrupts are generated at set intervals. At the moment of interruption, the baseband module (existing module) latches the observations and local time of each channel; in the baseband interrupt handling of the BSP, the latched observations and local time are read and saved to the queue; this module (observation extraction module) receives the save message, then outputs the channel details, observations, and local time to the serial port for testing, and then converts the raw observations into pseudorange, carrier phase, and carrier Doppler required by the positioning and velocity measurement module (existing module), and calls the relevant functions of the positioning and velocity measurement module to perform PVT calculation.

[0078] In some embodiments of the present invention, the main function of the calculation module is to calculate the user terminal's position, velocity, and time information using observations such as pseudorange, Doppler frequency shift, and carrier phase, as well as navigation messages. The calculation module includes functional modules for satellite position and velocity calculation, pseudorange error correction, positioning timing calculation, RAIM (Rapid Interval Imaging), and DOP (Doppler Opportunity) value calculation. This module calculates the user terminal's three-dimensional coordinates, clock bias, and DOP value based on the observations and navigation messages output by the user terminal. The timing calculation consists of six main steps:

[0079] A. First, determine observable satellites based on the input pseudorange observations. Then, check the satellite health information and differential alarm information in the message parameters to eliminate faulty satellites. Next, calculate the satellite positions. Based on the current user coordinates, calculate the azimuth and elevation angle of each satellite. Satellites with an elevation angle less than 5 degrees are considered invisible and are also eliminated.

[0080] B. Pseudorange correction: First, satellite clock bias information is used to correct the pseudorange to eliminate satellite clock bias; then, further correction is made based on the positioning / timing mode. For differential mode, equivalent clock bias information and ionospheric grid information can be used to correct the pseudorange; for dual-frequency mode, a combination of dual-frequency pseudoranges is used to eliminate ionospheric delay error.

[0081] C. Constructing the equation, that is, constructing the matrices for solving the equation using least squares / EKF, including the coefficient matrix A, the weight matrix P, and the observation matrix L;

[0082] D. Solve the equation. After constructing the matrices for each term of the equation, you can use the formula X = (A T PA) -1 A T PL calculates the position correction and adds it to the initial coordinates for iterative calculation, i.e., returns to step C; when the correction after each iteration is very small, the solution is considered to be complete.

[0083] E. Autonomous integrity monitoring: Check for faulty satellites using the TRAIM algorithm. If a faulty satellite is found, it is removed and the process proceeds to step C to solve the problem again.

[0084] F. The output 1PPS is precisely adjusted according to the local clock difference to complete unidirectional timing.

[0085] The user terminal extracts raw observation data and navigation messages from the baseband signal processing unit (existing unit). The pseudorange measured by the receiver contains various errors, which need to be estimated and corrected one by one. Combining the navigation message and error model, satellite position, clock error, tropospheric error, etc., are calculated. When BeiDou differential information is available, pseudorange equivalent clock error correction is performed. In single-frequency mode, if the differential grid ionosphere is available, the differential grid algorithm is used to correct ionospheric errors; otherwise, the single-frequency model method is used to correct ionospheric errors. Appropriate satellites are selected to participate in the least squares positioning solution based on elevation angle and signal-to-noise ratio. RAIM monitoring is performed based on the residual vector obtained from the solution to detect and remove faulty satellites, thereby obtaining higher accuracy and reliability.

[0086] To combat the gross errors introduced by user observations, robust estimates can be introduced into the least squares solution to enhance the algorithm's robustness against pseudorange observation errors. However, the aforementioned adaptive and robust algorithms are computationally intensive, and the limitations of computational resources must be fully considered.

[0087] In the above embodiments of the present invention, interference source spectrum monitoring and localization (i.e., step 2) is implemented using an interference source spectrum monitoring and localization module. This module: receives interference signals via a single satellite antenna, calculates the frequency offset in real time, combines this with satellite attitude trajectory parameters, and calculates a coarse trajectory of the interference source using a Doppler inversion algorithm to obtain the initial search area. The module structure diagram is as follows: Figure 2 As shown, it includes four sub-modules: initialization and self-test module, monitoring and capture tracking module, observation extraction module, and calculation module.

[0088] In some embodiments of the present invention, the initialization and self-test module in the interference source spectrum monitoring and location module is mainly used to implement CPU initialization, FPGA hardware initialization, task establishment and initialization. When the CPU starts working, the initialization process is as follows: configure the CPU's PLL to allow the CPU to operate at an appropriate clock frequency; initialize the FPGA hardware, receive an externally input precise clock, and complete the initialization of the local time; create threads for each functional module and allocate stack space for them, and initialize the variables of each module.

