Civil aviation GPS interference source monitoring and troubleshooting method and system

Abstract

The invention relates to the technical field of GPS interference source monitoring and troubleshooting, in particular to a civil aviation GPS interference source monitoring and troubleshooting method and system. The method comprises the following steps: acquiring track position data, attitude parameter data, GPS signal receiving data and interference detection performance parameters of a plurality of aircrafts, and establishing aircraft nodes and interference nodes; generating a common edge between any two aircraft nodes, generating a corresponding special edge between each interference node and the aircraft node within a preset influence radius, and constructing a space-time interference propagation topological graph at each moment; calculating an interference behavior characteristic parameter to obtain an interference behavior characteristic parameter vector, and calculating and outputting an interference behavior type identifier and an interference evolution trend vector corresponding to each interference node by using an interference behavior evolution trend inversion algorithm; and predicting an early warning interference source and generating an interference early warning result. According to the invention, real-time positioning, behavior identification and future influence range prediction of multiple types of interference sources in the civil aviation airspace are realized.

Description

Technical Field

[0001] This invention relates to the field of GPS interference source monitoring and investigation technology, specifically to a method and system for monitoring and investigating GPS interference sources in civil aviation. Background Technology

[0002] In the field of civil aviation navigation interference monitoring and investigation, existing civil aviation interference investigation mainly relies on ground monitoring stations to perform spectrum scanning or on single-unit equipment to perform interference assessment. The investigation process usually uses signal strength measurement, direction finding analysis, and interference frequency band identification as the basis for data judgment. In conventional investigation methods, interference events are generally located and calculated in a static point manner, and the impact of interference propagation is mostly assessed by distance attenuation models. The interference location and investigation process lacks the ability to model the spatial topological relationship between different flight platforms, making it difficult to make intelligent judgments based on the changes in interference behavior over time. In actual civil aviation operations, when flight training or missions are conducted in complex electromagnetic environments at low and medium altitudes, interference sources may include GPS jammers, transient radiation leakage caused by aging active electronic devices, intermittent wireless radiation caused by insulation damage to industrial equipment, or abnormal output from temporary signal transmitting equipment. This can lead to the following two problems: First, when the interference source is moving or affected by ground cover, the single direction finding result deviates from the actual propagation path of the interference, causing the positioning result to jump as the flight position changes. Second, when the interference source signal has intermittent triggering, energy mutation, or irregular temporal fluctuation characteristics, conventional methods cannot construct an interference propagation correlation structure between flight nodes, resulting in inconsistent perception results of the same interference source by different flight platforms. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for monitoring and investigating GPS interference sources in civil aviation, so as to solve the two problems mentioned in the background art.

[0004] To achieve the above objectives, the technical solution of the present invention is: a method for monitoring and investigating interference sources in civil aviation GPS, comprising: S1. Collect track position data, attitude parameter data, GPS signal reception data and interference detection performance parameters of multiple aircraft, and establish aircraft nodes and interference nodes when the interference detection performance parameters meet the interference judgment conditions. S2. Generate a normal edge between any two aircraft nodes, and generate a corresponding special edge between each interference node and an aircraft node within a preset influence radius. Construct a spatiotemporal interference propagation topology graph at each moment, assign a time index to all spatiotemporal interference propagation topology graphs, and form a sequence of spatiotemporal interference propagation topology graphs arranged in chronological order. Among them, the edge weight information of ordinary edges includes the spatial distance between aircraft nodes; the edge weight of special edges is an edge weight vector composed of the spatial distance between the interfering node and the aircraft node, the interference signal strength value of the interference source, and the interference propagation shielding effect parameter. S3. In the spatiotemporal interference propagation topology graph under adjacent time indices, perform temporal alignment on the same special edge to generate the interference continuous topology evolution sequence of the interference node, and calculate the interference behavior feature parameters to obtain the interference behavior feature parameter vector. Use the interference behavior evolution trend inversion algorithm to calculate and output the interference behavior type identifier and interference evolution trend vector corresponding to each interference node. Among them, the interference behavior evolution trend inversion algorithm is an interference behavior inversion algorithm implemented based on the topological structure of the spatiotemporal interference propagation topology map and combined with the time series prediction algorithm. S4. Based on the interference evolution trend vector of the interference node and the flight path data of the aircraft node, within the preset prediction time window, calculate the spatial positional relationship and interference intensity estimate of the interference node to the aircraft node within the preset influence radius at each future time, predict and warn the interference source and generate interference warning results.

[0005] Preferably, in S1, the aircraft node is a flight object data structure unit established at the current moment based on the collected aircraft trajectory position data and attitude parameter data; the interference node is an interference event data structure unit established when the interference detection performance parameters meet the interference judgment conditions. The specific method for establishing aircraft nodes and interference nodes includes: reading aircraft track position data and attitude parameter data at the current moment to establish corresponding aircraft nodes; establishing interference nodes when interference detection performance parameters meet the interference judgment threshold; and recording the spatial location information, interference signal strength value, and time index data corresponding to the interference nodes.

