GNSS suppression type jamming early warning method based on multi-satellite noise common mode degradation

Abstract

The application discloses a GNSS suppression type jamming early warning method based on multi-satellite carrier-to-noise ratio common mode degradation, relates to the technical field of satellite navigation anomaly monitoring and anti-jamming, and comprises the following steps: acquiring observation data and constructing a carrier-to-noise ratio observation matrix; calculating a single-satellite carrier-to-noise ratio degradation amount based on a sliding background window, and determining an affected satellite set; determining a multi-satellite synchronous degradation condition in combination with the number of affected satellites and the proportion of affected satellites, screening multi-satellite carrier-to-noise ratio common mode degradation candidate epochs; constructing a common mode edge sudden drop sub-amount, an affected satellite proportion sub-amount and a common mode platform drop sub-amount in the candidate epochs; performing robust standardization processing on the three sub-amounts, weighting and fusing, and obtaining a multi-satellite carrier-to-noise ratio common mode degradation risk index; dynamically constructing an early warning judgment threshold based on the risk index history background, extracting an early event in combination with an event persistence constraint, and outputting early warning information. The application can realize early warning before observation interruption or obvious positioning result anomaly, and can reduce the false alarm rate.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation anomaly monitoring and anti-interference technology, and in particular to a GNSS suppression interference early warning method based on multi-satellite carrier-to-noise ratio common-mode degradation. Background Technology

[0002] Global Navigation Satellite Systems (GNSS) are widely used in precise positioning, timing synchronization, intelligent transportation, unmanned systems, aviation and maritime navigation, and safety-critical infrastructure. These applications typically require receivers to continuously output reliable positioning, velocity, and timing results in complex environments. However, GNSS signals reach ground receivers at relatively low power, and their open service signal structure makes them susceptible to external electromagnetic interference, changes in the propagation environment, obstruction, multipath effects, and abnormal receiver tracking status. These factors can lead to degraded observation quality and weaken the reliability and continuity of navigation solutions.

[0003] Among the aforementioned influencing factors, suppressive interference is one of the typical threats affecting the stable operation of GNSS receivers. This type of interference typically raises the receiver front-end noise floor or lowers the correlation peak signal-to-noise ratio (SNR) by transmitting broadband noise, narrowband continuous waves, or pulse signals, causing a simultaneous decrease in the tracking quality of multiple visible satellite channels. Its direct manifestation in the observation domain is often a synchronous degradation of the carrier-to-noise ratio (CNR) of multiple satellites relative to their respective normal background levels. In the early stages of interference, this degradation process may be small in magnitude, not yet significantly affecting positioning accuracy, nor immediately causing carrier loop lock-up or data interruption, but it already reflects that the receiver signal quality is being abnormally affected. If the anomaly can be identified in the early stages of CNR common-mode degradation, it can provide advance warning time for initiating anti-interference measures, data quality marking, and navigation service risk alerts.

[0004] To address the problem of abnormal GNSS signal quality, various monitoring and early warning methods have been developed in existing technologies, which can be broadly categorized into the following three types:

[0005] The first type is the fixed carrier-to-noise ratio (CNR) threshold detection method. This method sets an absolute, fixed CNR threshold (e.g., considering anything below 35 dB-Hz as abnormal). When the CNR of a satellite output by the receiver falls below this threshold, an alarm is triggered. This method is simple to implement and computationally inexpensive, and was common in early receiver designs. However, its inherent drawbacks are also quite obvious: the actual normal level of the CNR is affected by a combination of factors, including the satellite elevation angle, antenna pattern, receiver RF front-end performance, observation station geographical environment, and signal frequency. The normal background can vary by more than 10 dB in different scenarios. Using a fixed threshold makes it difficult to consider the adaptability to different scenarios. In low-noise environments, it may lead to missed detections, while in high-noise or low-elevation environments, it is very easy to cause a large number of false alarms, resulting in poor reliability and universality.

[0006] The second category is posterior detection methods based on positioning residuals, loss-of-lock indicators, or observation interruptions. This method does not directly monitor the carrier-to-noise ratio (CNR), but rather identifies anomalies by monitoring indicators such as the magnitude of the positioning residuals, the number of satellite loss-of-lock events, or the continuity of observation data. When the positioning residuals increase significantly, multiple satellites lose lock simultaneously, or there is a large-scale interruption in observation data, the system is deemed to be interfered with or malfunctioning. The advantage of this type of method is its clear physical meaning, directly related to the final service performance. However, its fatal weakness lies in its lag: an increase in positioning residuals usually means that the position calculation has already shifted, and multiple satellite loss-of-lock and observation interruptions are more indicative of severely deteriorated signal quality. At this point, the navigation service may have already been interrupted or output unreliable results, failing to meet the needs of early warning and proactive defense.

[0007] The third category is the single-satellite historical background comparison method. This method maintains a historical carrier-to-noise ratio (CNR) baseline for each satellite and detects anomalies by comparing the current observation with the historical baseline. Compared to the fixed threshold method, this method can adapt to differences between different satellites and environments to some extent. However, CNR fluctuations of individual satellites are not uncommon: natural changes in satellite elevation angle, local short-term obstruction, temporary obstruction by leaves or buildings, and multipath effects near antennas can all cause a significant decrease in the CNR of individual satellites. If judgment is based solely on changes in individual satellites, these non-interference factors can easily generate a large number of false alarms. In addition, an important characteristic of suppressive interference is its simultaneous impact on multiple satellites, while the single-satellite historical background comparison method completely ignores this crucial cooperative information and lacks the ability to identify common-mode patterns from single-satellite anomalies, resulting in insufficient detection reliability.

