Method and apparatus for locating a radio interference source on a high-speed moving platform

Abstract

The application relates to a positioning method and device for a radio interference source of a high-speed moving platform, and relates to a radio interference source real-time positioning method and system which does not need a large-scale physical array antenna, is suitable for a high-speed moving platform, has high-precision positioning and strong anti-multipath capability, and solves the problems of hardware adaptation difficulty, poor environmental robustness, and insufficient real-time performance of radio interference source positioning in a high-speed moving scene, reduces hardware deployment complexity and cost, improves positioning precision and stability in a complex electromagnetic environment, and meets the engineering application requirements of real-time interference source positioning in scenes such as rail transit, low-altitude flight and low-orbit satellite communication.

Description

Technical Field

[0001] This application relates to the fields of radio monitoring, signal processing and electronic countermeasures, and in particular to a method and apparatus for locating radio interference sources on a high-speed mobile platform. Background Technology

[0002] With the rapid development of rail transit, low-altitude aircraft, and low-Earth orbit satellite communication systems, the application scenarios of radio communication are constantly expanding towards diversification and complexity. Against this backdrop, the safe monitoring of the radio environment and the precise location of potential interference sources have become critical requirements for ensuring the stable operation of communication systems, and their importance is increasingly prominent.

[0003] Especially during the operation of high-speed mobile platforms such as high-speed trains and drones, sudden radio interference sources that may appear in the environment along the route can easily cause serious interference to the platform's communication links, thereby threatening the safety and stability of operation. Therefore, developing a radio interference source localization technology that can adapt to high-speed mobile scenarios and has both high accuracy and high robustness has become an urgent technical challenge to be solved in this field. Summary of the Invention

[0004] Currently, existing radio interference source localization technologies can be mainly divided into the following three categories, but they all exhibit significant limitations when facing practical applications on high-speed mobile platforms: 1. DOA (Angle of Arrival) localization methods based on array antennas: These methods, such as the classic MUSIC (Multiple SIgnal Classification) and ESPRIT (Estimating Signal Parameters via Rotational Invariance Techniques) algorithms, rely on deploying multi-physical antenna arrays on the platform. However, their hardware structure is relatively complex, and their size and power consumption are relatively large. For high-speed mobile platforms with limited installation space and payload capacity, this approach faces significant challenges in engineering deployment, and may even be difficult to implement.

[0005] 2. TDOA (Time Difference of Arrival) based positioning method: This method requires multiple ground stations to coordinate sampling and data exchange, resulting in high deployment costs and stringent requirements for strict synchronization between stations. In high-speed, dynamic scenarios, this multi-station collaborative model is difficult to achieve flexible networking and cannot meet the needs of real-time positioning.

[0006] 3. Synthetic Aperture Method Based on Single-Station Motion: Although this technique utilizes platform motion to synthesize a large-aperture virtual array and possesses certain hardware advantages, it is currently mainly applied in the radar field. In complex electromagnetic environments such as urban rail transit and low-altitude flight, this method exhibits deficiencies in resistance to multipath effects and non-line-of-sight (NLOS) interference, resulting in large positioning errors and making it difficult to meet the engineering requirements of high-precision positioning.

[0007] In summary, existing technologies generally suffer from poor adaptability to high-speed mobile platforms, low positioning accuracy in complex electromagnetic environments, difficulty in balancing real-time performance and positioning accuracy, and high hardware deployment costs. Therefore, there is an urgent need for a new technical solution to overcome these shortcomings and meet the engineering requirements for real-time and accurate positioning of radio interference sources in high-speed mobile scenarios.

[0008] To address the problems of existing technologies, this application provides a localization scheme for radio interference sources on high-speed mobile platforms. This scheme eliminates the need for large-scale physical array antennas, adapts to high-speed mobile platforms, and combines high-precision positioning with strong multipath resistance. It solves the problems of difficult hardware adaptation, poor environmental robustness, and insufficient real-time performance in radio interference source localization in high-speed mobile scenarios, reduces hardware deployment complexity and cost, improves positioning accuracy and stability in complex electromagnetic environments, and meets the engineering application requirements for real-time interference source localization in scenarios such as rail transit, low-altitude flight, and low-orbit satellite communication.