[0006] Preferably, in S2, the spatiotemporal interference propagation topology is a topology model formed at the current moment by a set of nodes consisting of aircraft nodes and interference nodes and a set of edges consisting of ordinary edges and special edges. The topology model is sequentially associated through time index to form a data structure sequence of continuous multi-time moments, which is used to characterize the spatial propagation relationship of the interference source to each aircraft node at different times. The method for constructing the spatiotemporal interference propagation topology graph includes: adding all established aircraft nodes and interference nodes to the topology model at the current moment; generating ordinary edges for any two aircraft nodes and recording the edge weight information of the ordinary edges; generating special edges for each interference node and aircraft node within a preset influence radius and recording the edge weight vector of the special edges; combining the established nodes and edges into a topology model; and assigning a corresponding time index to the topology model to obtain the spatiotemporal interference propagation topology graph.

[0007] Preferably, in S2, the spatiotemporal interference propagation topology graph is also used to structurally represent the interference nodes causing interference to each aircraft node at continuous times; the graph structure of the spatiotemporal interference propagation topology graph is specifically as follows: the node set is identified and managed based on node identifiers, ordinary edges and special edges are associated and stored based on edge weight data structure, and the node set and edge set are dynamically expanded and replaced according to the time index order to form a continuous topology evolution sequence of interference. The interference continuous topological evolution sequence serves as the topological constraint input for the interference behavior evolution trend inversion algorithm. It is used to limit whether the interference behavior evolution trend inversion algorithm depends on the topological connection features between nodes when calculating the interference behavior type identifier and the interference evolution trend vector.

[0008] Preferably, in step S3, the interference continuous topology evolution sequence is a topology sequence formed by dynamically expanding and replacing the node set and edge set under multiple continuous time indices. It represents the topological relationship and edge weight change characteristics between all nodes during the temporal propagation of the interference source by temporally aligning the same special edge. The generation process of the interference continuous topology evolution sequence includes: updating the node set and edge set at each time index to form the topology at the current time, temporally aligning the edge weight vectors of the special edges corresponding to the same interference node at consecutive time indices, and combining the topologies of multiple aligned continuous time indices to form the interference continuous topology evolution sequence. The interference behavior feature parameters are behavior representation parameters calculated by the edge weight time series sequence of special edges based on the changes of edge weight vectors in the continuous topological evolution sequence of interference, and are used to generate the interference behavior feature parameter vector.

[0009] Preferably, in step S3, the interference behavior evolution trend inversion algorithm is an interference behavior inversion algorithm based on the topological structure of the spatiotemporal interference propagation topology graph. It uses the interference continuous topology evolution sequence formed by the spatiotemporal interference propagation topology graph under continuous time index as the topological structure constraint input, and the interference behavior feature parameter vector extracted based on the interference continuous topology evolution sequence as the calculation input to infer the interference behavior type identification and interference evolution trend vector. During execution, the interference behavior evolution trend inversion algorithm calculates the parameters of the interference behavior feature parameter vector and combines the temporal evolution structure of the node set and edge set in the spatiotemporal interference propagation topology graph to perform topological constraint reasoning on the interference propagation path direction, propagation intensity change trend and propagation shielding influence mechanism of the interference source, so as to predict and output the interference behavior type identifier and interference evolution trend vector of the interference source.

[0010] Preferably, in step S3, the interference behavior type identifier is the behavior type identifier field of the interference source at the current time, which is determined by the interference behavior evolution trend inversion algorithm and is used to characterize the behavior type state corresponding to the current interference source during the process of tracing the interference source; the interference evolution trend vector is calculated based on the interference behavior evolution trend inversion algorithm according to the interference behavior feature parameter vector and the temporal change trend of the edge weight of special edges in the spatiotemporal interference propagation topology graph, and is used to reflect the change of interference behavior of the interference node under the future time index.

[0011] On the other hand, the present invention provides a civil aviation GPS interference source monitoring and investigation system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the aforementioned civil aviation GPS interference source monitoring and investigation method.