[0008] Based on the above, it is evident that existing GNSS signal quality monitoring methods still struggle to simultaneously meet the requirements of environmental adaptation, suppression of sporadic single-satellite fluctuations, identification of multi-satellite common-mode degradation, and early warning. Given the synchronous degradation of the carrier-to-noise ratio across multiple satellites exhibited by suppression interference in the observation domain, it is necessary to propose a GNSS suppression interference early warning method capable of utilizing the historical background of each satellite, adaptively identifying the multi-satellite common-mode degradation process, and outputting early warning information before observation interruptions or significant anomalies in positioning results. Summary of the Invention

[0009] This invention provides a GNSS suppression interference early warning method based on multi-satellite carrier-to-noise ratio common-mode degradation, which can achieve early warning before observation is interrupted or the positioning results are obviously abnormal, effectively distinguishing between single-satellite sporadic fluctuations and multi-satellite common-mode degradation, and reducing false alarm rate and missed alarm rate.

[0010] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0011] A GNSS suppression-based jamming early warning method based on multi-satellite carrier-to-noise ratio common-mode degradation includes the following steps:

[0012] Acquire GNSS observation data, parse the carrier-to-noise ratio (CNR) observation values ​​of each satellite at each epoch from the GNSS observation data, and construct a CNR observation matrix with the observation epoch as the row index and the satellite number as the column index;

[0013] Based on the carrier-to-noise ratio observation matrix, the effective satellite set for the current epoch is determined. The carrier-to-noise ratio degradation of each satellite is calculated using the sliding background window before the current epoch. The set of affected satellites is determined based on the single-satellite degradation threshold.

[0014] Based on the number of affected satellites and the number of effective satellites, determine whether the current epoch meets the multi-satellite synchronous degradation condition, and identify the epoch that meets the multi-satellite synchronous degradation condition as candidate epochs for multi-satellite carrier-to-noise ratio common-mode degradation.

[0015] Within the candidate epochs of the multi-satellite carrier-to-noise ratio common-mode degradation, construct the common-mode edge drop subquant, the affected satellite proportion subquant, and the common-mode platform drop subquant;

[0016] Robust normalization is performed on the common-mode edge drop subquant, the affected satellite proportion subquant, and the common-mode platform drop subquant, respectively. The three subquants are then weighted and fused to obtain a multi-satellite carrier-to-noise ratio common-mode degradation risk index.

[0017] Based on the multi-satellite carrier-to-noise ratio common-mode degradation risk index and preset persistence judgment conditions, early events of suppression interference or signal power degradation are extracted, and early warning information of the early events is output.

[0018] A further improvement to the above technical solution is as follows:

[0019] Preferably, the method for constructing the carrier-to-noise ratio (CNR) observation matrix includes: constructing a CNR observation matrix with the observation epoch as the row index and the satellite number as the column index, wherein each element in the CNR observation matrix uniquely corresponds to a CNR observation value of an epoch and a satellite; and marking missing values ​​in the CNR observation matrix for cases where valid CNR observation values ​​cannot be obtained.

[0020] Preferably, the method for calculating the carrier-to-noise ratio degradation is as follows:

[0021]

[0022] in, Indicates satellite In the calendar The carrier-to-noise ratio degradation Indicates satellite In the sliding background window The median of all carrier-to-noise ratio observations. Indicates the epoch index within the window; This represents a sliding background window used to estimate the normal signal level of a satellite, containing epochs. Several previous historical periods; Indicates satellite Eratosthenes in the background window The observed carrier-to-noise ratio, Indicates satellite In the current epoch The observed carrier-to-noise ratio, This indicates taking the non-negative part.

[0023] Preferably, the method for determining the set of affected satellites is as follows:

[0024]

[0025] in, Represents the epoch The affected satellite set Indicates the satellite number, Represents the epoch The effective set of satellites participating in the carrier-to-noise ratio degradation determination Indicates satellite In the calendar The carrier-to-noise ratio degradation This indicates the threshold for single-star degradation.

[0026] Preferably, the multi-satellite synchronous degradation conditions include: the number of affected satellites is not less than the minimum threshold for the number of affected satellites, and the ratio of the number of affected satellites to the number of effective satellites is not less than the minimum threshold for the affected proportion.

[0027] Preferably, the formula for calculating the common-mode edge drop subquant is:

[0028]

[0029] in, Represents the epoch The common-mode edge sudden drop in quantum quantity, This indicates the epoch index within the window. Indicates the epoch index of the observation. Indicates the length of the short-term statistical window. Indicates the epoch The median of the positive decrease in carrier-to-noise ratio for all affected satellites. Indicates satellite In the calendar The positive decrease in carrier-to-noise ratio, Represents the epoch The affected satellite set;

[0030] The formula for calculating the proportional quantum of the affected satellite is:

[0031]

[0032] in, Represents the epoch The proportion of satellites affected Represents the epoch The number of satellites affected Represents the epoch The number of effective satellites, Indicates the minimum affected percentage. This indicates taking the non-negative part;

[0033] The formula for calculating the common-mode platform descending quantum is:

[0034]

[0035] in, Represents the epoch The common-mode platform reduces the number of sub-units. Indicates the epoch Carrier-to-noise ratio degradation of all affected satellites The median of Represents the epoch The affected satellite set Indicates satellite In the calendar The carrier-to-noise ratio degradation.

