Interference signal identification and response method, device and ground control platform

Abstract

The application provides a method for identifying and coping with interference signals, which is used for a ground control platform and comprises the following steps: a data receiving step of receiving telemetry data; an information screening step of screening receiver positioning information, navigation signal carrier-to-noise ratio and receiver state information in the telemetry data; a grid unit generating step of dividing a three-dimensional monitoring space into a plurality of independent grid units; an anomaly detecting step of determining a suspected interference unit based on a machine learning algorithm; an interference area determining step of determining a statistical space and determining an interference area based on the machine learning algorithm; an interference situation spectrum generating step of generating an interference situation spectrum based on the machine learning algorithm; and an anti-interference strategy generating step of determining a first working mode and a second working mode, wherein the anti-interference capability of the first working mode is stronger than that of the second working mode. The application solves the problems of slow identification and response of a receiver to interference signals in the prior art.

Description

Technical Field

[0001] This application belongs to the technical field of interference signal identification, specifically relating to a method, device, and ground control platform for identifying and responding to interference signals. Background Technology

[0002] GNSS (Global Navigation Satellite System) is a space-based radio navigation and positioning system that provides users with all-weather, three-dimensional coordinates, velocity, and time information from any location on the Earth's surface or in near-Earth space. GNSS mainly consists of navigation satellites, a ground control platform, and user terminals. For user terminals, after receiving navigation signals from navigation satellites, the receiver performs real-time PVT (Position, Velocity, Time) positioning calculations based on the signals to determine the receiver's own coordinates and time in space.

[0003] Distortion of navigation signals can be caused by changes in the ground environment, the presence of multipath effects, and unreasonable antenna configurations. In particular, when the user terminal is in an environment with L-band suppression or deceptive interference, the receiver may be unable to continue tracking the navigation signal and thus be unable to continue positioning.

[0004] For GNSS, identifying interference signals is a crucial step in achieving continuous receiver positioning. Traditional interference signal identification methods determine the impact of interference signals on the receiver based on factors such as the decrease in the receiver's carrier-to-noise ratio. However, when encountering strong interference signals, there may be situations where the receiver can no longer locate the target when the carrier-to-noise ratio decreases. Therefore, traditional methods cannot provide early warning of interference signals.

[0005] Therefore, a new method for identifying interference signals is urgently needed to overcome the shortcomings of the existing technology. Summary of the Invention

[0006] The main purpose of this application is to provide a method, device, and ground control platform for identifying and responding to interference signals, in order to solve the problems of slow identification and response to interference signals and untimely response of receivers in the prior art.

[0007] To address the aforementioned technical problems, in a first aspect, this application proposes a method for identifying and responding to interference signals for a ground control platform, comprising: a data receiving step, receiving telemetry data pre-stored by a satellite-borne computer from a navigation satellite, wherein the telemetry data is transmitted from a user terminal receiver to the satellite-borne computer; an information filtering step, filtering receiver positioning information, navigation signal carrier-to-noise ratio, and receiver status information from the telemetry data; a grid cell generation step, acquiring preset three-dimensional information of the area to be monitored, establishing a three-dimensional monitoring space based on the preset three-dimensional information, and dividing the three-dimensional monitoring space into grids to generate several independent grid cells, wherein the preset three-dimensional information includes longitude information, latitude information, and altitude information; and an anomaly detection step, based on a machine learning algorithm, traversing any grid cell and acquiring the number of historical anomalies in the grid cell within a unit time period, wherein if the number of historical anomalies in the unit time period is greater than a first preset... A threshold is set to identify corresponding grid cells as suspected interference cells. Historical abnormal events are determined based on receiver positioning information, and / or navigation signal carrier-to-noise ratio, and / or receiver status information. In the interference area determination step, a statistical space covering several grid cells is determined in the three-dimensional monitoring space. Based on a machine learning algorithm, any statistical space is traversed. If the proportion of suspected interference cells in the statistical space is greater than a second preset threshold, the corresponding statistical space is determined as an interference area. In the interference situation map generation step, an interference situation map covering all interference areas is generated based on a machine learning algorithm. The interference situation map is a dynamic geographic information dataset including the confidence level and interference intensity of all interference areas. In the anti-interference strategy generation step, the receiver's mode within the interference area is determined as the first working mode, and the receiver's mode within the three-dimensional monitoring space outside the interference area is determined as the second working mode. The first working mode has stronger anti-interference capability than the second working mode.

[0008] Furthermore, the receiver positioning information includes longitude, latitude, and altitude information; the receiver status information includes the number of acquired signals, the positioning validity indicator, and the loop loss count; historical abnormal events include: the difference between the receiver positioning information and the preset three-dimensional information is greater than the target threshold, and / or the number of acquired signals is less than 4, and / or the navigation signal carrier-to-noise ratio is less than 40dB, and / or the positioning validity indicator is "0", and / or the loop loss count is greater than 3.

[0009] Furthermore, machine learning algorithms include moving average algorithm, local outlier factor algorithm, and ST-DBSCAN algorithm.

[0010] Furthermore, the first operating mode includes: the receiver is configured to use a first loop bandwidth, a first integration time, and a first threshold value; the second operating mode includes: the receiver is configured to use a second loop bandwidth, a second integration time, and a second threshold value, wherein the first loop bandwidth is greater than the second loop bandwidth, the first integration time is greater than the second integration time, and the first threshold value is less than the second threshold value.