[0012] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial technical effects: 1. In this invention, the spatiotemporal interference propagation topology map constructs the spatial connection relationship between aircraft nodes and interference nodes under different time indices. It can maintain the continuous depiction of the interference propagation path even when the interference source is in a moving state or has a shielding effect, and realize the comprehensive judgment of the same interference event from multiple flight perspectives. 2. In this invention, the interference behavior feature parameter vector is inferred by the interference behavior evolution trend inversion algorithm, and the interference behavior type identifier and interference evolution trend vector are output. This enables the calculation and identification of the continuous influence changes of the interference source, and can predict the interference period and influence range of the interference signal on the flight track in advance when there are irregular triggering or intensity fluctuations in the interference signal. Attached Figure Description

[0013] Figure 1 This is a flowchart of one embodiment of the present invention. Detailed Implementation

[0014] Example 1, as Figure 1 As shown, the present invention proposes a method for monitoring and investigating interference sources in civil aviation GPS, and its specific implementation steps are as follows: S1. Collect track position data, attitude parameter data, GPS signal reception data and interference detection performance parameters of multiple aircraft, and establish aircraft nodes and interference nodes when the interference detection performance parameters meet the interference judgment conditions. S2. Generate a normal edge between any two aircraft nodes, and generate a corresponding special edge between each interference node and an aircraft node within a preset influence radius. Construct a spatiotemporal interference propagation topology graph at each moment, assign a time index to all spatiotemporal interference propagation topology graphs, and form a sequence of spatiotemporal interference propagation topology graphs arranged in chronological order. Among them, the edge weight information of ordinary edges includes the spatial distance between aircraft nodes; the edge weight of special edges is an edge weight vector composed of the spatial distance between the interfering node and the aircraft node, the interference signal strength value of the interference source, and the interference propagation shielding effect parameter. S3. In the spatiotemporal interference propagation topology graph under adjacent time indices, perform temporal alignment on the same special edge to generate the interference continuous topology evolution sequence of the interference node, and calculate the interference behavior feature parameters to obtain the interference behavior feature parameter vector. Use the interference behavior evolution trend inversion algorithm to calculate and output the interference behavior type identifier and interference evolution trend vector corresponding to each interference node. Among them, the interference behavior evolution trend inversion algorithm is an interference behavior inversion algorithm implemented based on the topological structure of the spatiotemporal interference propagation topology map and combined with the time series prediction algorithm. S4. Based on the interference evolution trend vector of the interference node and the flight path data of the aircraft node, within the preset prediction time window, calculate the spatial positional relationship and interference intensity estimate of the interference node to the aircraft node within the preset influence radius at each future time, predict and warn the interference source and generate interference warning results.

[0015] In this embodiment S1, the aircraft node is a flight object data structure unit established at the current moment based on the collected aircraft trajectory position data and attitude parameter data; the interference node is an interference event data structure unit established when the interference detection performance parameters meet the interference judgment conditions. The specific method for establishing aircraft nodes and interference nodes includes: reading aircraft track position data and attitude parameter data at the current moment to establish corresponding aircraft nodes; establishing interference nodes when interference detection performance parameters meet the interference judgment threshold; and recording the spatial location information, interference signal strength value, and time index data corresponding to the interference nodes.

[0016] In this embodiment S1, the track position data is a set of position parameters including longitude, latitude, altitude, and speed output by the airborne navigation system at the current moment. The attitude parameter data is a set of attitude angle parameters including roll angle, pitch angle, and yaw angle collected by the airborne attitude sensing components and output by the flight control system. The GPS signal reception data is a set of signal quality parameters including signal-to-noise ratio, pseudorange measurement offset, and satellite channel status output by the airborne GPS receiving equipment after real-time demodulation of satellite signals. The interference detection performance parameters are a set of interference-sensitive characteristic parameters including signal energy fluctuation value, signal phase stability, and interference-to-noise ratio output by the airborne anti-interference components after processing the GPS signal processing data. The track position data, attitude parameter data, and GPS signal reception data are collected in real time by the airborne sensor components, processed by the flight control system, and then uploaded to the interference monitoring data processing unit. The interference detection performance parameters are directly obtained by the anti-interference monitoring module during the data parsing stage of the GPS signal processing process.

[0017] In this embodiment S1, the interference determination condition is a logical judgment condition used to determine whether there is an interference event at the current moment. Specifically, it may include at least one of the following: the signal carrier-to-noise ratio is lower than a preset threshold, the phase fluctuation range exceeds a preset change range, or the interference-to-noise ratio exceeds a preset intensity threshold. When any parameter in the interference detection performance parameters meets the interference determination condition, it is considered that there is interference at the current moment. When the interference detection performance parameters meet the interference determination condition, an interference node is established. At the same time, corresponding aircraft nodes are established for all aircraft that have collected track position data and attitude parameter data at the current moment.

[0018] In this embodiment S1, the aircraft node is the established flight object data structure unit. The flight object data structure unit includes a node identifier field for identifying the aircraft, a spatial coordinate field for recording the current aircraft track position data, an attitude parameter field for recording the current attitude parameter data, and a time index field for recording the node establishment time. The interference node is the established interference event data structure unit. The interference event data structure unit includes a node identifier field for representing the subject that triggers the interference event, an interference position field for recording the spatial position of the interference node at the current time, an interference strength field for recording the interference signal strength value of the interference source, a time index field for recording the interference detection time, and an interference propagation shielding effect parameter field that can be selected according to actual execution requirements. The specific composition of each field in the data structure unit can be set or expanded according to the sampling accuracy requirements of the actual operating environment and the data compatibility requirements of different aircraft types.