[0036] Preferably, the robust normalization process includes: letting ( () represents three sub-quantities, and their background median and robustness scale are calculated respectively:

[0037]

[0038]

[0039] in, Indicates the first Each common-mode degenerate subquantum in the epoch The background median, Indicates the first A common-mode degenerate subquantum in its sliding background window The median of all values ​​in the range. Indicates the first Each common-mode degenerate subquantity is within the epoch of the background window. The value, Indicates the use of estimating the first A sliding background window with normal common-mode degenerate subquantities. This indicates the epoch index within the window. Indexes representing common-mode degenerate subquantities; Indicates the first Each common-mode degenerate subquantum in the epoch robustness scale Indicates the first The median of the absolute deviations of each common-mode degenerate subquantity from its median within the background window. This is a proportionality constant used to convert the median absolute deviation to a scale consistent with the standard deviation;

[0040] Standardized common-mode degenerate subquantity:

[0041]

[0042] in, Indicates the first Robust normalization results of common-mode degenerate subquantities Indicates the first The common-mode degenerate subquantes in the current epoch The value, This indicates a positive number that prevents the denominator from being zero.

[0043] Preferably, the multi-satellite carrier-to-noise ratio common-mode degradation risk index The weighted sum of the three standardized subquantities:

[0044]

[0045] in, Represents the epoch Multi-satellite carrier-to-noise ratio common-mode degradation risk indicator. The weighting coefficients represent the common-mode edge drop subquantities. The weighting coefficients representing the proportions of affected satellites. This represents the weighting coefficient of the common-mode platform's descent component. This represents the robust normalization result of the common-mode edge drop subquantity. This indicates the robust normalization result of the proportion of affected satellites. This represents the robust normalization result of the common-mode platform decreasing the number of subquantities.

[0046] Preferably, the method for extracting early events of suppression interference or signal power degradation includes:

[0047] A risk background window is selected before the current epoch. Within the risk background window, the background median and robust fluctuation scale of the multi-satellite carrier-to-noise ratio common-mode degradation risk index are calculated. An early warning judgment threshold is constructed based on the weighted sum of the background median and the robust fluctuation scale, wherein the weight of the robust fluctuation scale is determined by the early warning threshold adjustment coefficient.

[0048] Candidate event flags are generated based on the comparison results between the multi-satellite carrier-to-noise ratio common-mode degradation risk index and the early warning judgment threshold: when the risk index is greater than or equal to the early warning judgment threshold, the current epoch is marked as a candidate early warning epoch; otherwise, it is marked as a non-candidate early warning epoch.

[0049] When the number of consecutive values ​​of the candidate event flag as candidate warning epochs is not less than the preset start duration epoch, the suppression interference or signal power degradation early event is determined to have started; after the early event has started, when the number of consecutive values ​​of the candidate event flag as non-candidate warning epochs is not less than the preset end duration epoch, the early event is determined to have ended.

[0050] Based on the epochs corresponding to the start and end of the early event, determine the start time, end time, and duration of the early event; based on the set of affected satellites at each epoch during the duration of the early event, determine the set of affected satellites corresponding to the early event, and output the early warning information of the early event.

[0051] The GNSS suppression jamming early warning method based on multi-satellite carrier-to-noise ratio common-mode degradation provided by this invention has the following advantages compared with the prior art:

[0052] (1) The GNSS suppression interference early warning method based on multi-satellite carrier-to-noise ratio common mode degradation of the present invention effectively overcomes the problem of insufficient adaptability of the fixed carrier-to-noise ratio threshold method under different satellite elevation angles, different antenna patterns and different observation environments by establishing a carrier-to-noise ratio sliding background baseline for each satellite and using the historical median of each satellite as a robust estimate of the normal level. At the same time, it introduces the multi-satellite synchronization constraint condition that the number of affected satellites is not less than the minimum number threshold and the affected proportion is not less than the minimum proportion threshold. Only when multiple satellites show carrier-to-noise ratio degradation at the same time will subsequent judgment be triggered. It can accurately distinguish between single-satellite sporadic fluctuations caused by local blockage of a single satellite, low elevation angle fading or multipath effect and multi-satellite common mode degradation caused by suppression interference. It fundamentally solves the defect of high false alarm rate caused by ignoring multi-satellite cooperative information in the single-satellite historical background comparison method.