[0089] In some embodiments of the present invention, the monitoring and acquisition tracking module in the interference source spectrum monitoring and positioning module includes the following functions: determining whether to activate the signal acquisition and tracking function based on the input signal power; and acquiring and tracking the monitored signal. This monitoring and acquisition tracking module includes two sub-modules: a monitoring module and a acquisition and tracking module. The monitoring module is mainly responsible for determining whether an interference signal is currently entering the receiver. The present invention only monitors and observes interference with power significantly greater than the receiver's noise floor; therefore, the total power entering the channel can be used to determine whether to initiate subsequent acquisition and tracking actions. The acquisition and tracking module is responsible for acquiring and tracking the interference signal, but the acquisition and tracking action is mainly used for high-power deception interference, thereby achieving more accurate observation of the interference signal. For general suppression interference, stable acquisition and tracking of the interference signal cannot be achieved; therefore, the Doppler value is obtained by performing an FFT transform on the input block signal.

[0090] In some embodiments of the present invention, the observation extraction module in the interference source spectrum monitoring and positioning module is used to obtain the carrier Doppler value of the signal through signal domain processing for general suppression interference that is difficult to acquire and track. The module's operation includes: receiving signal sample values ​​of a certain length, extracting the carrier Doppler value required by the solution module through FFT operation, and calling the relevant functions of the solution module to perform position calculation. For modulated signed broadband interference, the carrier Doppler value of the interference signal can be observed through squaring followed by FFT operation.

[0091] In some embodiments of the present invention, the observation extraction algorithm specifically integrates the FFT method, the cyclic off-grid closed compensation method, the Root-MUSIC algorithm, and the ESPRIT method in the software, which can simultaneously use multiple algorithms to extract Doppler observations.

[0092] In some embodiments of the present invention, the solution module in the interference source spectrum monitoring and positioning module mainly utilizes carrier Doppler observations and the real-time precise position, velocity, and time information of the spacecraft to accurately calculate the location of the ground interference source. The module's workflow includes: the user terminal extracts raw observation data from the baseband signal processing unit and corrects the observation values ​​by combining the frequency difference of the local clock; based on multi-epoch observations throughout the flight arc, a system of equations is formed to accurately locate the ground static interference source; during this process, the onboard high-dynamic user terminal provides the solution module with precise position, velocity, time, and frequency information; in this process, it is necessary to solve for the three-dimensional position and frequency difference information of the interference source, therefore at least four epochs of observations are required to obtain a unique solution; simultaneously, the longer the flight arc and the more epochs accumulated, the better the positioning accuracy.

[0093] Key Indicator Demonstration:

[0094] The strength of detectable interference signals is calculated and evaluated. Taking the B3 frequency point (center frequency 1268.52MHz) as an example, the free space loss of satellite-to-ground transmission is 147-154dB when the space station is at different elevation angles. Considering the current interference detection performance of BeiDou receivers, reliable detection can only be performed when the interference power reaches an intensity of -124dBW, at which point the corresponding ground interference source's transmission power needs to reach 23dBW-29dBW. This is consistent with the performance indicators of current major navigation countermeasure equipment. In the first stage, the radio frequency positioning module relies on multi-epoch Doppler observations for joint positioning to calculate and determine the location of the interference source.

[0095] R=-λf d ,

[0096] In the formula, R represents the pseudorange change rate, λ is the wavelength of the nominal carrier, and f d This represents the Doppler frequency shift. Typically, the user's speed is much lower than the space station's speed (approximately 7 km / s). The pseudorange change rate is related to the relative motion between the space station and the interference source, as well as the clock frequency difference of the radio frequency positioning module, and can be expressed by the following formula:

[0097]

[0098] Where F = λΔf, Δf is the frequency difference between the interference source clock and the RF positioning module clock, and R represents the distance between the interference source and the space station. (x i ,y i ,z i (x) represents the position of the space station at the time of observation. u ,y u ,z u () indicates the location of the interference source at the observation time.

[0099] Expanding the above equation using Taylor at the initial position and frequency difference, and ignoring higher-order terms, yields the linear equation:

[0100]

[0101] In the formula, x u For vector (x) u ,y u ,z u ), representing the three-dimensional location of the interference source; x i For vector (x) i ,y i ,z i ), representing the three-dimensional position of the space station at each epoch; x i For vector (x) i ,y i ,z i ), representing the three-dimensional velocity of the space station at each epoch.