[0019] In this embodiment S2, the spatiotemporal interference propagation topology is a topological structure model formed by a set of nodes consisting of aircraft nodes and interference nodes and a set of edges consisting of ordinary edges and special edges at the current moment. The topological structure model is sequentially associated through time index to form a data structure sequence of continuous multi-time moments, which is used to characterize the spatial propagation association relationship of the interference source to each aircraft node at different times. The method for constructing the spatiotemporal interference propagation topology graph includes: adding all established aircraft nodes and interference nodes to the topology model at the current moment; generating ordinary edges for any two aircraft nodes and recording the edge weight information of the ordinary edges; generating special edges for each interference node and aircraft node within a preset influence radius and recording the edge weight vector of the special edges; combining the established nodes and edges into a topology model; and assigning a corresponding time index to the topology model to obtain the spatiotemporal interference propagation topology graph.

[0020] In this embodiment S2, the aircraft node within the preset influence radius is the aircraft node whose spatial distance from the interfering node at the current moment is less than the influence range threshold determined based on the propagation characteristics of the interference source. The preset influence radius is preset according to the type of interference source and the attenuation law of propagation intensity, combined with the aircraft flight altitude, the frequency band characteristics of the interference source, and the electromagnetic field propagation model. The electromagnetic field propagation model is a signal propagation prediction model commonly used in this technical field, and an applicable model can be selected from existing models according to the type of interference source and the operating environment.

[0021] In this embodiment S2, the spatiotemporal interference propagation topology map can be constructed using graph data structure construction technology, time-series data index management technology, or continuous time-based topology generation technology. The edge weight information of ordinary edges is the spatial distance between aircraft nodes, which is calculated based on node position coordinate parameters and obtained using Euclidean distance calculation method or spherical coordinate distance calculation method. The edge weight vector of special edges is a vector structure composed of the spatial distance between the interference node and the aircraft node, the interference source interference signal strength value, and interference propagation shielding influence parameters. The spatial distance is calculated based on the interference node position data and the aircraft node position data. The interference source interference signal strength value is extracted from the signal carrier-to-noise ratio or signal energy fluctuation parameters in the airborne GPS signal reception data. The interference propagation shielding influence parameters are calculated based on interference detection performance parameters and the geographical location and airframe structural obstruction conditions within the civil aviation operating space.

[0022] In this embodiment S2, the edge weight vector is a data vector structure that records the spatial distance field, interference intensity field, and shielding influence parameter field in a fixed field order. The time index of the spatiotemporal interference propagation topology map is formed by assigning monotonically increasing time tags to the construction time corresponding to each topology map and arranging the topology map structure data in time order.

[0023] In this embodiment S2, the spatiotemporal interference propagation topology graph is also used to structurally represent the interference nodes causing interference to each aircraft node at continuous time. The graph structure of the spatiotemporal interference propagation topology graph is as follows: the node set is identified and managed based on node identifiers, ordinary edges and special edges are associated and stored based on edge weight data structure, and the node set and edge set are dynamically expanded and replaced according to the time index order to form a continuous topology evolution sequence of interference. The interference continuous topological evolution sequence serves as the topological constraint input for the interference behavior evolution trend inversion algorithm. It is used to limit whether the interference behavior evolution trend inversion algorithm depends on the topological connection features between nodes when calculating the interference behavior type identifier and the interference evolution trend vector.

[0024] In this embodiment S2, the spatiotemporal interference propagation topology graph uniquely identifies and manages the node set based on node identifiers, and establishes an association storage relationship between ordinary edges and special edges and their corresponding nodes based on the edge weight data structure. By updating the node set and edge set at each time step and recording the current time index for the newly created topology graph, a continuous topology structure based on time series is formed. When the topology graph is generated at adjacent time steps, the updated node position data and updated edge weight data in the node set are replaced, while the unchanged node and edge structure is retained, thereby forming an scalable topology evolution sequence. The interference continuous topology evolution sequence is input to the interference behavior evolution trend inversion algorithm to provide topology constraints, so that the interference behavior evolution trend inversion algorithm relies on the topology connection features between nodes when calculating the interference behavior type identifier and the interference evolution trend vector. The topology connection features between nodes are whether there is a valid edge connection relationship between any pair of nodes and the change relationship of the corresponding edge weight vector of the edge connection relationship at adjacent time steps.