[0053] (2) The GNSS suppression interference early warning method based on multi-satellite carrier-to-noise ratio (CNR) common-mode degradation of the present invention constructs three complementary feature subquantities: common-mode edge drop subquantity, affected satellite proportion subquantity, and common-mode platform drop subquantity. These subquantities quantify the common-mode degradation phenomenon from three different dimensions: the short-term cumulative intensity of the synchronous CNR drop of multiple satellites, the coverage area of ​​the affected satellites, and the overall CNR drop depth of the affected satellites. The three subquantities complement and corroborate each other, overcoming the limitation of insufficient descriptive ability of a single indicator, and can comprehensively depict the complete evolution process of suppression interference from the edge drop at the start to the platform drop during the continuous process. The multi-satellite carrier-to-noise ratio common-mode degradation risk index obtained by weighted fusion after robust standardization of the three subquantities comprehensively reflects the suddenness, impact range, and drop magnitude of common-mode degradation, providing a reliable multi-dimensional fusion basis for subsequent early warning decisions.

[0054] (3) The GNSS suppression interference early warning method based on multi-satellite carrier noise ratio common mode degradation of the present invention uses the median and median absolute deviation as estimates of background position and dispersion in robust standardization processing. Compared with the traditional standardization method based on mean and standard deviation, it has a natural immunity to outliers and short-term abnormal fluctuations that may exist in the historical background window, and can effectively avoid the pollution of the background estimation by historical abnormal observations. At the same time, each feature quantity is statistically analyzed using a sliding background window independent of the current epoch. The abnormal observations of the current epoch will not feed back into the background estimation, ensuring that the standardization results are not disturbed by the current outliers, thereby ensuring the stability and reliability of the risk index calculation and improving the robustness of the early warning method in complex observation environments.

[0055] (4) The GNSS suppression interference early warning method based on the common mode degradation of the multi-satellite carrier noise ratio of the present invention adopts an adaptive strategy that dynamically constructs the early warning judgment threshold based on the historical background of the risk index itself. It uses the median of the risk index in the background window as the benchmark level, uses the robust fluctuation scale to reflect its normal fluctuation amplitude, and controls the sensitivity through the early warning threshold adjustment coefficient. This enables the early warning judgment threshold to be adaptively adjusted with the normal changes in the observation environment, avoiding the mismatch problem of fixed threshold in different scenarios. At the same time, it introduces event persistence constraints, requiring the candidate event flag to continuously meet the threshold conditions for a preset start duration epoch before the event is confirmed to have started. After the event starts, it requires the candidate event flag to continuously fail to meet the threshold conditions for a preset end duration epoch before the event is confirmed to have ended. This effectively suppresses false triggering caused by observation noise and instantaneous fluctuations, and ensures the time stability and continuity of the early warning results.

[0056] (5) The GNSS suppression interference early warning method based on multi-satellite carrier-to-noise ratio common-mode degradation of the present invention directly uses the carrier-to-noise ratio observation value output by the receiver as input, and can complete the anomaly detection at the signal tracking level without relying on the positioning solution results or post-processing of the observation value. Therefore, it can identify anomalies and trigger early warnings in the early stage of carrier-to-noise ratio common-mode degradation, that is, before the signal power begins to decrease synchronously but before it deteriorates to the point of carrier loop loss, before the observation data is interrupted, and before the positioning accuracy deteriorates significantly. It overcomes the defect of the posterior detection method based on positioning residuals or loss of lock flags, which cannot meet the requirements of early warning and active defense due to lag. It provides valuable prediction time for the initiation of subsequent anti-interference measures, data quality marking, and navigation service risk warning. At the same time, the output early warning information includes the event start time, end time, duration, and the corresponding set of affected satellites, providing users with a complete description of the abnormal event and traceability basis, which has good engineering application value. Attached Figure Description

[0057] Figure 1 This is a flowchart of the early warning method of the present invention.

[0058] Figure 2 The results are the measured GNSS multi-system carrier-to-noise ratio timing and interference interval marking results in the experimental verification. Among them, (a) is the GPS system C / N0 timing, (b) is the BDS system C / N0 timing, (c) is the GLONASS system C / N0 timing, and (d) is the Galileo system C / N0 timing.

[0059] Figure 3 It is a common-mode carrier-to-noise ratio anomaly index constructed based on multi-satellite synchronization degradation constraints in experimental verification. Timing results.

[0060] Figure 4 It is a risk indicator of common-mode degradation of multi-satellite carrier-to-noise ratio in typical suppression interference events during experimental verification. The results of the early warning threshold determination are as follows: (a) is event E1, (b) is event E2, and (c) is event E3. Detailed Implementation

[0061] The following provides a detailed description of specific embodiments of the present invention. It should be understood that the specific embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the scope of the invention.

[0062] The GNSS suppression interference early warning method based on multi-satellite carrier-to-noise ratio common-mode degradation of the present invention achieves early identification of suppression interference or signal power degradation events by analyzing the synchronous degradation characteristics of the carrier-to-noise ratio of multiple satellites relative to their respective historical backgrounds.

[0063] Using GNSS carrier-to-noise ratio (CNR) observation data as input, a degradation judgment benchmark is established based on the historical background of each satellite. Common-mode degradation caused by suppressive interference or signal power degradation is identified through multi-satellite synchronization constraints. Furthermore, common-mode edge drop subquantities, affected satellite proportion subquantities, and common-mode platform drop subquantities are constructed to obtain multi-satellite CNR common-mode degradation risk indicators. Early warning events are extracted by combining these indicators with persistence-based judgments.

[0064] Specifically, the following steps are included:

[0065] Step S1: Data acquisition and construction of the carrier-to-noise ratio observation matrix.