[0011] Secondly, this application provides a device for identifying and responding to interference signals for a ground control platform, characterized by comprising: a data receiving module configured to receive telemetry data pre-stored by a satellite-borne computer of a navigation satellite, wherein the telemetry data is transmitted from a user terminal receiver to the satellite-borne computer of the navigation satellite; an information filtering module configured to filter receiver positioning information, navigation signal carrier-to-noise ratio, and receiver status information from the telemetry data; a grid cell generation module configured to acquire preset three-dimensional information of the area to be monitored, establish a three-dimensional monitoring space based on the preset three-dimensional information, and divide the three-dimensional monitoring space into grids to generate several mutually independent grid cells, wherein the preset three-dimensional information includes longitude information, latitude information, and altitude information; and an anomaly detection module configured to, based on a machine learning algorithm, traverse any grid cell and acquire the number of historical anomalies in the grid cell within a unit time period, wherein if the number of historical anomalies in the unit time period is greater than a first preset value, the anomaly detection module will detect anomalies. The system includes a threshold module to identify corresponding grid cells as suspected interference cells, and historical abnormal events are determined based on receiver positioning information, and / or navigation signal carrier-to-noise ratio, and / or receiver status information. An interference area determination module is configured to determine a statistical space encompassing several grid cells within the 3D monitoring space. Based on a machine learning algorithm, it traverses any statistical space; if the proportion of suspected interference cells in the statistical space is greater than a second preset threshold, the corresponding statistical space is determined as an interference area. An interference situation map generation module is configured to generate an interference situation map covering all interference areas based on a machine learning algorithm. The interference situation map is a dynamic geographic information dataset including the confidence level and interference intensity of all interference areas. An anti-interference strategy generation module is configured to determine the receiver's mode within the interference area as a first working mode and the receiver's mode within the 3D monitoring space outside the interference area as a second working mode, wherein the first working mode has stronger anti-interference capabilities than the second working mode.

[0012] Thirdly, this application provides a ground control platform, including a processor and a memory for storing processor-executable instructions, wherein the processor executes the instructions to implement the steps of the method described in the first aspect.

[0013] Fourthly, this application provides a method for identifying and responding to interference signals, including: an information filtering step, in which a ground control platform receives telemetry data pre-stored by the onboard computer of a navigation satellite, and the ground control platform filters out receiver positioning information, navigation signal carrier-to-noise ratio, and receiver status information from the telemetry data, wherein the telemetry data is transmitted from the receiver of a user terminal to the onboard computer of the navigation satellite; a grid cell generation step, in which the ground control platform acquires preset three-dimensional information of the area to be monitored, establishes a three-dimensional monitoring space based on the preset three-dimensional information, and divides the three-dimensional monitoring space into grids to generate several mutually independent grid cells, wherein the preset three-dimensional information includes longitude information, latitude information, and altitude information; an anomaly detection step, in which the ground control platform, based on a machine learning algorithm, traverses any grid cell and obtains the number of historical anomalies in the grid cell within a unit time period, and if the number of historical anomalies in the unit time period is greater than a first preset threshold, the ground control platform determines the corresponding grid cell as a suspected interference cell, wherein the historical anomalies are determined based on receiver positioning information, and / or navigation signal carrier-to-noise ratio, and / or receiver status information; and an interference area determination step, in which the ground control platform determines a statistical space in the three-dimensional monitoring space, and statistically analyzes the data. The statistical space is a three-dimensional space encompassing several grid cells. The ground control platform, based on a machine learning algorithm, traverses any statistical space. If the proportion of suspected interference cells in the statistical space is greater than a second preset threshold, the corresponding statistical space is identified as an interference area. In the interference situation map determination step, the ground control platform, based on a machine learning algorithm, generates an interference situation map covering all interference areas and transmits the interference situation map to the receiver at the user terminal via the navigation satellite's onboard computer. The interference situation map includes the confidence level and interference intensity of the interference areas. In the critical distance calculation step, the receiver generates a dynamic trajectory curve within a preset time period. The receiver traverses the dynamic trajectory curve and, based on the interference areas in the interference situation map, calculates the critical distance between the dynamic trajectory curve and the center point of any interference area. In the adaptive anti-interference step, if the critical distance is less than or equal to a third preset threshold, the receiver is located in the interference area and adopts a first working mode; if the critical distance is greater than the third preset threshold, the receiver is located in a three-dimensional monitoring space outside the interference area and adopts a second working mode. The first and second working modes are generated by the ground control platform based on the interference situation map, and the first working mode has stronger anti-interference capabilities than the second working mode.

[0014] Furthermore, the receiver positioning information includes longitude, latitude, and altitude information; the receiver status information includes the number of acquired signals, the positioning validity indicator, and the loop loss count; historical abnormal events include: the difference between the receiver positioning information and the preset three-dimensional information is greater than the target threshold, and / or the number of acquired signals is less than 4, and / or the navigation signal carrier-to-noise ratio is less than 40dB, and / or the positioning validity indicator is "0", and / or the loop loss count is greater than 3.