[0025] In this embodiment S3, the interference continuous topology evolution sequence is a topology sequence formed by dynamically expanding and replacing the node set and edge set under multiple continuous time indices. By temporally aligning the same special edge, it represents the topological relationship and edge weight change characteristics between all nodes during the temporal propagation of the interference source. The generation process of the interference continuous topology evolution sequence includes: updating the node set and edge set at each time index to form the topology at the current time, temporally aligning the edge weight vectors of the special edges corresponding to the same interference node at consecutive time indices, and combining the topologies of multiple aligned continuous time indices to form the interference continuous topology evolution sequence. The interference behavior feature parameters are behavior representation parameters calculated by the edge weight time series sequence of special edges based on the changes of edge weight vectors in the continuous topological evolution sequence of interference, and are used to generate the interference behavior feature parameter vector.

[0026] In this embodiment S3, the method for temporal alignment of special edges corresponding to the same interference node under adjacent time indices is to match special edges belonging to the same interference node according to the interference node identifier in the spatiotemporal interference propagation topology map at consecutive moments, so that multiple sets of special edges with the same interference node identifier and connecting objects of different aircraft nodes form a temporal sequence of edge weights according to the time index order; the temporal alignment process retains the node and edge structure established under the previous time index when dynamically expanding the node set and edge set, and performs insertion processing on newly added nodes and edges, while performing replacement or removal processing on nodes and edges that no longer meet the preset edge generation conditions, so that the node set and edge set in the topology map under adjacent time indices still maintain traceability after the structure is updated. The topological relationship between nodes is whether there is a connection relationship between ordinary edges or special edges between node pairs. The edge weight change feature is the change of the spatial distance field, interference intensity field and shielding influence parameter field in the edge weight vector of special edges under adjacent time indices. The edge weight vector change in the continuous topological evolution sequence of interference is the sequence change structure formed by arranging the edge weight vectors of the above special edges in a unified index order at multiple consecutive moments.

[0027] In this embodiment S3, the process of calculating the behavior representation parameters from the edge weight time sequence of special edges involves extracting the rate of change parameter, direction of change parameter, or stability of change parameter from the edge weight vector corresponding to multiple consecutive time points, and generating a set of behavior representation parameters according to preset calculation rules. The behavior representation parameters include, but are not limited to, the rate of change of interference signal intensity, the direction of change of interference propagation path, and the fluctuation parameter of interference shielding effect. The interference behavior feature parameter vector is a multi-dimensional vector data structure formed by combining the above behavior representation parameters in a fixed field order, which is used as the calculation input of the interference behavior evolution trend inversion algorithm.

[0028] In this embodiment S3, the interference behavior evolution trend inversion algorithm is an interference behavior inference algorithm based on the spatiotemporal interference propagation topology graph. It uses the continuous topological evolution sequence of interference formed by the spatiotemporal interference propagation topology graph under continuous time index as the topological constraint input, and the interference behavior feature parameter vector extracted based on this continuous topological evolution sequence as the calculation input to infer the interference behavior type identification and interference evolution trend vector. The interference behavior evolution trend inversion algorithm is an interference behavior inference algorithm based on the spatiotemporal interference propagation topology graph and combined with a time-series prediction algorithm. The time-series prediction algorithm can be constructed based on existing technologies, including Kalman filtering, time-series inference algorithms based on state-space models, time-series prediction algorithms based on recurrent neural network structures, and sequence prediction algorithms based on attention mechanisms. When the above time-series prediction algorithms are combined with the spatiotemporal interference propagation topology graph to construct the interference behavior evolution trend inversion algorithm, they are used according to the different characteristics of the interference source behavior and the operating scenario. Specifically, when the interference propagation process exhibits a continuous and smooth change, the Kalman filtering algorithm can be combined with the topological node connection relationship to... Continuous state estimation is performed based on the edge weight temporal sequence. When interference behavior is affected by environmental changes, intermittent triggering, or nonlinear trends, a temporal inference algorithm based on a state-space model can be used, combined with the connection constraints between nodes in the topology for state transition inversion. When the interference signal change process has long temporal dependence or the interference propagation path has complex temporal characteristics, a temporal prediction algorithm based on a recurrent neural network structure can be used, combined with the special edge temporal change parameters in the topology for network input construction. When the interference source exhibits alternating triggering by multiple nodes during propagation or the interference behavior change has spatial selectivity, an attention mechanism sequence prediction algorithm can be used, combined with the topological coupling relationship between nodes in the topology for weight allocation to generate prediction input. The interference behavior evolution trend inversion algorithms constructed by the above-mentioned temporal prediction algorithms all use the interference continuous topological evolution sequence formed by the evolution of the spatiotemporal interference propagation topology under continuous time index as the topological constraint input, and use the interference behavior feature parameter vector calculated based on the interference edge weight temporal sequence as the calculation input, outputting the interference behavior type identifier for determining the interference source behavior state and the interference evolution trend vector for describing the interference trend.

[0029] In this embodiment S3, the interference behavior evolution trend inversion algorithm calculates the interference behavior feature parameter vector during execution, and combines the temporal evolution structure of the node set and edge set in the spatiotemporal interference propagation topology graph to perform topological constraint reasoning on the interference propagation path direction, propagation intensity change trend and propagation shielding influence mechanism of the interference source, so as to predict and output the interference behavior type identifier and interference evolution trend vector of the interference source.