[0066] The raw GNSS observation data is acquired, and the carrier-to-noise ratio (CNR) observation values ​​of each satellite at each epoch are extracted from it. These data are then organized into a structured format that facilitates subsequent processing and analysis, namely the carrier-to-noise ratio observation matrix.

[0067] In this embodiment, GNSS observation data can come from two sources: one is a standardized RINEX (Receiver Independent Exchange Format) observation file, which is suitable for post-processing and analysis; the other is an observation data stream output by the receiver in real time, which is suitable for online or real-time early warning applications.

[0068] The acquired GNSS observation data is analyzed to extract key information, including but not limited to: observation epoch (time stamp), satellite number (PRN number or system ID), satellite system (such as GPS, GLONASS, Galileo, BeiDou, etc.), observation type, and data sampling interval. Subsequently, the carrier-to-noise ratio observation values ​​corresponding to each epoch and each satellite are extracted.

[0069] Construct a carrier-to-noise ratio (CNR) observation matrix with observation epochs as row indices and satellite numbers as column indices. Each element in this matrix uniquely corresponds to a CNR observation value for a specific epoch and a specific satellite. For cases where valid CNR observation values ​​cannot be obtained due to signal loss, unassigned receiver channels, or other reasons, the corresponding position in the matrix is ​​marked as a missing value.

[0070] Satellite In the calendar The carrier-to-noise ratio is This value directly reflects the satellite In the calendar The signal power and tracking quality status.

[0071] Step S2: Determine the degradation of a single satellite and identify the set of affected satellites.

[0072] The effective satellite set for the current epoch is determined based on the carrier-to-noise ratio (CNR) observation matrix. The CNR degradation of each satellite is calculated using the sliding background window prior to the current epoch. The set of affected satellites is determined based on the single-satellite degradation threshold.

[0073] S2-1, Based on the carrier-to-noise ratio observation matrix constructed in step S1, determine the current epoch. The effective satellite set. Effective satellites refer to those satellites that have an effective carrier-to-noise ratio observation at the current epoch, and this set can be represented as:

[0074] (1)

[0075] in, Represents the epoch The effective satellite set, Indicates the satellite number, Indicates satellite In the calendar The observed carrier-to-noise ratio, This indicates the epoch index of the observation.

[0076] For the effective satellite set Each satellite in Select the current epoch. Previous sliding background window The sliding background window contains the satellite's carrier-to-noise ratio observations over a past period, used to estimate its normal signal power level. The sliding background window does not include the current epoch. The purpose of this data is to prevent outlier observations in the current epoch from contaminating the background estimate, thereby improving the reliability of the judgment.

[0077] S2-2, based on historical carrier-to-noise ratio (CNR) observations within the sliding background window, calculates the degradation of the satellite's current CNR relative to its historical background using the following formula:

[0078] (2)

[0079] in, Indicates satellite In the calendar The carrier-to-noise ratio degradation Indicates satellite In the sliding background window The median of all carrier-to-noise ratio observations. Indicates the epoch index within the window; This represents a sliding background window used to estimate the normal signal level of a satellite, containing epochs. Several previous historical periods; Indicates satellite Eratosthenes in the background window The observed carrier-to-noise ratio, Indicates satellite In the current epoch The observed carrier-to-noise ratio, This indicates taking the non-negative part.

[0080] A positive degradation occurs only when the current carrier-to-noise ratio (CNR) is significantly lower than its historical background value; conversely, if the current value is higher than the background value, the degradation is zero. When the CNR degradation exceeds the single-satellite degradation threshold, the corresponding satellite is identified as an affected satellite, and a set of affected satellites is constructed.

[0081] (3)

[0082] in, Represents the epoch The affected satellite set Indicates the satellite number, Represents the epoch The effective set of satellites participating in the carrier-to-noise ratio degradation determination Indicates satellite In the calendar The carrier-to-noise ratio degradation This represents the single-satellite degradation decision threshold. The single-satellite degradation decision threshold is a system preset parameter, and its specific value can be adjusted according to the actual application scenario, receiver performance, and signal frequency to control the sensitivity of single-satellite determination. The typical value range is 3~10 dB-Hz.

[0083] Step S3: Determine the conditions for multi-satellite synchronous degradation.

[0084] The decrease in the carrier-to-noise ratio of a single satellite can be caused by various local factors (such as cloud cover, foliage obstruction, and multipath effects) and does not necessarily indicate suppressive interference. By introducing multi-satellite synchronization constraints, a true global interference event can be distinguished from localized, sporadic event regions. Suppressive interference is only considered to have occurred when multiple satellites simultaneously exhibit degradation characteristics.

[0085] Based on the number of affected satellites, the number of effective satellites, and their ratio, it is determined whether the current epoch meets the conditions for multi-satellite synchronous degradation, and the epochs that meet the conditions are identified as candidate epochs for multi-satellite carrier-to-noise ratio common-mode degradation.

[0086] Obtain the current epoch The affected satellite set and effective satellite set The process for determining the conditions for multi-satellite synchronous degradation is as follows:

[0087] When based on the affected satellite set Calculate the current epoch Number of affected satellites:

[0088] (4)

[0089] in, Represents the epoch The number of satellites affected Represents the epoch The affected satellite set.

[0090] Based on the effective satellite set Calculate the current epoch Valid number of satellites:

[0091] (5)

[0092] in, Represents the epoch The number of effective satellites, Represents the epoch The effective satellite set.