[0009] According to a first aspect of this application, a method for locating radio interference sources on a high-speed mobile platform is provided, characterized in that it includes: (a) Within the current time window, by constructing a synthetic aperture virtual array, the signal model matrix data corresponding to the physical antenna at each moment in the current time window is determined, wherein the physical antenna is installed in the high-speed mobile platform; (b) Calculate the estimated angle of arrival based on the signal model matrix data; (c) Calculate the estimated values ​​of the positioning observations based on the signal model matrix data; (d) By fusing the estimated angle of arrival and the estimated location observations, a preliminary location estimate of the interference source is determined using a pre-constructed optimization objective function and the current weighting coefficients of the optimization objective function; (e) Optimize the weight coefficients in the objective function using an artificial intelligence model based on the estimated angle of arrival, the estimated location observations, and the preliminary location estimate, and determine the optimized weight coefficients as the current weight coefficients of the objective function, then return to step (d); and (f) If the optimized weight coefficients meet the preset conditions, the location estimate of the interference source is determined by the optimization objective function based on the weight coefficients that meet the preset conditions, so as to determine the final location estimate of the current time window.

[0010] According to a second aspect of this application, a device for locating radio interference sources on a high-speed mobile platform is provided, characterized in that it comprises: The first determining module is used to determine the signal model matrix data of the physical antenna at each moment in the current time window by constructing a synthetic aperture virtual array, wherein the physical antenna is installed in the high-speed mobile platform; The first calculation module is used to calculate the estimated angle of arrival based on the signal model matrix data; The second calculation module is used to calculate the estimated values ​​of the positioning observations based on the signal model matrix data; The fusion module is used to fuse the estimated angle of arrival and the estimated location observations, and determine the preliminary location estimate of the interference source by using a pre-constructed optimization objective function and the current weight coefficients of the optimization objective function. The optimization module is used to optimize the weight coefficients in the objective function using an artificial intelligence model based on the estimated angle of arrival, the estimated location observations, and the preliminary position estimate, and to determine the optimized weight coefficients as the current weight coefficients of the objective function; and The second determining module is used to determine the location estimate of the interference source based on the optimized weight coefficients that meet the preset conditions, through the optimization objective function, so as to determine the final location estimate of the current time window.

[0011] According to a third aspect of this application, an electronic device is provided, comprising: Processor; and The memory stores computer instructions that, when executed by the processor, cause the processor to perform the methods described in the first and second aspects.

[0012] According to a fourth aspect of this application, a non-transitory computer storage medium is provided, which stores a computer program that, when executed by a plurality of processors, causes the processors to perform the methods described in the first and second aspects.

[0013] The method and apparatus for locating radio interference sources on high-speed mobile platforms provided in this application can improve positioning accuracy compared to single positioning methods (such as TDOA, FDOA, PDOA); it eliminates the need for multi-antenna arrays and high-precision clock synchronization modules, reducing hardware requirements; moreover, it has strong anti-interference capabilities while meeting real-time requirements, and can be directly adapted to different high-speed motion platforms such as high-speed rail, drones, and satellites, demonstrating good versatility. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings, without exceeding the scope of protection claimed by this application.

[0015] Figure 1 This is an overall architecture diagram of a positioning system for radio interference sources on a high-speed mobile platform according to an embodiment of this application.

[0016] Figure 2 This is a schematic diagram of a synthetic aperture virtual array according to an embodiment of this application.

[0017] Figure 3 This is a flowchart of DOA estimation based on one embodiment.

[0018] Figure 4 This is a flowchart of TDOA estimation based on one embodiment.

[0019] Figure 5 This is a schematic diagram of the fusion positioning process according to an embodiment of this application.

[0020] Figure 6 This is a structural diagram of the artificial intelligence fusion module according to an embodiment of this application.

[0021] Figure 7 This is a flowchart of a method for locating radio interference sources on a high-speed mobile platform according to an embodiment of this application.

[0022] Figure 8 This is a schematic diagram of a location device for radio interference sources on a high-speed mobile platform according to an embodiment of this application.