[0015] Furthermore, machine learning algorithms include moving average algorithm, local outlier factor algorithm, and ST-DBSCAN algorithm.

[0016] Furthermore, the first operating mode includes: the receiver is configured to use a first loop bandwidth, a first integration time, and a first threshold value; the second operating mode includes: the receiver is configured to use a second loop bandwidth, a second integration time, and a second threshold value, wherein the first loop bandwidth is greater than the second loop bandwidth, the first integration time is greater than the second integration time, and the first threshold value is less than the second threshold value.

[0017] Compared with the prior art, this application has the following advantages:

[0018] By employing a "space-ground joint" interference identification method, the ground control platform uses telemetry data such as the number of navigation satellites, positioning effectiveness, signal tracking continuity, and receiver on-board noise ratio to simultaneously determine the three-dimensional monitoring space. Machine learning algorithms are then used to determine the regional distribution patterns of interference signals within the three-dimensional monitoring space. Based on the regional characteristics of the interference signals, the operating mode of the receiver in different areas is determined, ensuring stable tracking loops without loss of lock, thus improving the efficiency of interference signal identification and receiver operation. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the GNSS structure of this application;

[0020] Figure 2 This is a flowchart illustrating the method for identifying and responding to interference signals according to the first aspect of an embodiment of this application;

[0021] Figure 3 This is a schematic diagram illustrating the critical distance between the dynamic trajectory curve and the interference region in an embodiment of this application.

[0022] Figure 4 This is a flowchart illustrating a method for identifying and responding to interference signals for a ground control platform, representing a second aspect of an embodiment of this application.

[0023] Figure 5 This is a schematic diagram of the composition of a device for identifying and responding to interference signals for a ground control platform, which is a third aspect of the embodiments of 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. However, the embodiments described below are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the protection scope of this application. Unless obvious from the context or otherwise, the same reference numerals in the figures represent the same structures or operations.

[0025] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0026] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more steps may be removed from these processes.

[0027] Please see Figure 1 GNSS mainly consists of three components: a ground control platform 100, a navigation satellite 200, and a receiver 300 for user terminals. After generating navigation signals, the navigation satellite 200 broadcasts these signals to the receiver 300. Upon receiving the navigation signals, the receiver 300 performs real-time PVT calculations. Simultaneously, the navigation satellite 200 acquires telemetry data from the receiver 300 in real time and packages this data before sending it to the ground control platform 100. The ground control platform 100 communicates with the onboard computer of the navigation satellite 200 via a space-to-ground link (including uplink and downlink), and the onboard computer of the navigation satellite 200 also communicates with the receiver 300 via a space-to-ground link (including uplink and downlink). The navigation signals transmitted by the navigation satellite 200 mainly consist of three parts: a carrier wave, a ranging code, and navigation messages. The receiver 300 needs to lock onto the carrier wave before it can perform positioning calculations.

[0028] Please see Figure 2 The first aspect of this application proposes a method for identifying and responding to interference signals, comprising:

[0029] A1. Information filtering step: The ground control platform receives telemetry data pre-stored by the onboard computer of the navigation satellite. The ground control platform filters out receiver positioning information, navigation signal carrier-to-noise ratio and receiver status information from the telemetry data. The telemetry data is transmitted from the receiver of the user terminal to the onboard computer of the navigation satellite.

[0030] A2. Grid unit generation steps: The ground control platform acquires the preset three-dimensional information of the area to be monitored, establishes a three-dimensional monitoring space based on the preset three-dimensional information, and divides the three-dimensional monitoring space into grids to generate several independent grid units. The preset three-dimensional information includes longitude information, latitude information, and altitude information.

[0031] A3. Anomaly detection step: The ground control platform, based on a machine learning algorithm, traverses any grid cell and obtains the number of historical anomalies in the grid cell within a unit time period. If the number of historical anomalies in the unit time period is greater than the first preset threshold, the ground control platform determines the corresponding grid cell as a suspected interference cell. The historical anomalies are determined based on receiver positioning information, and / or navigation signal carrier-to-noise ratio, and / or receiver status information.

[0032] A4. Interference Area Determination Steps: The ground control platform determines the statistical space in the three-dimensional monitoring space. The statistical space is a three-dimensional space covering several grid units. Based on the machine learning algorithm, the ground control platform traverses any statistical space. If the proportion of suspected interference units in the statistical space is greater than the second preset threshold, the corresponding statistical space is determined as the interference area.

[0033] A5. The steps for determining the interference situation map are as follows: The ground control platform generates an interference situation map covering all interference areas based on machine learning algorithms, and transmits the interference situation map to the receiver of the user terminal via the onboard computer of the navigation satellite. The interference situation map includes the confidence level and interference intensity of the interference areas.

[0034] A6. Critical distance calculation steps: The receiver generates a dynamic trajectory curve within a preset time period. The receiver traverses the dynamic trajectory curve and calculates the critical distance between the dynamic trajectory curve and the center point of any interference area based on the interference situation map.

[0035] A7. Adaptive anti-interference steps: If the critical distance is less than or equal to the third preset threshold, the receiver is located in the interference area and adopts the first working mode; if the critical distance is greater than the third preset threshold, the receiver is located in the three-dimensional monitoring space outside the interference area and adopts the second working mode. The anti-interference capability of the first working mode is stronger than that of the second working mode.