[0093] When the number of affected satellites and the proportion of affected satellites both satisfy the following formula, the current epoch is determined. Meets the conditions for multi-satellite synchronous degradation:

[0094] (6)

[0095] (7)

[0096] in, Indicates the minimum number of satellites affected. This indicates the minimum affected proportion. Epochs that meet the multi-satellite synchronous degradation conditions are identified as candidate epochs for multi-satellite carrier-to-noise ratio common-mode degradation.

[0097] By using the multi-satellite synchronous degradation condition, we can distinguish between single-satellite degradation caused by single-satellite obstruction, low-elevation fading, or local multipath effects, and common-mode degradation where multiple satellites are simultaneously affected. All epochs that meet the multi-satellite synchronous degradation condition will be marked as candidate epochs for multi-satellite carrier-to-noise ratio common-mode degradation. These candidate epochs form the basis for subsequent more refined feature extraction and risk quantification.

[0098] Step S4: Construction of common-mode degenerate subquantities.

[0099] After determining the candidate epochs, the common-mode degradation phenomenon is quantitatively described from multiple dimensions. This invention constructs three complementary characteristic subquantities: a subquantity describing the sudden drop in common-mode edge, a subquantity describing the proportion of affected satellites on the impact surface, and a subquantity describing the overall degradation intensity of the common-mode platform. These three subquantities together constitute multidimensional evidence for assessing risk.

[0100] Within each candidate epoch marked as multi-satellite carrier-to-noise ratio common-mode degradation, the following calculations are performed.

[0101] S4-1, defining a satellite The positive decrease in carrier-to-noise ratio between adjacent epochs is:

[0102] (8)

[0103] in, Indicates satellite From the calendar To the epoch The positive decrease in carrier-to-noise ratio, Indicates satellite In the current epoch The observed carrier-to-noise ratio, Indicates satellite In the previous epoch The observed carrier-to-noise ratio, This indicates taking the non-negative part.

[0104] The positive decrease in carrier-to-noise ratio is positive only when the carrier-to-noise ratio decreases from the previous epoch to the current epoch; otherwise, it is 0.

[0105] S4-2, construct three sub-quantities:

[0106] S4-2-1, the common-mode edge drop quantile is used to capture the intensity of a synchronous drop in the multi-satellite carrier-to-noise ratio within a short time window, and its expression is:

[0107] (9)

[0108] in, Represents the epoch The common-mode edge sudden drop in quantum quantity, This indicates the epoch index within the window. Indicates the epoch index of the observation. Indicates the length of the short-term statistical window. This represents the median of the positive decrease in carrier-to-noise ratio for all affected satellites at epoch τ. Indicates satellite In the calendar The positive decrease in carrier-to-noise ratio, Represents the epoch The affected satellite set.

[0109] The common-mode edge drop quantum quantity reflects the abrupt edge characteristics of common-mode carrier-to-noise ratio degradation by accumulating the intensity of the synchronous drop across multiple satellites within a short time window. The larger the value of the common-mode edge drop quantum quantity, the more drastic and synchronized the drop in carrier-to-noise ratio across multiple satellites in a short period of time, which is a characteristic of suppression jamming.

[0110] S4-2-2, the Affected Satellite Proportion quantifier, reflects the extent of the impact of current interference or degradation. It considers not only whether the affected proportion exceeds a threshold, but also quantifies the degree to which it exceeds the threshold. Its calculation method is as follows:

[0111] (10)

[0112] in, Represents the epoch The proportion of satellites affected Represents the epoch The number of satellites affected Represents the epoch The number of effective satellites, Indicates the minimum affected percentage. This indicates taking the non-negative part.

[0113] The larger the proportion of satellites affected, the wider the range of satellites affected by the interference.

[0114] S4-2-3, the common-mode platform descent quantity, reflects the overall decrease in the carrier-to-noise ratio of the affected satellite ensemble, embodying the intensity level of the interference. Its calculation method is as follows:

[0115] (11)

[0116] in, Represents the epoch The common-mode platform reduces the number of sub-units. Indicates the epoch Carrier-to-noise ratio degradation of all affected satellites The median of Represents the epoch The affected satellite set Indicates satellite In the calendar The carrier-to-noise ratio degradation.

[0117] The larger the value of the common-mode platform descent quantity, the more deeply the overall signal power is suppressed and the more severe the interference.

[0118] Step S5: Robust normalization and calculation of the common-mode degradation risk index for multi-satellite carrier-to-noise ratio.

[0119] Since the three sub-quantities constructed in step S4 have different physical meanings and numerical scales, they cannot be directly fused and compared. This step first performs robust normalization on these three sub-quantities to eliminate the influence of their dimensions and background fluctuations. Then, it assigns different weights according to their importance and weights them to fuse them into a comprehensive multi-satellite carrier-to-noise ratio common-mode degradation risk index, providing a single and reliable basis for the final early warning decision.

[0120] make ( ) represent epochs respectively The common-mode edge drop subquant, the affected satellite proportional subquant, and the common-mode platform drop subquant are given by the following formulas:

[0121] (12)

[0122] in, Represents the epoch The A common-mode degenerate subquant, Represents the epoch The common-mode edge sudden drop in quantum quantity, Represents the epoch The proportion of satellites affected Represents the epoch The common-mode platform reduces the number of sub-units. Index representing the common-mode degenerate subquantity.