[0102] Therefore, when using Doppler frequency shift for position calculation, the observed quantities are the Doppler frequency shifts of the signal observed by the RF positioning module at each epoch. The state quantities include the three-dimensional position of the interference source and the frequency difference (Δx, Δy, Δz, ΔF) between the interference source and the RF positioning module. The observation matrix G is represented by the following equation:

[0103]

[0104] In multi-epoch joint positioning, the stability of the frequency sources of both the transmitter and receiver has a significant impact. Based on research into interference source equipment and the stability of various crystal oscillators, OCXO is a widely used and suitable frequency reference (its stability over 100 seconds can reach the order of 1e-10). According to the above performance indicators, the crystal oscillator can remain relatively stable for several minutes, with jitter within 1Hz. Therefore, the state variables and observation matrix G of multi-epoch joint positioning are consistent with those of multi-satellite joint positioning, requiring only 4 epochs of observations to calculate the user's three-dimensional position and local frequency difference. The participation of more epochs will further improve accuracy.

[0105] This invention evaluates the achievable accuracy when using the Doppler method for positioning, such as... Figure 3 As shown.

[0106] The positioning error decreases significantly as the frequency measurement error decreases; at a frequency difference of 0.1 Hz, the error is approximately 500 meters (1σ). In the above evaluation, the total observation time lasted 60 seconds. This positioning accuracy can provide precise guidance information for visible light imaging and target localization.

[0107] In some embodiments of the present invention, steps 4 and 5 are implemented by a remote sensing image target matching and localization module for interference sources. This module can perform matching search and localization on the acquired optical satellite images and complete image target identification and location calibration. This module includes three sub-modules: an image search and localization module, an image correction and registration module, and a target identification and location calibration module, with the following architecture: Figure 4 As shown.

[0108] In some embodiments of the present invention, the image search and positioning module primarily functions to perform global search and positioning on input remote sensing images, identifying the specific geographical location of the images and their corresponding reference images. The module's workflow includes: acquiring remote sensing images and extracting image features; comparing the extracted image feature vectors with pre-stored geographic image information to complete a rapid search; and performing geometric verification and filtering of the search results to obtain the reference image of the target area.

[0109] In some embodiments of the present invention, the main function of the image correction and registration module is to precisely correct and register the remote sensing image to be located based on the reference image, assigning accurate geographic coordinate information to the image pixels. The workflow of this module includes: after obtaining the reference image of the target area using the image search and positioning module, estimating the image parameters using the RPC model, and using the estimated parameters to precisely correct and register the image to obtain accurate satellite imagery.

[0110] In some embodiments of the present invention, the target recognition and location calibration module mainly functions to execute a target recognition algorithm on the registered image, identify the pixel position of the target of interest, and map its precise geographical location. The workflow of this module includes: after obtaining the registered image, using the target recognition algorithm to identify the target of interest; calibrating the relative position of the identified target of interest in the image; and mapping the relative position of the target of interest in the image to absolute position coordinates.

[0111] Example:

[0112] During a drill, a low-orbit satellite carrying this system detected an abnormal spectrum in a certain area. The system located the area through radio frequency direction finding, and discovered an illegal interference vehicle through gimbal remote sensing imaging and AI identification. The system then registered the location to a specific street, output the latitude and longitude coordinates, and finally guided ground law enforcement to investigate and deal with the case.

[0113] Any part of this invention not described in detail can be referred to in the prior art or in the art known to those skilled in the art. This embodiment does not limit such part and will not describe it in detail here.

[0114] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

Claims

1. An on-orbit interference source localization method that integrates Doppler direction finding and map registration, characterized in that, The method includes: S1. Obtain the real-time positioning and attitude of dynamic satellites; S2. Receive interference signals through a single satellite antenna, calculate the frequency offset in real time, combine the satellite attitude trajectory parameters, and calculate the coarse trajectory of the interference source using the Doppler inversion algorithm to obtain the coarse positioning result; S3. Based on the coarse positioning results, control the gimbal camera to image the target area and obtain a ground remote sensing image of the target area. S4. Process the ground remote sensing images using image recognition algorithms to identify suspected interference devices in the images, and classify and locate them. S5. Based on the results of S4, feature point matching is performed between the remote sensing image and the preset high-precision map, and then the image-map transformation matrix is ​​solved to achieve geometric registration of the image. S6. Map the pixel positions of the identified interfering devices to the real geographic coordinate system, fuse the coarse positioning results, and output the target position.

2. The on-orbit interference source localization method based on Doppler direction finding and map registration fusion according to claim 1, characterized in that, Step S2 includes: constructing a frequency-time curve to fit the target trajectory based on the observed frequency shift value and the relative motion velocity between the satellite and the ground, and optimizing the position solution using least squares and Bayesian estimation.

3. The on-orbit interference source localization method based on Doppler direction finding and map registration fusion according to claim 1, characterized in that, Step S4 uses a deep convolutional network to extract semantic features from the image, and classifies and locates the image based on the extracted semantic features.