[0036] This application employs a "space-ground joint" interference identification method. The ground control platform filters receiver positioning information, navigation signal carrier-to-noise ratio, and receiver status information from onboard telemetry data. Based on preset three-dimensional information, it determines a three-dimensional monitoring space. Using machine learning algorithms, it statistically analyzes historical anomalies in each independent grid cell within the three-dimensional monitoring space, generating an interference situation map covering all interference areas. This clarifies the regional distribution characteristics of interference signals within the three-dimensional monitoring space. Furthermore, based on the regional characteristics of the interference signals, different operating modes for the receiver are determined in different areas. A high-precision mode is used in non-interference areas, while a more robust anti-interference mode is used in interference areas. This ensures stable tracking loop without loss of lock, improving the efficiency of interference signal identification and receiver operation.

[0037] For the ground control platform, it receives telemetry data from multiple navigation satellites in real time via downlink. This telemetry data is then packaged and uploaded by the receiver to the onboard computer of the navigation satellite via uplink. The ground control platform filters out the receiver positioning information (LOCATION), navigation signal carrier-to-noise ratio (C / N0), and receiver status information (STATUS) from the telemetry data, and uses this information as an important benchmark for identifying interference signals.

[0038] The ground control platform also acquires preset three-dimensional information of the area to be monitored and establishes a three-dimensional monitoring space S based on this information. This preset three-dimensional information includes longitude L, latitude B, and altitude H. For example, acquiring longitude L = 73°E~135°E, latitude B = 4°N~53°N, and altitude H = 0~8800m constructs a three-dimensional monitoring space S spanning these three dimensions. Further, the ground control platform divides the three-dimensional monitoring space S into a grid, generating several independent grid units. Each grid unit is the smallest cubic unit with a unit length of 1m within the three-dimensional monitoring space S, and each grid unit can contain multiple coordinate points composed of the preset three-dimensional information.

[0039] In the embodiments of this application, the preset three-dimensional information of the area to be monitored can be obtained through orbit determination information in telemetry data. In addition to a set of real-time parameters reflecting the receiver's own operating status and the signal quality of the received navigation satellites, the telemetry data also includes orbit determination information. This orbit determination information is objective geographic coordinate information obtained by the receiver through a filtering algorithm and is not affected by the signal quality of the navigation satellites. The specific methods for obtaining the orbit determination information are common knowledge in the art and will not be elaborated upon here.

[0040] After generating the grid cells, the ground control platform will further use machine learning algorithms to traverse all grid cells and obtain the number of historical abnormal events E within a unit time period. Grid cells in which historical abnormal events E occur at a high frequency are identified as suspected interference cells (e.g., more than 2 historical abnormal events E occur within 1 second).

[0041] In some specific embodiments, historical anomaly events E are determined based on receiver positioning information LOCATION, and / or navigation signal carrier-to-noise ratio C / N0, and / or receiver status information STATUS in telemetry data.

[0042] The receiver positioning information (LOCATION) includes longitude (L), latitude (B), and altitude (H). This information is obtained from the receiver's PVT calculation results and is further converted using formulas.

[0043] In some specific embodiments, if the difference between the receiver positioning information LOCATION and the preset three-dimensional information is greater than the target threshold, including the difference in longitude information L being greater than 1°, and / or the difference in latitude information B being greater than 1°, and / or the difference in altitude information H being greater than 50m, for example, the actual coordinate point of the three-dimensional monitoring space entered by the receiver is 121°E / 31°N / 4000m, but the receiver positioning information LOCATION read by the ground control platform is 120°E / 31°N / 3800m, reflecting that the receiver positioning information LOCATION has changed at this time, it will be marked as a historical abnormal event E by the ground control platform.

[0044] The carrier-to-noise ratio (C / N0) of a navigation signal is the ratio of signal power to noise power, reflecting the receiver's ability to continuously track the navigation signal. During PVT calculation, the receiver first replicates the intermediate frequency carrier of the navigation signal through a phase-locked loop (PLL) or frequency-locked loop (FLL), performs signal processing such as filtering and integration, and down-converts it to a baseband signal to achieve continuous tracking. In addition, the receiver calculates the C / N0 in real time. If the C / N0 is greater than or equal to 40 dB, the loop can track the signal. If the C / N0 falls below 40 dB, it indicates potential interference in the receiver's environment, and the loop may lose tracking of the navigation signal. This is then marked as a historical anomaly event (E) by the ground control platform.

[0045] The receiver status information STATUS includes the number of signals acquired by the receiver, the positioning validity indicator, and the loop loss count.

[0046] For GNSS systems, a typical setup includes 12 navigation satellites. The user terminal receiver needs to acquire navigation signals from at least four of these satellites to perform PVT (Progressive Virtual Transmission) calculations to obtain the receiver's three-dimensional coordinates, velocity, and time information. Therefore, if the number of acquired signals is less than four, the receiver cannot perform accurate PVT calculations, indicating the presence of interference signals in the receiver's environment. This will be marked as a historical anomaly event (E) by the ground control platform.