[0123] For each subquant In its corresponding sliding background window The background median and robust scale are calculated internally. This invention uses the Median Absolute Deviation (MAD) as an estimator of the robust scale because it is insensitive to outliers.

[0124] The formula for the background median is:

[0125] (13)

[0126] in, Indicates the first Each common-mode degenerate subquantum in the epoch The background median, Indicates the first A common-mode degenerate subquantum in its sliding background window The median of all values ​​in the range. Indicates the first Each common-mode degenerate subquantity is within the epoch of the background window. The value, Indicates the use of estimating the first A sliding background window with normal common-mode degenerate subquantities. This indicates the epoch index within the window. Index representing the common-mode degenerate subquantity.

[0127] The robustness metric formula is:

[0128] (14)

[0129] in, Indicates the first Each common-mode degenerate subquantum in the epoch robustness scale Indicates the first The median of the absolute deviations of each common-mode degenerate subquantity from its median within the background window. Indicates the use of estimating the first A sliding background window with normal common-mode degenerate subquantities. Indicates the first Each common-mode degenerate subquantum in the epoch The value, Indicates the first The background median of the common-mode degenerate subquantities This is the proportionality constant used to convert the median absolute deviation to a scale consistent with the standard deviation.

[0130] Robust normalization is performed on the common-mode degenerate subquant to obtain the normalized common-mode degenerate subquant:

[0131] (15)

[0132] in, Indicates the first Robust normalization results of common-mode degenerate subquantities Indicates the first The common-mode degenerate subquantes in the current epoch The value, Indicates the first The background median of the common-mode degenerate subquantities Indicates the first Robustness scale of common-mode degenerate subquantities This indicates a positive number that prevents the denominator from being zero.

[0133] The standardization process transforms the original subquantity values ​​into anomalies relative to their historical fluctuation levels, making different subquantities comparable.

[0134] Based on the robustly normalized common-mode edge drop subquant, the affected satellite proportion subquant, and the common-mode platform drop subquant, a multi-satellite carrier-to-noise ratio common-mode degradation risk index is constructed:

[0135] (16)

[0136] in, Represents the epoch Multi-satellite carrier-to-noise ratio common-mode degradation risk indicator. The weighting coefficients represent the common-mode edge drop subquantities. The weighting coefficients representing the proportions of affected satellites. This represents the weighting coefficient of the common-mode platform's descent component. This represents the robust normalization result of the common-mode edge drop subquantity. This indicates the robust normalization result of the proportion of affected satellites. This represents the robust normalization result of the common-mode platform decreasing the number of subquantities.

[0137] The specific values ​​of the weights can be preset according to the different application scenarios and the degree of importance attached to the suddenness, scope of impact, and depth of decline. The weights must meet the following conditions:

[0138] (17)

[0139] (18)

[0140] Robust normalization can reduce the impact of background fluctuations and short-term outlier observations on the quantum scale. The multi-satellite carrier-to-noise ratio common-mode degradation risk index obtained by weighted fusion can comprehensively reflect the abruptness, coverage, and magnitude of common-mode degradation.

[0141] Step S6: Early event extraction and warning results output.

[0142] Based on the multi-satellite carrier-to-noise ratio common-mode degradation risk index and preset persistence judgment conditions, early events of suppression interference or signal power degradation are extracted, and the start time, end time, duration, affected satellite set, and early warning results of the early events are output.

[0143] This step mainly includes three sub-processes: candidate event flag generation, event start and end time determination, and warning result output.

[0144] S6-1, Candidate event flag generation.

[0145] A warning threshold needs to be determined. To maintain the threshold's adaptability, it is dynamically calculated based on the historical background of the risk indicator itself. The formula is as follows:

[0146] (19)

[0147] in, Represents the epoch Candidate event flags, Represents the epoch Multi-satellite carrier-to-noise ratio common-mode degradation risk indicator. Represents the epoch The warning threshold. Represents the epoch As candidate early warning era, Represents the epoch This is a non-candidate warning era.

[0148] In the current epoch Previously selected risk background window And calculate the background median of the multi-satellite carrier-to-noise ratio common-mode degradation risk index within the risk background window:

[0149] (20)

[0150] in, Indicates the epoch The background median of the multi-satellite noise ratio common-mode degradation risk index within the risk background window. Indicators of risk In the risk background window The median of all values ​​in the range. This indicates the risk indicator within the window. The value, Indicates the indicators used to estimate risk. A normal horizontal sliding background window, This indicates the epoch index within the window.

[0151] Robust fluctuation scale for calculating the common-mode degradation risk index of multi-satellite carrier-to-noise ratio:

[0152] (twenty one)

[0153] in, Indicates the epoch Risk Background Window: Multi-Satellite Noise Ratio Common-Mode Degradation Risk Indicator robust fluctuation scale, This indicates the risk indicator within the window. The value, Indicates the epoch The background median of the multi-satellite noise ratio common-mode degradation risk index within the risk background window. Indicates the indicators used to estimate risk. A normal horizontal sliding background window, This indicates the epoch index within the window. Indicators of risk The median of the absolute deviation relative to the median within the background window.