[0023] Figure 9 This is a schematic diagram of the structure of an electronic device provided in this application. Detailed Implementation

[0024] The technical solutions of the embodiments of this application 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 this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0025] Overall, addressing the limitations of hardware deployment, difficulties in multi-station synchronization, and low positioning accuracy in complex environments in existing technologies, this application proposes a radio interference source localization method and system adapted to high-speed mobile platforms, integrating the technical concepts of "single-station synthetic aperture virtual array" and "multi-parameter joint optimization." Specifically, this application utilizes the motion trajectory of the high-speed mobile platform (such as high-speed rail or UAV) and coherently processes signals received by a single or a small number of physical antennas at different times using synthetic aperture technology to construct an equivalent large-scale virtual array. Based on this, a sliding time window mechanism is used to extract various positioning observations of the signal in real time (such as DOA, TDOA, FDOA, PDOA, or signal strength), and a corresponding optimization objective function is constructed. By solving this function, a preliminary estimate of the interference source's location is achieved, and adaptive correction algorithms (such as machine learning models) are combined to further improve the real-time performance and robustness of the localization. This application cleverly uses "time to space" to solve the problem of limited physical arrays; at the same time, through multi-parameter fusion and sliding window processing, it effectively suppresses multipath and non-line-of-sight (NLOS) interference in complex electromagnetic environments, and achieves high-precision, real-time dynamic positioning of interference sources under single-station conditions. It has significant advantages such as low hardware cost, strong engineering adaptability, and high positioning accuracy.

[0026] In one embodiment, the high-speed mobile platform can encompass airborne, ground-based, and specialized high-speed mobile objects, such as fixed-wing drones, manned aircraft, low-orbit satellites, high-speed trains, and urban rail vehicles. The positioning system of this application is deployed on the high-speed mobile platform and consists of hardware modules and software processing modules. These modules work together to achieve signal acquisition, synchronization, processing, and positioning output. Figure 1 This is an overall architecture diagram of a positioning system for radio interference sources on a high-speed mobile platform according to an embodiment of this application. Figure 1As shown, the positioning system includes a receiving antenna, a high-speed sampling receiver, a time synchronization module, a trajectory acquisition module, a signal processing module, an artificial intelligence processing module, and a positioning output module. The receiving antenna collects radio interference signals, supporting single-antenna or multi-distributed antenna deployment without the need for a physical array. The high-speed sampling receiver performs high-speed sampling and down-conversion on the collected RF signals, converting them into digital baseband signals. The time synchronization module ensures precise time synchronization among all modules, guaranteeing consistency in sampling, trajectory acquisition, and signal processing. The trajectory acquisition module acquires real-time trajectory information such as the position and speed of the high-speed mobile platform (e.g., GPS / BDS / INS positioning data). The signal processing module performs tasks such as constructing a synthetic aperture virtual array, angle of arrival estimation, positioning observation estimation (e.g., TDOA), and multi-source information fusion positioning calculations. The artificial intelligence processing module performs signal type recognition, multipath / NLOS detection, path reliability assessment, and dynamic adjustment of fusion weights. The positioning output module outputs the real-time coordinates of the interference source, enabling continuous tracking and trajectory estimation of the interference source.

[0027] In some embodiments, this application achieves real-time and accurate localization of radio interference sources by a high-speed mobile platform through a technical approach that includes constructing a synthetic aperture virtual array, fusing multi-source information from angle-of-arrival estimation and positioning observations, employing artificial intelligence-based multipath optimization, and real-time sliding window processing. The solution process of this application mainly includes the following steps.

[0028] Step 1: Construct a synthetic aperture virtual array like Figure 2 As shown, by utilizing the motion trajectory of a high-speed mobile platform, the sampling signals of a single station (physical antenna) at different times and locations are mapped into spatial virtual array elements to construct a synthetic aperture virtual array, without the need to deploy physical array antennas on the high-speed mobile platform.

[0029] In one embodiment, the signal model of the constructed synthetic aperture virtual array is: (1) in: This represents the signal model matrix data at time k. Let k be the spatial position vector of the platform at time k. Let be the incident direction vector of the interference source signal. s is the signal wavelength, and s is the signal source amplitude. It is Gaussian white noise.

[0030] By acquiring real-time location data from the trajectory acquisition module, the mapping from time sampling to spatial array elements is completed, enabling the dynamic construction of the virtual array and determining the signal model matrix data for each moment in the current time window corresponding to the physical antenna.

[0031] Step 2: Determine the estimated Angle of Arrival (DOA) based on the signal model matrix data. Based on the constructed synthetic aperture virtual array, the incident direction of the interference source signal is estimated through a preset algorithm.