[0030] In this embodiment S3, the interference behavior evolution trend inversion algorithm is an algorithm that performs interference behavior inversion calculation based on the spatiotemporal interference propagation topology graph. The spatiotemporal interference propagation topology graph is a topological connection structure formed by the set of nodes consisting of aircraft nodes and interference nodes and the set of edges consisting of ordinary edges and special edges under each time index. The topological structure constraint input is the node connection relationship formed by the change of the set of nodes and the set of edges in the continuous topological evolution sequence of interference with the change of time index, and the edge weight change characteristics of special edges. It is used to constrain the inversion algorithm to rely on the topological connection relationship for structural judgment when performing interference behavior mechanism calculation during the inference process. The calculation input is the interference behavior feature parameter vector extracted and combined based on the continuous topological evolution sequence of interference. It is used as the source of numerical parameters for behavior mechanism inference in the inversion algorithm. The difference between the two is that the topological structure constraint input is used to provide the structural dependency of the interference propagation relationship, while the calculation input is used to provide the numerical characteristic dependency of the behavior law.

[0031] In this embodiment S3, the interference behavior evolution trend inversion algorithm performs parameter calculation on the interference behavior feature parameter vector by performing behavior change trend analysis based on the interference signal strength change rate parameter, propagation path direction change parameter, and shielding effect fluctuation parameter included in the interference behavior feature parameter vector, and combines the temporal evolution structure of the node set and edge set in the spatiotemporal interference propagation topology graph; the temporal evolution structure is a continuous topology model formed by updating the node position field and edge weight vector field of the topology structure according to the time index, which is used to cooperate with parameter calculation to realize the inversion of the interference propagation process. The interference propagation path direction of the interference source is the node relative direction change feature formed by the change of special edge connection relationship with the time index, the propagation strength change trend is the interference signal strength field change trend, and the propagation shielding effect mechanism is the shielding effect feature formed by the change relationship of the shielding effect parameter field; the topology constraint reasoning is to perform structured inversion calculation on the behavior mechanism by combining the topology constraint input and calculation input during the algorithm execution process. The reasoning process can be implemented by graph structure behavior modeling algorithm, topology constraint behavior reasoning algorithm, or multi-time parameter fusion analysis algorithm, and outputs the interference behavior type identifier and interference evolution trend vector after the reasoning is completed.

[0032] In this embodiment S3, the interference behavior type identifier is the behavior type identifier field of the interference source at the current time, which is determined by the interference behavior evolution trend inversion algorithm. It is used to characterize the behavior type state corresponding to the current interference source during the process of tracing the interference source. The interference evolution trend vector is calculated based on the interference behavior evolution trend inversion algorithm, according to the interference behavior feature parameter vector and the temporal change trend of the edge weight of special edges in the spatiotemporal interference propagation topology graph. It is used to reflect the change of interference behavior of the interference node under the future time index.

[0033] In this embodiment S3, the interference behavior type identifier is a data field used to identify which type of interference behavior state the interference node currently belongs to, determined by the interference behavior evolution trend inversion algorithm based on the parameter change law in the interference behavior feature parameter vector. The interference behavior type may include interference behavior with stable interference source location, interference behavior with moving interference source location, interference behavior with fluctuating interference signal strength, and interference behavior with limited interference propagation shielding. To achieve the determination of the above different types of interference behavior, a behavior classification determination technology based on multi-parameter edge weight change analysis or a interference behavior state identification technology based on topological structure features can be used to complete the interference behavior type identification. The generation process of the knowledge; the interference evolution trend vector is a data structure formed by the interference behavior evolution trend inversion algorithm after performing trend calculations on the rate of change parameter, direction change parameter and shielding influence fluctuation parameter in the interference behavior feature parameter vector. It is used to characterize the possible changes in propagation path direction, interference intensity and shielding influence direction of the interference node under multiple time indices in the future; the interference evolution trend vector can be realized by multi-time parameter trend analysis technology or topology-driven interference evolution prediction calculation technology. Its data structure is a vector form that records the prediction time index field, interference intensity trend parameter field, propagation direction trend parameter field and shielding influence trend parameter field in a fixed order.

[0034] In this embodiment S3, the temporal change trend of the edge weights of special edges in the spatiotemporal interference propagation topology graph is a change sequence formed by arranging the spatial distance field, interference intensity field, and shielding influence parameter field in the edge weight vector of the special edge in a unified index order under continuous time index. This change sequence is used to provide the basis for interference propagation direction reasoning and interference intensity change trend reasoning for the interference behavior evolution trend inversion algorithm. The reason why the interference behavior evolution trend inversion algorithm can output two different types of data, interference behavior type identifier and interference evolution trend vector, is that during the execution of the algorithm, it completes the interference propagation structure identification processing based on the topological structure constraint input and completes the interference behavior change trend inference processing based on the calculation input, thereby forming behavior classification data and trend inference data respectively.