[0047] Generally, the positioning validity identifier is a binary identifier, including four states: 0 / 1 / 2 / 3. "0" represents the receiver not positioning, "1" represents single-point positioning, "2" represents RTK (Real-time Kinematic) positioning, and "3" represents differential positioning. When the positioning validity identifier is "0", it indicates that there may be interference signals in the receiver's environment, and the ground control platform will mark it as a historical anomaly event E.

[0048] The loop lockout count reflects the number of times the receiver experiences a loop lockout per unit time. Loop lockouts can be caused by various factors, with typical causes including: malfunctions in internal receiver components (such as the RF front-end, baseband, or clock); improper or damaged external antenna installation; numerous metal obstructions in the receiver's environment; a navigation signal carrier-to-noise ratio (C / N0) greater than 40 dB; repeated fluctuations in the navigation signal C / N0; and the receiver being in a high-speed moving state, resulting in failure to compensate for Doppler shift in a timely manner. Generally, if the loop lockout is caused by a hardware failure in the receiver, the loop lockout count is "1," meaning that loop lockouts will not occur multiple times in a short period. If the loop lockout is caused by interference from the external environment, multiple loop lockouts may occur in a short period. When the loop lockout count exceeds 3, the ground control platform will mark it as a historical anomaly event E.

[0049] In summary, historical anomaly events E include: the difference between receiver positioning information and preset 3D information is greater than the target threshold, the number of acquired signals is less than 4, the carrier-to-noise ratio of navigation signals is less than 40 dB, the positioning validity indicator is "0", and the loop loss count is greater than 3. After identifying which grid cells have experienced more than two historical anomaly events E within a unit time period (e.g., 1 second), the ground control platform will designate these grid cells as suspected interference cells.

[0050] It is understandable that, since the three-dimensional monitoring space S contains countless grid cells, each grid cell is equivalent to a point in the entire three-dimensional monitoring space S. For suspected interference cells, their distribution in the three-dimensional monitoring space may be discrete or continuous. It is necessary to merge and statistically analyze these cells to reflect the distribution of interference areas within the macroscopic three-dimensional monitoring space S. Therefore, the embodiments of this application divide the three-dimensional monitoring space into several independent statistical spaces, each with a size of 100m × 100m × 100m, that is, each statistical space includes one million independent grid cells. Based on machine learning algorithms, traversing any statistical space, if the proportion of suspected interference cells in it is greater than a second preset threshold (e.g., 60%), then the corresponding statistical space can be determined as an interference area.

[0051] In some specific embodiments, the machine learning algorithm of this application may include a moving average algorithm, a local outlier algorithm, and an ST-DBSCAN algorithm. Specifically: First, the ground control platform standardizes and constructs features for the input telemetry data. Then, the local outlier algorithm is used to score each data point, identifying observations that significantly deviate from the normal pattern. Next, the ST-DBSCAN algorithm is used to aggregate these discrete anomalous data points (i.e., historical anomalous events) based on their geographical location and timestamp proximity, forming preliminary suspected interference unit clusters (i.e., interference areas). Each cluster has preliminary geographical boundaries and duration. To further improve accuracy, a moving average algorithm is used to process long-term interference areas, thereby calculating the confidence level and interference intensity of interference events in each interference area.

[0052] Furthermore, the ground control platform generates a Map of Interference Situations (MAP) covering all interference areas. This MAP is essentially a dynamic geographic information dataset including the confidence level and interference intensity of all interference areas. The ground control platform transmits the MAP to the onboard computer of the navigation satellite via uplink, and the onboard computer then transmits the MAP to the receiver via downlink.

[0053] For the interference area, the ground control platform generates a corresponding anti-interference strategy in the first operating mode (Mode 1). For the three-dimensional monitoring space S outside the interference area, the ground control platform generates a corresponding anti-interference strategy in the second operating mode (Mode 2). The ground control platform sends the above anti-interference strategies to the onboard computer of the navigation satellite via the uplink, and the onboard computer then transmits the anti-interference strategies to the receiver via the downlink.

[0054] In some specific embodiments, the first operating mode Mode1 includes: the receiver is configured to perform loop integration using a first loop bandwidth (e.g., 25 Hz), a first integration time (e.g., 4 ms), and a first threshold value (e.g., 38 dB), and the second operating mode Mode2 includes: the receiver is configured to perform loop integration using a narrower second loop bandwidth (e.g., 12 Hz), a shorter second integration time (e.g., 1 ms), and a larger second threshold value (e.g., 40 dB).

[0055] Understandably, the first working mode, Mode 1, corresponds to the high robustness mode, which uses a wider loop bandwidth, a longer integration time, and a lower threshold value, making it more tolerant of signal attenuation in the external environment and specifically designed to combat interference signals in the environment. The second working mode, Mode 2, corresponds to the high precision mode, which uses standard loop bandwidth, integration time, and threshold value to ensure optimal positioning accuracy in a normal environment without interference signals.

[0056] For the receiver of the user terminal, ensuring continuous signal tracking and positioning is crucial. Therefore, based on the orbit determination information obtained by the receiver through filtering algorithms, a dynamic trajectory curve is extracted. The dynamic trajectory curve reflects the trajectory trend of the receiver within a preset time period. At the same time, the receiver also obtains the interference situation map (MAP) stored in the onboard computer through the downlink, determines the interference area, matches the dynamic trajectory curve with the interference area, calculates the critical distance between the coordinate point on the dynamic trajectory curve and the center point of any interference area, and then determines the receiver's operating mode based on the critical distance.