[0032] In one embodiment, such as Figure 3 As shown, the incident direction estimation of the interference source signal can be achieved using a subspace decomposition algorithm. The specific steps include: ① Perform data preprocessing on the sampled signals of the virtual array to construct the signal covariance matrix: ; in, Let X be the signal vector and its conjugate transpose X. H The outer product, R, is the average of the outer product matrices from multiple snapshots (time samplings); ② Perform eigenvalue decomposition on the covariance matrix to separate the signal subspace and the noise subspace En; ③ Construct the spatial spectral function: ,in It is the direction guide vector; ④ Perform peak search on the spatial spectrum; the angle corresponding to the peak is the incident direction of the interference source signal. .

[0033] In some embodiments, in order to achieve angle of arrival estimation, in addition to the subspace decomposition algorithm, other algorithms can be used, such as the MUSIC or ESPRIT algorithms. In addition, beamforming methods, sparse reconstruction methods or machine learning-based methods can also be used.

[0034] Step 3: Determine the estimation of positioning observations based on the signal model matrix data. In some embodiments, the positioning observations include time difference or spatial difference measurements, wherein the time difference or spatial difference measurements are not limited to time difference of arrival (TDOA), but may also include frequency difference (FDOA), time of arrival (TOA), phase difference (PDOA), signal strength information, and other information.

[0035] Taking the estimation of Time Difference of Arrival (TDOA) as an example, such as Figure 4 As shown, based on the sampled signals from two receiving channels, the time difference of arrival of the interference source signal can be estimated using a cross-correlation algorithm. The specific steps include: ① Input dual-channel digital baseband signal , , , The data represents two channels respectively; ② Calculate the cross-correlation function of the two-channel signals: ; ③ Perform peak detection on the cross-correlation function; the delay corresponding to the peak is the estimated time difference of arrival. .

[0036] The two receiving channels can be two physical antennas installed on a high-speed mobile platform. The signals sampled by the two physical antennas are used for calculation. The signals can be collected by a single wire and the two channels are formed by time differentiation. For example, it can be the signal in equation (1). and .

[0037] Those skilled in the art will understand that, in addition to the cross-correlation algorithm, other algorithms can be used to estimate the Time Difference of Arrival (TDOA). This application does not impose any restrictions on this, and all other algorithms capable of estimating the Time Difference of Arrival (TDOA) are within the scope of this application.

[0038] Step 4: Multi-source information fusion positioning In this step, the estimated angle of arrival and the estimated location observations are fused together, and a preliminary location estimate of the interference source is determined by using a pre-constructed optimization objective function and the current weighting coefficients of the optimization objective function.

[0039] Taking the location observation as the Time Difference of Arrival (TDOA) as an example, such as Figure 5 As shown, a joint optimization model of DOA and TDOA is constructed to integrate the positioning results of the two types of information, thereby improving positioning accuracy. In a specific embodiment, the optimization objective function can be: (2) Where p is the spatial location vector of the interference source. Let p be the theoretical incident direction. Let p be the theoretical time difference of arrival. , These are the fusion weighting coefficients for DOA and TDOA (initial values ​​can be set as needed). The objective function is solved using a numerical optimization algorithm to obtain a preliminary estimate of the interference source's location.

[0040] In some embodiments, in addition to the weighted least squares algorithm described above, the multi-source information fusion method can also be implemented using nonlinear optimization, Bayesian estimation, filtering methods, or artificial intelligence-based fusion methods.

[0041] Step 5: Artificial Intelligence Fusion and Multipath Resistance Optimization In this step, the weight coefficients in the objective function are optimized using an artificial intelligence model based on the estimated angle of arrival, the estimated location observations, and the preliminary location estimate. The optimized weight coefficients are then determined as the current weight coefficients of the objective function.

[0042] In some embodiments, an artificial intelligence model is introduced to optimize the fusion positioning process, addressing positioning errors caused by multipath and NLOS interference in complex electromagnetic environments. In one specific embodiment, such as... Figure 6 As shown, the function and implementation of this step are as follows: The input features include: the time-frequency plot of the interference source signal, the virtual array covariance matrix, the angle of arrival (DOA) estimate, the estimated value of the positioning observation, and the preliminary position estimate. Among them, the time-frequency plot of the interference source signal is a priori spectrum plot, and the virtual array covariance matrix can be the synthetic aperture virtual array covariance matrix, which can be obtained by replacing s in equation (1) with the interference source signal.