[0035] In this embodiment S4, the flight path data of the aircraft node is a set of time position parameters output by the airborne flight control system and processed by the position calculation module to describe the future flight path of the aircraft node. The data form of the flight path data is a time-series data structure that records the predicted time, predicted spatial coordinate field and predicted flight speed field of the aircraft node in time index order. The method of calculating the spatial position relationship and interference intensity estimate of the aircraft node and the interference node within the preset prediction time window at each future time is to calculate the position prediction parameter of the interference node under the future time index based on the interference evolution trend vector of the interference node, and calculate the spatial coordinate parameter of the aircraft node at the same time based on the flight path data of the aircraft node. The spatial distance between the interference node and the aircraft node at the future time is calculated as the spatial position relationship parameter based on the spatial distance calculation method. The interference intensity trend parameter in the interference evolution trend vector and the spatial position relationship parameter are combined with the interference propagation model to calculate the interference intensity estimate.

[0036] In this embodiment S4, the data forms of the interference evolution trend vector and the trajectory data are both data vector structures arranged in time index order. The spatial position relationship and the interference intensity estimate can be obtained by parameter matching operation, time series interpolation calculation, or joint calculation based on conventional flight path prediction technology and signal propagation estimation technology. The spatial position relationship is the spatial distance parameter between the interference node and the aircraft node at each future time index. The interference intensity estimate is the interference intensity parameter that may be generated on the aircraft node at each future time, calculated by the interference propagation model based on the interference intensity trend parameter and the spatial position parameter. The interference warning result is a data set formed after judging based on the above interference intensity estimate and the preset warning threshold, which is used to identify the affected aircraft node, the future impact time index field, and the interference intensity estimate parameter field. The preset warning threshold is used to determine the risk of the estimated interference intensity. The method for setting it is as follows: based on the civil aviation flight mission level, flight phase category, and GPS navigation signal performance requirements, the minimum navigation interference tolerance value specified in the civil aviation operation regulations is selected as the threshold benchmark; combined with the interference source type and the statistical distribution of interference intensity recorded in historical interference cases, the boundary intensity of typical interference impact intervals is calculated, and the interference tolerance coefficient is determined based on the aircraft node track altitude, aircraft type parameters, and interference detection performance parameters; by weighting the threshold benchmark and the interference tolerance coefficient, the preset warning threshold used for interference warning determination is obtained.

[0037] Example 2: The present invention proposes a civil aviation GPS interference source monitoring and investigation system, which is applied to the civil aviation GPS interference source monitoring and investigation method proposed in Example 1. It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the civil aviation GPS interference source monitoring and investigation method in Example 1.

[0038] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for monitoring and investigating interference sources in civil aviation GPS, characterized in that, Includes the following steps: S1. Collect track position data, attitude parameter data, GPS signal reception data and interference detection performance parameters of multiple aircraft, and establish aircraft nodes and interference nodes when the interference detection performance parameters meet the interference judgment conditions. S2. Generate a normal edge between any two aircraft nodes, and generate a corresponding special edge between each interference node and an aircraft node within a preset influence radius. Construct a spatiotemporal interference propagation topology graph at each moment, assign a time index to all spatiotemporal interference propagation topology graphs, and form a sequence of spatiotemporal interference propagation topology graphs arranged in chronological order. Among them, the edge weight information of ordinary edges includes the spatial distance between aircraft nodes; the edge weight of special edges is an edge weight vector composed of the spatial distance between the interfering node and the aircraft node, the interference signal strength value of the interference source, and the interference propagation shielding effect parameter. S3. In the spatiotemporal interference propagation topology graph under adjacent time indices, perform temporal alignment on the same special edge to generate the interference continuous topology evolution sequence of the interference node, and calculate the interference behavior feature parameters to obtain the interference behavior feature parameter vector. Use the interference behavior evolution trend inversion algorithm to calculate and output the interference behavior type identifier and interference evolution trend vector corresponding to each interference node. Among them, the interference behavior evolution trend inversion algorithm is an interference behavior inversion algorithm implemented based on the topological structure of the spatiotemporal interference propagation topology map and combined with the time series prediction algorithm. S4. Based on the interference evolution trend vector of the interference node and the flight path data of the aircraft node, within the preset prediction time window, calculate the spatial positional relationship and interference intensity estimate of the interference node to the aircraft node within the preset influence radius at each future time, predict and warn the interference source and generate interference warning results.