[0057] Please see Figure 3 For three consecutive 100m×100m×100m interference areas, with corresponding center points C1, C2, and C3, and a third preset threshold of, for example, 87m, the receiver's dynamic trajectory curve partially overlaps with the aforementioned interference areas. Taking any three points P1, P2, and P3 on the curve as an example: the critical distance D1 between point P1 and C1 is 100m > 87m, indicating that point P1 does not fall within the interference area, and the receiver's operating mode at this time is determined to be the second operating mode, Mode 2; the critical distance D2 between point P2 and C2 is 60m < 87m, indicating that point P2 falls within the interference area, and the receiver's operating mode at this time is determined to be the first operating mode, Mode 1; the critical distance D3 between point P3 and C3 is 120m > 87m, indicating that point P3 does not fall within the interference area, and the receiver's operating mode at this time is determined to be the second operating mode, Mode 2.

[0058] In this application, based on the relationship between the critical distance and the third preset threshold, before it is determined that the receiver's trajectory is about to enter a known interference area, its signal tracking loop is automatically switched from the default second working mode Mode2 to the first working mode Mode2. Alternatively, before it is determined that the receiver is about to leave the current interference area and enter the normal operating environment, its signal tracking loop is automatically switched from the first working mode Mode1 to the second working mode Mode2.

[0059] The method in this application uses a satellite-ground cooperative architecture to match the receiver's dynamic trajectory curve with the interference situation map. This enables closed-loop fine-tuning of the high-robust tracking mode while in the interference zone, based on real-time monitoring of the carrier-to-noise ratio. This optimizes tracking performance while maintaining signal lock, greatly improving the receiver's survivability and service continuity in complex electromagnetic environments.

[0060] Please see Figure 4 The second aspect of this application proposes a method for identifying and responding to interference signals for a ground control platform, comprising:

[0061] B1. Data receiving step: receiving telemetry data pre-stored by the onboard computer of the navigation satellite, wherein the telemetry data is transmitted from the receiver of the user terminal to the onboard computer of the navigation satellite;

[0062] B2. Information filtering step: Filter the receiver positioning information, navigation signal carrier-to-noise ratio and receiver status information in the telemetry data;

[0063] B3. Grid unit generation step: Obtain the preset three-dimensional information of the area to be monitored, establish a three-dimensional monitoring space based on the preset three-dimensional information, and divide the three-dimensional monitoring space into grids to generate several independent grid units. The preset three-dimensional information includes longitude information, latitude information, and altitude information.

[0064] B4. Anomaly detection step: Based on a machine learning algorithm, traverse any of the grid cells to obtain the number of historical anomalies in the grid cell within a unit time period. If the number of historical anomalies in the unit time period is greater than a first preset threshold, determine the corresponding grid cell as a suspected interference cell. The historical anomalies are determined based on the receiver positioning information, and / or the navigation signal carrier-to-noise ratio, and / or the receiver status information.

[0065] B5. Interference area determination step: Determine a statistical space in the three-dimensional monitoring space that covers a number of grid units. Based on the machine learning algorithm, traverse any statistical space. If the proportion of the suspected interference units in the statistical space is greater than a second preset threshold, determine the corresponding statistical space as an interference area.

[0066] B6. Interference situation map generation step: Based on the machine learning algorithm, generate an interference situation map covering all the interference areas. The interference situation map is a dynamic geographic information dataset that includes the confidence level and interference intensity of all the interference areas.

[0067] B7. Anti-interference strategy generation step: determine the receiver's mode within the interference area as a first working mode, and determine the receiver's mode within the three-dimensional monitoring space outside the interference area as a second working mode, wherein the anti-interference capability of the first working mode is stronger than that of the second working mode.

[0068] For details of other operations performed in each step of this embodiment, please refer to the foregoing embodiments, which will not be repeated here.

[0069] Please see Figure 5 The third aspect of this application proposes an interference signal identification and response device for a ground control platform, comprising: a data receiving module configured to receive telemetry data pre-stored by a satellite-borne computer of a navigation satellite, wherein the telemetry data is transmitted from a user terminal receiver to the satellite-borne computer of the navigation satellite; an information filtering module configured to filter receiver positioning information, navigation signal carrier-to-noise ratio, and receiver status information from the telemetry data; a grid cell generation module configured to acquire preset three-dimensional information of the area to be monitored, establish a three-dimensional monitoring space based on the preset three-dimensional information, and divide the three-dimensional monitoring space into grids to generate several independent grid cells, wherein the preset three-dimensional information includes longitude information, latitude information, and altitude information; and an anomaly detection module configured to, based on a machine learning algorithm, traverse any grid cell and acquire the number of historical anomalies in the grid cell within a unit time period, wherein if the number of historical anomalies in the unit time period is greater than a first preset threshold, anomaly detection is performed. The system is configured to: identify corresponding grid cells as suspected interference cells, and determine historical abnormal events based on receiver positioning information, and / or navigation signal carrier-to-noise ratio, and / or receiver status information; determine interference area identification module, configured to identify a statistical space covering several grid cells in the three-dimensional monitoring space, and based on a machine learning algorithm, traverse any statistical space, and if the proportion of suspected interference cells in the statistical space is greater than a second preset threshold, identify the corresponding statistical space as an interference area; generate interference situation map generation module, configured to generate an interference situation map covering all interference areas based on a machine learning algorithm, the interference situation map being a dynamic geographic information dataset including the confidence and interference intensity of all interference areas; and generate anti-interference strategy module, configured to determine the receiver's mode within the interference area as a first working mode, and the receiver's mode within the three-dimensional monitoring space outside the interference area as a second working mode, wherein the first working mode has stronger anti-interference capability than the second working mode.