[0043] Functions include: ① Signal type identification, distinguishing useful interference signals from clutter; ② Multipath and NLOS detection, identifying line-of-sight (LOS) and non-line-of-sight (NLOS) propagation paths; ③ Path reliability assessment, quantifying the effectiveness of different propagation paths; ④ Dynamic weight adjustment, optimizing the fusion weight coefficients in real time based on path reliability. , ; Output results: signal type, path confidence, and optimized fusion weights. , The optimized weights are then sent back to the multi-source information fusion module to correct the positioning results.

[0044] Then, the optimized weight coefficients can be... , The current weight coefficients are determined as the optimization objective function; the data is then returned to the multi-source information fusion module, which performs a preliminary position estimate based on the optimized weight coefficients, thereby refining the weight coefficients. , The process involves continuous updates and iterations. If the optimized weighting coefficients meet preset conditions, the location estimate of the interference source is determined by optimizing the objective function based on these weighting coefficients, thus determining the final location estimate for the current time window. In one embodiment, the preset conditions can be the weighting coefficients... , The change in the update is less than the preset range, or it can be a weight coefficient. , The number of updates has reached a preset value, and this application does not impose any restrictions on this.

[0045] It is understandable that the above-mentioned artificial intelligence methods can be implemented using deep learning models, traditional machine learning models, or non-learning methods based on rules and statistical features. For example, deep learning models can use convolutional neural networks (CNNs) or gated recurrent units (GRUs); traditional machine learning models can use support vector machines (SVMs) or decision trees; and non-learning methods can use energy detection methods based on fixed thresholds or biased iterative algorithms based on mathematical formulas.

[0046] Through steps one through five described above, a final position estimate can be obtained within a time window. In some embodiments, a sliding time window can be used to iteratively execute steps one through five. The window length is adaptively adjusted based on the platform's movement speed and the signal sampling rate to ensure sufficient sampling data is acquired for virtual array construction and parameter estimation while meeting real-time requirements. Steps one through five are executed for any one of multiple time windows within a preset observation period to obtain the final position estimates for multiple time windows.

[0047] In some embodiments, the final position estimates of multiple time windows are filtered and / or smoothed to achieve trajectory estimation of the interference source; based on the trajectory estimation of the interference source, the motion trend and position prediction of the interference source can be determined. Specifically, the spatial coordinates of the interference source can be output in real time based on the iterative processing results of the sliding window. By performing Kalman filtering or smoothing on the positioning results of consecutive frames, continuous tracking and trajectory estimation of the interference source can be achieved, and the motion trend and position prediction results of the interference source can be output.

[0048] Based on the above description, according to one aspect of this application, a method for locating radio interference sources on a high-speed mobile platform is provided. Figure 7 This is a flowchart of a method for locating radio interference sources on a high-speed mobile platform according to an embodiment of this application. Figure 7 As shown, the method includes the following steps: Step S701: Within the current time window, by constructing a synthetic aperture virtual array, the signal model matrix data corresponding to the physical antenna at each moment in the current time window is determined, wherein the physical antenna is installed in the high-speed mobile platform; Step S702: Calculate the estimated angle of arrival based on the signal model matrix data; Step S703: Calculate the estimated value of the positioning observation based on the signal model matrix data; Step S704: By fusing the estimated angle of arrival and the estimated location observations, a preliminary location estimate of the interference source is determined using a pre-constructed optimization objective function and the current weight coefficients of the optimization objective function. Step S705: The weight coefficients in the objective function are optimized using an artificial intelligence model based on the estimated angle of arrival, the estimated location observations, and the preliminary position estimate. The optimized weight coefficients are then determined as the current weight coefficients of the objective function, and the process returns to step S704. Step S706: If the optimized weight coefficients meet the preset conditions, the location estimate of the interference source is determined by the optimization objective function based on the weight coefficients that meet the preset conditions, so as to determine the final location estimate of the current time window.

[0049] In one alternative embodiment, Figure 7 The method shown may also include: Step S707: For any one of the multiple time windows within the preset observation time, execute steps S701-S706 to obtain the final position estimate of the multiple time windows.

[0050] In one alternative embodiment, Figure 7 The method shown may also include: Step S708: Filter and / or smooth the final position estimates of the multiple time windows to estimate the trajectory of the interference source; and Step S709: Based on the trajectory estimation of the interference source, determine the motion trend and location prediction of the interference source.

[0051] In an optional embodiment, step S705 may include: The time-frequency plot of the interference source signal, the covariance matrix of the synthetic aperture virtual array, the estimated angle of arrival, the estimated location observations, and the preliminary position estimate are input into the artificial intelligence model; and The artificial intelligence model optimizes the weight coefficients in the objective function to determine the optimized weight coefficients.