2. The method for monitoring and investigating interference sources in civil aviation GPS according to claim 1, characterized in that: In S1, the aircraft node is a flight object data structure unit established at the current moment based on the collected aircraft trajectory position data and attitude parameter data; the interference node is an interference event data structure unit established when the interference detection performance parameters meet the interference judgment conditions. The specific method for establishing aircraft nodes and interference nodes includes: reading aircraft track position data and attitude parameter data at the current moment to establish corresponding aircraft nodes; establishing interference nodes when interference detection performance parameters meet the interference judgment threshold; and recording the spatial location information, interference signal strength value, and time index data corresponding to the interference nodes.

3. The method for monitoring and investigating interference sources in civil aviation GPS according to claim 2, characterized in that: In S2, the spatiotemporal interference propagation topology is a topological structure model formed by a set of nodes consisting of aircraft nodes and interference nodes and a set of edges consisting of ordinary edges and special edges at the current moment. The topological structure model is sequentially associated through time index to form a data structure sequence of continuous multi-time moments, which is used to characterize the spatial propagation association relationship of the interference source to each aircraft node at different times. The method for constructing the spatiotemporal interference propagation topology map includes: adding all established aircraft nodes and interference nodes to the topology model at the current moment; For any two aircraft nodes, generate ordinary edges and record the edge weight information of ordinary edges. For each interfering node and an aircraft node within a preset influence radius, generate special edges and record the edge weight vector of special edges. Combine the established nodes and edges to form a topological structure model, and assign a corresponding time index to the topological structure model to obtain a spatiotemporal interference propagation topology graph.

4. The method for monitoring and investigating interference sources in civil aviation GPS according to claim 3, characterized in that: In S2, the spatiotemporal interference propagation topology graph is also used to structurally express the interference nodes causing interference to each aircraft node at continuous time. The graph structure of the spatiotemporal interference propagation topology graph is as follows: the node set is identified and managed based on node identifiers, ordinary edges and special edges are associated and stored based on edge weight data structure, and the node set and edge set are dynamically expanded and replaced according to the time index order to form a continuous topology evolution sequence of interference. The interference continuous topology evolution sequence serves as the topology constraint input for the interference behavior evolution trend inversion algorithm. It is used to limit whether the interference behavior evolution trend inversion algorithm depends on the topology connection features between nodes when calculating the interference behavior type identifier and the interference evolution trend vector.

5. The method for monitoring and investigating interference sources in civil aviation GPS according to claim 4, characterized in that: In S3, the interference continuous topology evolution sequence is a topology sequence formed by the dynamic expansion and replacement of node sets and edge sets under multiple continuous time indices. The topology relationship and edge weight change characteristics between all nodes during the temporal propagation of the interference source are represented by temporal alignment of the same special edge. The generation process of the interference continuous topology evolution sequence includes: updating the node set and edge set at each time index to form the topology structure at the current time, and performing temporal alignment on the edge weight vectors of the special edges corresponding to the same interference node at consecutive times, and combining the topology structures of multiple aligned consecutive time indices to form the interference continuous topology evolution sequence. The interference behavior feature parameters are behavior representation parameters calculated by performing edge weight time series sequences on special edges based on the changes in edge weight vectors in the continuous topological evolution sequence of interference, and are used to generate interference behavior feature parameter vectors.

6. The method for monitoring and investigating interference sources in civil aviation GPS according to claim 5, characterized in that: In S3, the interference behavior evolution trend inversion algorithm is an interference behavior inversion algorithm based on the topological structure of the spatiotemporal interference propagation topology graph. It uses the interference continuous topology evolution sequence formed by the spatiotemporal interference propagation topology graph under continuous time index as the topological structure constraint input, and the interference behavior feature parameter vector extracted based on the interference continuous topology evolution sequence as the calculation input to infer the interference behavior type identification and interference evolution trend vector. During execution, the interference behavior evolution trend inversion algorithm calculates the parameters of the interference behavior feature parameter vector and combines the temporal evolution structure of the node set and edge set in the spatiotemporal interference propagation topology graph to perform topological constraint reasoning on the interference propagation path direction, propagation intensity change trend and propagation shielding influence mechanism of the interference source, so as to predict and output the interference behavior type identifier and interference evolution trend vector of the interference source.

7. The method for monitoring and investigating interference sources in civil aviation GPS according to claim 6, characterized in that: In step S3, the interference behavior type identifier is the behavior type identifier field of the interference source at the current time, which is determined by the interference behavior evolution trend inversion algorithm. It is used to characterize the behavior type state of the current interference source during the process of tracing the interference source. The interference evolution trend vector is calculated based on the interference behavior evolution trend inversion algorithm, according to the interference behavior feature parameter vector and the temporal change trend of the edge weight of special edges in the spatiotemporal interference propagation topology graph. It is used to reflect the change of interference behavior of the interference node under the future time index.

8. A civil aviation GPS interference source monitoring and investigation system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes a computer program to implement the civil aviation GPS interference source monitoring and investigation method as described in any one of claims 1-7.

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