[0070] For details of other operations performed by each module in this embodiment, please refer to the foregoing embodiments, which will not be elaborated here.

[0071] The interference signal identification and response device in this application embodiment can be a device, or it can be a component, integrated circuit, or chip in a terminal. The interference signal identification and response device in this application embodiment can be a chip, including FPGA (Field Programmable Gate Array), MCU (Microcontroller Unit), etc., and is not specifically limited thereto.

[0072] The fourth aspect of this application proposes a ground control platform, including a processor and a memory for storing processor-executable instructions. When the processor executes the instructions, it implements the various processes of the embodiment of the interference signal identification and response method of the second aspect described above, and can achieve the same technical effect. To avoid repetition, it will not be described in detail here.

[0073] The processor can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0074] For those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0075] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of scope in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0076] Although this application has been described with reference to specific embodiments, those skilled in the art should recognize that the above embodiments are only used to illustrate this application, and various equivalent changes or substitutions can be made without departing from the spirit of this application. Therefore, any changes or modifications to the above embodiments within the scope of the essential spirit of this application will fall within the scope of this application.

Claims

1. A method for identifying and responding to interference signals, used in a ground control platform, characterized in that, include: The data receiving step involves receiving telemetry data pre-stored by the onboard computer of the navigation satellite, wherein the telemetry data is transmitted from the receiver of the user terminal to the onboard computer of the navigation satellite. The information filtering step filters the receiver positioning information, navigation signal carrier-to-noise ratio, and receiver status information from the telemetry data. The grid cell generation step involves obtaining preset three-dimensional information of the area to be monitored, establishing a three-dimensional monitoring space based on the preset three-dimensional information, and dividing the three-dimensional monitoring space into grids to generate several independent grid cells. The preset three-dimensional information includes longitude information, latitude information, and altitude information. The anomaly detection step, based on a machine learning algorithm, traverses any of the grid cells to obtain the number of historical anomalies in the grid cell within a unit time period. If the number of historical anomalies in the unit time period is greater than a first preset threshold, the corresponding grid cell is determined to be a suspected interference cell. The historical anomalies are determined based on the receiver positioning information, and / or the navigation signal carrier-to-noise ratio, and / or the receiver status information. The interference area determination step involves determining a statistical space encompassing several grid units within the three-dimensional monitoring space. Based on the machine learning algorithm, any statistical space is traversed. If the proportion of suspected interference units in the statistical space is greater than a second preset threshold, the corresponding statistical space is determined to be an interference area. The interference situation map generation step involves generating an interference situation map covering all the interference areas based on the machine learning algorithm. The interference situation map is a dynamic geographic information dataset that includes the confidence level and interference intensity of all the interference areas. The anti-interference strategy generation step involves determining the receiver's mode within the interference area as a first operating mode and determining the receiver's mode within the three-dimensional monitoring space outside the interference area as a second operating mode, wherein the anti-interference capability of the first operating mode is stronger than that of the second operating mode.

2. The method for identifying and responding to interference signals according to claim 1, characterized in that, The receiver positioning information includes longitude information, latitude information and altitude information, and the receiver status information includes the number of signals acquired by the receiver, the positioning validity indicator and the loop loss count value; The historical abnormal events include: the difference between the receiver positioning information and the preset three-dimensional information is greater than the target threshold, and / or the number of captured signals is less than 4, and / or the carrier-to-noise ratio of the navigation signal is less than 40dB, and / or the positioning validity identifier is "0", and / or the loop loss count value is greater than 3.

3. The method for identifying and responding to interference signals according to claim 1 or 2, characterized in that, The machine learning algorithms include the moving average algorithm, the local outlier factor algorithm, and the ST-DBSCAN algorithm.

4. The method for identifying and responding to interference signals according to claim 1, characterized in that, The first operating mode includes: the receiver is configured to use a first loop bandwidth, a first integration time, and a first threshold value; The second operating mode includes: the receiver is configured to use a second loop bandwidth, a second integration time, and a second threshold value, wherein, The first loop bandwidth is greater than the second loop bandwidth, the first integration time is greater than the second integration time, and the first threshold value is less than the second threshold value.