[0052] Based on the above description, according to another aspect of this application, a device for locating radio interference sources of high-speed mobile platforms is provided. Figure 8 This is a schematic diagram of a device for locating radio interference sources on a high-speed mobile platform according to an embodiment of this application. Figure 8As shown, the device includes: a first determining module 801, a first calculating module 802, a second calculating module 803, a fusion module 804, an optimization module 805, and a second determining module 806. The first determining module 801 is used to determine the signal model matrix data for each moment in the current time window corresponding to the physical antenna by constructing a synthetic aperture virtual array, wherein the physical antenna is installed in the high-speed mobile platform. The first calculating module 802 is used to calculate the angle of arrival estimate based on the signal model matrix data. The second calculating module 803 is used to calculate the estimated value of the positioning observation based on the signal model matrix data. The fusion module 804 is used to fuse the angle of arrival estimate and the... The estimated location of the observations is used to determine the preliminary location estimate of the interference source through a pre-constructed optimization objective function and the current weight coefficients of the optimization objective function. The optimization module 805 is used to optimize the weight coefficients in the optimization objective function based on the estimated angle of arrival, the estimated location of the observations, and the preliminary location estimate using an artificial intelligence model, and to determine the optimized weight coefficients as the current weight coefficients of the optimization objective function. The second determination module 806 is used to determine the location estimate of the interference source based on the weight coefficients that meet the preset conditions through the optimization objective function, in order to determine the final location estimate of the current time window.

[0053] In one alternative embodiment, Figure 8 The apparatus shown may also include: The acquisition module 807 is used to acquire the final position estimates of multiple time windows within a preset observation period.

[0054] In one alternative embodiment, Figure 8 The method shown may also include: Processing module 808 is used to filter and / or smooth the final position estimates of the multiple time windows to estimate the trajectory of the interference source; and The third determining module 809 is used to determine the motion trend and position prediction of the interference source based on the trajectory estimation of the interference source.

[0055] In an optional embodiment, the optimization module 805 can be used to: The time-frequency plot of the interference source signal, the covariance matrix of the synthetic aperture virtual array, the estimated angle of arrival, the estimated location observations, and the preliminary position estimate are input into the artificial intelligence model; and The artificial intelligence model optimizes the weight coefficients in the objective function to determine the optimized weight coefficients.

[0056] The main technological innovations of this application are: 1. Single / few antenna "synthetic aperture virtual array construction technology" based on high-speed motion trajectory This application breaks through the hardware dependence of traditional positioning technologies on multi-physics arrays, and innovatively utilizes the motion trajectory of high-speed mobile platforms (such as high-speed trains and drones) to construct an equivalent "synthetic aperture virtual array" in the time dimension through coherent processing. This technology effectively solves the core pain point of high-speed mobile platforms being unable to deploy large-scale antenna arrays due to limited installation space and load, and can still achieve high-precision angle of arrival (DOA) estimation under engineering constraints.

[0057] 2. Spatiotemporal fusion positioning mechanism of angle of arrival and positioning observations This application constructs a joint positioning model based on angle of arrival (Angle of Arrival) and positioning observations (e.g., Time Difference of Arrival). It deeply integrates the angular constraints provided by the Angle of Arrival with the hyperbolic distance constraints provided by the positioning observations, forming a dual spatiotemporal constraint positioning system of "angle + time difference." By constructing and solving the joint optimization objective function, the geometric accuracy factor of single-parameter positioning is significantly improved, enhancing the convergence speed and robustness of the positioning solution.

[0058] 3. AI-driven multipath recognition and fusion weight adaptive mechanism This application deeply integrates artificial intelligence technology into the positioning link, establishing an AI-based assisted decision-making and optimization module. This module can output signal type discrimination, multipath / non-line-of-sight (NLOS) propagation environment identification, and path confidence score in real time, and adaptively adjust the positioning fusion weights accordingly. In complex electromagnetic environments such as mountainous areas, tunnels, and urban building complexes, this mechanism can significantly suppress multipath effects and NLOS errors, greatly improving the positioning performance of the system.