4. The on-orbit interference source localization method based on Doppler direction finding and map registration fusion according to claim 1, characterized in that, Step S5 includes: extracting stable geometric structures from remote sensing images and pre-set high-precision maps, performing multi-scale feature point matching through local region matching strategies, eliminating mismatched points using spatial transformation matrices and random consistency sampling, and optimizing the spatial transformation relationship between the image and the map by combining geographical prior information.

5. The on-orbit interference source localization method based on Doppler direction finding and map registration fusion according to claim 4, characterized in that, Stable geometric structures can be any one or more of the following: corner points, road boundaries, and building outlines; Multi-scale feature point matching methods are SIFT or ORB; Geographic prior information includes known landmark locations and map semantic category constraints.

6. An on-orbit interference source localization system that integrates Doppler direction finding and map registration, characterized in that, The system includes: The high dynamic satellite positioning module is configured to acquire the satellite's precise position and attitude in real time, providing satellite reference data for locating interference sources; The interference source spectrum monitoring and positioning module is configured to receive interference signals through a single satellite antenna, calculate the frequency offset in real time, and combine the satellite attitude trajectory parameters to calculate the coarse trajectory of the interference source through the Doppler inversion algorithm to obtain the initial search area. The ground remote sensing image acquisition module is configured to acquire ground remote sensing images of the initial search area; The remote sensing image target matching and localization module for interference sources is configured to perform image recognition on ground remote sensing images and register them with a preset map to obtain the mapping relationship between the image-recognized target and its geographic coordinates; and, The positioning output module is configured to fuse data from the interference source spectrum monitoring and positioning module and data from the interference source remote sensing image target matching and positioning module to output the location of the interference source with sub-meter accuracy.

7. The on-orbit interference source localization system based on Doppler direction finding and map registration fusion according to claim 6, characterized in that, The high-dynamic satellite positioning module includes: The initialization and self-test submodule is configured to configure the CPU clock frequency, pin functions and baseband chip parameters of the high dynamic satellite positioning module, create functional threads and initialize variables, so that the system enters a stable working state. The capture and tracking submodule is configured to perform capture tasks, recapture lost channels, formulate capture strategies, allocate idle channels to achieve adaptive satellite search and recapture. The message processing and parsing submodule is configured to receive navigation messages, extract parameters, complete message repair, parsing and availability assessment, and provide raw data for the calculation. The observation extraction module is configured to acquire interrupted observation data, convert raw observations into pseudorange, carrier phase, and carrier Doppler, providing standardized observation data for the solution module; and, The calculation module is configured to calculate the user terminal's position, velocity, and time information using observations including pseudorange, Doppler shift, and carrier phase, as well as navigation messages.

8. The on-orbit interference source localization system based on Doppler direction finding and map registration fusion according to claim 6, characterized in that, The disturbance source spectrum monitoring and location module includes: The initialization and self-test submodule is configured to perform CPU initialization, FPGA hardware initialization, task establishment and initialization of the disturbance source spectrum monitoring and positioning module. The monitoring and capture tracking submodule is configured to determine whether to activate the signal capture and tracking function based on the input signal, and to monitor, capture and track the corresponding signal. The observation extraction submodule is configured to suppress general interference that is difficult to acquire and track, and obtain the carrier Doppler value through signal domain processing. The solution module is configured to use carrier Doppler observations and the spacecraft's real-time precise position, velocity, and time information to calculate the location of ground interference sources and obtain coarse positioning results.

9. The on-orbit interference source localization system based on Doppler direction finding and map registration fusion according to claim 6, characterized in that, The remote sensing image target matching and localization module for interference sources includes: The image search and localization submodule is configured to extract remote sensing image features, compare them with a global reference image feature library, determine the reference image of the target area through geometric verification and screening, and achieve global image localization. The image correction and registration submodule is configured to estimate image parameters using an RPC model, perform precise correction and registration of remote sensing images based on reference imagery, and assign accurate geographic coordinate mappings to pixels; and... The target recognition and location calibration submodule is configured to identify interfering devices using an interest target recognition algorithm, calibrate the target pixel position, and then output the preliminary geographic coordinates of the target through pixel-geographic coordinate mapping.

10. The on-orbit interference source localization system based on Doppler direction finding and map registration fusion according to claim 6, characterized in that, The positioning output module is configured to receive the pixel coordinates of the image recognition target, the mapping relationship between the image recognition target and the geographic coordinates, and the initial search area. It first maps the pixel coordinates to geographic coordinates through the mapping relationship, and then merges them with the initial search area to output sub-meter latitude and longitude data.

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