5. A device for identifying and responding to interference signals, used on a ground control platform, characterized in that, include: The data receiving module is configured to receive telemetry data pre-stored by the onboard computer of the navigation satellite, wherein the telemetry data is transmitted from the receiver of the user terminal to the onboard computer of the navigation satellite; The information filtering module is configured to filter receiver positioning information, navigation signal carrier-to-noise ratio, and receiver status information from the telemetry data; The grid cell generation module is configured to acquire preset three-dimensional information of the area to be monitored, establish a three-dimensional monitoring space based on the preset three-dimensional information, and divide the three-dimensional monitoring space into grids to generate several independent grid cells. The preset three-dimensional information includes longitude information, latitude information, and altitude information. An anomaly detection module is configured to traverse any of the grid cells based on a machine learning algorithm, obtain the number of historical anomalies in the grid cell within a unit time period, and if the number of historical anomalies in the unit time period is greater than a first preset threshold, determine the corresponding grid cell as a suspected interference cell. The historical anomalies are determined based on the receiver positioning information, and / or the navigation signal carrier-to-noise ratio, and / or the receiver status information. The interference area determination module is configured to determine a statistical space in the three-dimensional monitoring space that covers a number of grid units, and based on the machine learning algorithm, traverse any statistical space. If the proportion of the suspected interference units in the statistical space is greater than a second preset threshold, the corresponding statistical space is determined to be an interference area. The interference situation map generation module is configured to generate an interference situation map covering all the interference areas based on the machine learning algorithm. The interference situation map is a dynamic geographic information dataset that includes the confidence level and interference intensity of all the interference areas. An anti-interference strategy generation module is configured to determine the receiver's mode within the interference area as a first operating mode, and to determine the receiver's mode within the three-dimensional monitoring space outside the interference area as a second operating mode, wherein the anti-interference capability of the first operating mode is stronger than that of the second operating mode.

6. A ground control platform, characterized in that, include: A processor and a memory for storing processor-executable instructions, wherein the processor, when executing the instructions, implements the steps of the method according to any one of claims 1-4.

7. A method for identifying and responding to interference signals, characterized in that, include: In the information filtering step, the ground control platform receives telemetry data pre-stored by the onboard computer of the navigation satellite. The ground control platform filters out receiver positioning information, navigation signal carrier-to-noise ratio, and receiver status information from the telemetry data. The telemetry data is transmitted from the receiver of the user terminal to the onboard computer of the navigation satellite. In the grid cell generation step, the ground control platform acquires preset three-dimensional information of the area to be monitored, establishes a three-dimensional monitoring space based on the preset three-dimensional information, and divides the three-dimensional monitoring space into grids to generate several independent grid cells. The preset three-dimensional information includes longitude information, latitude information, and altitude information. In the anomaly detection step, the ground control platform, based on a machine learning algorithm, traverses any of the grid cells and obtains the number of historical anomalies in the grid cell within a unit time period. If the number of historical anomalies in the unit time period is greater than a first preset threshold, the ground control platform determines the corresponding grid cell as a suspected interference cell. The historical anomalies are determined based on the receiver positioning information, and / or the navigation signal carrier-to-noise ratio, and / or the receiver status information. In the interference area determination step, the ground control platform determines a statistical space in the three-dimensional monitoring space. The statistical space is a three-dimensional space covering a number of grid units. Based on the machine learning algorithm, the ground control platform traverses any statistical space. If the proportion of the suspected interference units in the statistical space is greater than a second preset threshold, the corresponding statistical space is determined to be an interference area. In the interference situation map determination step, the ground control platform generates an interference situation map covering all the interference areas based on the machine learning algorithm, and transmits the interference situation map to the receiver of the user terminal via the onboard computer of the navigation satellite. The interference situation map includes the confidence level and interference intensity of the interference areas. The critical distance calculation step involves the receiver generating a dynamic trajectory curve within a preset time period, the receiver traversing the dynamic trajectory curve, and calculating the critical distance between the dynamic trajectory curve and the center point of any of the interference regions based on the interference situation map. In the adaptive anti-interference step, if the critical distance is less than or equal to a third preset threshold, the receiver is located in the interference area and adopts a first working mode; if the critical distance is greater than the third preset threshold, the receiver is located in the three-dimensional monitoring space outside the interference area and adopts a second working mode. The first working mode and the second working mode are generated by the ground control platform based on the interference situation map, and the anti-interference capability of the first working mode is stronger than that of the second working mode.

8. The method for identifying and responding to interference signals according to claim 7, characterized in that, The receiver positioning information includes longitude information, latitude information and altitude information, and the receiver status information includes the number of signals acquired by the receiver, the positioning validity indicator and the loop loss count value; The historical abnormal events include: the difference between the receiver positioning information and the preset three-dimensional information is greater than the target threshold, and / or the number of captured signals is less than 4, and / or the carrier-to-noise ratio of the navigation signal is less than 40dB, and / or the positioning validity identifier is "0", and / or the loop loss count value is greater than 3.

9. The method for identifying and responding to interference signals according to claim 7 or 8, characterized in that, The machine learning algorithms include the moving average algorithm, the local outlier factor algorithm, and the ST-DBSCAN algorithm.

10. The method for identifying and responding to interference signals according to claim 7, characterized in that, The first operating mode includes: the receiver is configured to use a first loop bandwidth, a first integration time, and a first threshold value; The second operating mode includes: the receiver is configured to use a second loop bandwidth, a second integration time, and a second threshold value, wherein, The first loop bandwidth is greater than the second loop bandwidth, the first integration time is greater than the second integration time, and the first threshold value is less than the second threshold value.

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