[0059] 4. A universal real-time positioning system for high-speed mobile platforms across speed domains This application proposes a highly versatile real-time positioning architecture that can be widely adapted to high-speed motion platforms of different speed levels. Experiments show that, whether it is a high-speed train with a speed of 300 km / h, a drone with a speed of ≤100 km / h, or a low-orbit satellite with a speed of 7.8 km / s, this system can maintain stable positioning performance through adaptive algorithms, effectively solving the problems of poor adaptability to scenarios and difficulty in taking into account platforms with different dynamic characteristics in existing technologies.

[0060] 5. An integrated closed-loop architecture of "synthetic array + multi-source fusion + AI" This application deeply integrates virtual array construction, multi-source parameter fusion positioning, and artificial intelligence optimization technologies to form a complete closed-loop control system encompassing "environmental perception—intelligent judgment—weight adjustment—precise positioning." This architecture achieves full-process intelligence from signal acquisition to position calculation, significantly improving the system's comprehensive processing capabilities in complex dynamic environments.

[0061] Beneficial effects of this application 1. Significantly improves positioning accuracy to meet the needs of source tracing and disposal. Compared to single TDOA, FDOA, or PDOA positioning technologies, this application significantly reduces positioning errors through multi-source fusion and AI optimization. In simulation verification scenarios, for high-speed rail and drone scenarios, the planar positioning accuracy can reach ≤100m (CE90); for low-Earth orbit satellite scenarios, the planar positioning accuracy can reach ≤500m (CE90). This accuracy fully meets the practical engineering requirements for accurate source tracing and subsequent handling of radio interference.

[0062] 2. Significantly reduce hardware costs and deployment barriers This application achieves high-performance positioning using a single antenna or a small number of antennas, eliminating the need for complex multi-element physical antenna arrays and high-precision inter-station clock synchronization modules. In the case of GNSS interference positioning, compared with traditional UAV-borne positioning equipment solutions, hardware costs are reduced by more than 70%, and equipment size is reduced by 80%, greatly alleviating the platform load burden and perfectly adapting to the stringent deployment requirements of high-speed mobile platforms for lightweight and low-power equipment.

[0063] 3. Excellent system real-time performance The optimized joint solution algorithm and lightweight AI model within this community ensure the system's high-efficiency operation. Real-world testing shows that the total processing latency of the joint solution and AI optimization is ≤5ms, fully meeting the timeliness requirements for real-time or near-real-time location of interference sources in high-speed mobile scenarios such as high-speed rail, drones, and satellites.

[0064] 4. Extremely strong anti-interference and environmental adaptability By introducing a joint compensation module and an AI intelligent recognition module, this application can effectively eliminate systematic errors such as Doppler frequency shift caused by high-speed motion, and strongly suppress multipath interference and measurement noise in complex electromagnetic environments. Under harsh conditions such as multiple interferences and weak signals, the system's positioning success rate remains at ≥90%, demonstrating extremely high robustness.

[0065] 5. Wide applicability and promotional value This application exhibits strong platform adaptability and can be directly applied to various high-speed carriers such as high-speed rail, drones, and satellites without significant modifications to the core architecture. This technology can be widely applied to multiple fields, including radio spectrum monitoring, interference investigation, and navigation safety assurance, demonstrating significant social benefits and application prospects.

[0066] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0067] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be an electrical connection or other forms.

[0069] See Figure 9 , Figure 9 An electronic device is provided, including a processor and a memory. The memory stores computer instructions or one or more programs, which, when executed by the processor, cause the processor to execute the computer instructions to achieve the following: Figure 7 and Figure 8 The method and its detailed scheme are shown.

[0070] It should be understood that the above-described device embodiments are merely illustrative, and the device disclosed in this invention can be implemented in other ways. For example, the division of units / modules described in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, integrated into another system, or some features may be ignored or not executed.

[0071] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of the present invention can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0072] When the integrated unit / module is implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor or chip can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the on-chip cache, off-chip memory, and storage can be any suitable magnetic or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0073] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer electronic device (which may be a personal computer, server, or network electronic device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0074] This application also provides a computer-readable storage medium storing one or more computer programs, which, when executed by a processor, cause the processor to perform the following actions: Figure 7 and Figure 8 The method and its detailed scheme are shown.

[0075] This application also provides a computer program product, which includes a computer program that, when run on a computer, causes the computer to perform the methods of any of the above embodiments.

[0076] References to features, advantages, or similar language in this specification do not imply that all features and advantages achievable with this solution should be included or included in any single implementation thereof. Rather, references to features and advantages are understood to mean that a particular feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of this solution. Therefore, discussions of features, advantages, and similar language throughout this specification may, but do not necessarily, refer to the same embodiments.

[0077] Furthermore, the features, advantages, and characteristics described herein can be combined in any suitable manner in one or more embodiments. Based on the description herein, those skilled in the art will recognize that this solution can be implemented without one or more specific features or advantages of a particular embodiment. In other instances, additional features and advantages can be appreciated in specific embodiments not presented in all embodiments of this solution.

[0078] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this application. Furthermore, any changes or modifications made by those skilled in the art based on the ideas of this application, and on the specific implementation methods and application scope of this application, are all within the scope of protection of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for locating radio interference sources on a high-speed mobile platform, characterized in that, include: (a) Within the current time window, by constructing a synthetic aperture virtual array, the signal model matrix data corresponding to the physical antenna at each moment in the current time window is determined, wherein the physical antenna is installed in the high-speed mobile platform; (b) Calculate the estimated angle of arrival based on the signal model matrix data; (c) Calculate the estimated values ​​of the positioning observations based on the signal model matrix data; (d) By fusing the estimated angle of arrival and the estimated location observations, a preliminary location estimate of the interference source is determined using a pre-constructed optimization objective function and the current weighting coefficients of the optimization objective function; (e) Optimize the weight coefficients in the objective function using an artificial intelligence model based on the estimated angle of arrival, the estimated location observations, and the preliminary location estimate, and determine the optimized weight coefficients as the current weight coefficients of the objective function, then return to step (d); and (f) If the optimized weight coefficients meet the preset conditions, the location estimate of the interference source is determined by the optimization objective function based on the weight coefficients that meet the preset conditions, so as to determine the final location estimate of the current time window.

2. The method as described in claim 1, characterized in that, Also includes: For any one of the multiple time windows within the preset observation period, execute steps (a)-(f) to obtain the final position estimate of the multiple time windows.

3. The method as described in claim 2, characterized in that, Also includes: The final position estimates of the multiple time windows are filtered and / or smoothed to estimate the trajectory of the interference source; as well as Based on the trajectory estimation of the interference source, the motion trend and location prediction of the interference source are determined.

4. The method according to any one of claims 1 to 3, characterized in that, The signal model corresponding to the constructed synthetic aperture virtual array includes: in, This represents the signal model matrix data at time k. Let k be the spatial position vector of the platform at time k. Let be the incident direction vector of the interference source signal. s is the signal wavelength, and s is the signal source amplitude. It is Gaussian white noise.

5. The method according to any one of claims 1 to 3, characterized in that, Step (b) includes: The estimated angle of arrival is determined based on the signal model matrix data using a subspace decomposition algorithm.

6. The method according to any one of claims 1 to 3, characterized in that, Step (c) includes: The estimated values ​​of the positioning observations are determined based on the signal model matrix data using a cross-correlation algorithm.

7. The method according to any one of claims 1 to 3, characterized in that, Step (e) includes: The time-frequency plot of the interference source signal, the covariance matrix of the synthetic aperture virtual array, the estimated angle of arrival, the estimated location observations, and the preliminary position estimate are input into the artificial intelligence model; and The artificial intelligence model optimizes the weight coefficients in the objective function to determine the optimized weight coefficients.

8. A device for locating radio interference sources on a high-speed mobile platform, characterized in that, include: The first determining module is used to determine the signal model matrix data of the physical antenna at each moment in the current time window by constructing a synthetic aperture virtual array, wherein the physical antenna is installed in the high-speed mobile platform; The first calculation module is used to calculate the estimated angle of arrival based on the signal model matrix data; The second calculation module is used to calculate the estimated value of the positioning observation based on the signal model matrix data; The fusion module is used to fuse the estimated angle of arrival and the estimated location observations, and determine the preliminary location estimate of the interference source by using a pre-constructed optimization objective function and the current weight coefficients of the optimization objective function. The optimization module is used to optimize the weight coefficients in the objective function using an artificial intelligence model based on the estimated angle of arrival, the estimated location observations, and the preliminary position estimate, and to determine the optimized weight coefficients as the current weight coefficients of the objective function; and The second determining module is used to determine the location estimate of the interference source based on the optimized weight coefficients that meet the preset conditions, through the optimization objective function, so as to determine the final location estimate of the current time window.

9. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program in the memory, implements the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

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