[0154] Based on the background median and robust fluctuation scale, a warning threshold is constructed:

[0155] (twenty two)

[0156] in, Represents the epoch The warning judgment threshold This represents the background median of the multi-satellite carrier noise ratio common-mode degradation risk index within the risk background window. Robust fluctuation scale representing the risk index of common-mode degradation of multi-satellite carrier-to-noise ratio. This indicates the adjustment coefficient for the warning threshold. It is a dimensionless positive number, with a value range of 2.0 to 5.0.

[0157] The warning threshold adjustment coefficient is used to control the sensitivity of the warning. The higher the value, the higher the threshold, and the more likely the alert will only report significant events, resulting in a lower false alarm rate; conversely, The smaller the value, the lower the threshold, the more sensitive the warning, and the more warnings may be generated, but the false negative rate is lower.

[0158] Candidate event flags When the number of consecutive epochs with a value of 1 is not less than the preset starting duration epoch, the early event of suppression interference or signal power degradation is determined to have begun. After the early event begins, when the candidate event flag... The early event is considered to have ended when the number of consecutive epochs with a value of 0 is not less than the preset end duration epoch. The end duration determination only takes effect after the early event has started; if the event has not yet started, the end determination is ignored.

[0159] Based on the epochs corresponding to the start and end of the early event, determine the start time, end time, and duration of the early event. Based on the set of affected satellites at each epoch during the duration of the early event, determine the set of affected satellites corresponding to the early event, and output the early event warning result.

[0160] This invention can provide early warning of suppression interference or signal power degradation processes based on the synchronous degradation characteristics of the multi-satellite carrier-to-noise ratio relative to the historical background before observation is interrupted, large-scale loss of lock, or obvious abnormality in positioning results.

[0161] S6-2, The method for determining the start and end times of events in this invention is as follows:

[0162] After obtaining the candidate event flag sequence, a persistence determination condition is introduced to finally confirm the event, so as to filter out short-term random fluctuations.

[0163] Event begins: When the candidate event flag is displayed When the number of consecutive epochs with a value of 1 reaches or exceeds a preset starting duration epoch for the first time, the first epoch of this continuous sequence is determined as the start time of the early event of suppression interference or signal power degradation.

[0164] Event End: After the event begins, when the candidate event flag is displayed. When the number of consecutive epochs with a value of 0 reaches or exceeds a preset end duration epoch, the previous epoch of this consecutive non-compliant sequence is determined as the end time of the event.

[0165] The above rules allow for the precise definition of the lifecycle of each abnormal event.

[0166] S6-3, output of early warning results.

[0167] The duration of the event is calculated based on the determined start and end epochs. Simultaneously, a summary is compiled of all affected satellites that occurred during the event's duration. The union of these sets represents the final set of affected satellites for this event.

[0168] Finally, a complete warning result is output, which includes at least the following information: event start time, event end time, event duration, set of affected satellites, and warning result (i.e., a suppression interference or early event of signal power degradation has been successfully identified).

[0169] Experimental verification:

[0170] like Figure 2 As shown in (a) to (d), each curve corresponds to the C / N0 output of a satellite within a continuous observation epoch. The vertical axis represents the carrier-to-noise ratio (CNR), and the horizontal axis represents the local time. The CNR is the signal quality observation value output by the GNSS receiver baseband processing; its decrease directly reflects the degree of signal power degradation. As can be seen from the figure, during non-interference periods, the C / N0 of most satellites is mainly stable at approximately 40–50 dB-Hz, with relatively small overall fluctuations, indicating that the satellites of each system are in a relatively stable tracking state. In contrast, during the interference period marked by red shading, multiple systems including GPS, BDS, GLONASS, and Galileo experienced varying degrees of synchronous decreases, sharp fluctuations, or short-term drops in C / N0. The C / N0 of some satellites decreased from approximately 45 dB-Hz to 20–30 dB-Hz, and locally approached or fell below 20 dB-Hz, with a decrease of approximately 15–25 dB-Hz. This change is not an accidental fading of a single satellite, but a signal quality degradation exhibited by multiple systems and multiple satellites, showing obvious multi-satellite common-mode characteristics.

[0171] From a temporal distribution perspective, the red shaded areas correspond to multiple continuous or quasi-continuous anomalous intervals, including the strong disturbance intervals around 08:59, 09:10 to 09:20, 09:24 to 09:36, and after 09:38. During these periods, the original stable platform of C / N0 for each system was disrupted, and multiple satellites simultaneously experienced a decrease or rapid jitter. This result indicates that the method of this invention does not rely on the absolute C / N0 threshold of a single satellite for determination. Instead, it first constructs a carrier-to-noise ratio observation matrix with the observation epoch as the row index and the satellite number as the column index, then calculates the degradation amount by combining the historical background of each satellite, and determines whether the current epoch meets the conditions for multi-satellite synchronous degradation through the dual constraints of the number of affected satellites and the proportion of affected satellites, thereby achieving the identification of suppression interference or signal power common-mode degradation processes.

[0172] like Figure 3 As shown, the F1 index is used to characterize the risk of common-mode degradation in the noise ratio of multiple satellites. Its result is not a direct output of the C / N0 of a single satellite, but rather a fusion of the synchronous degradation characteristics of multiple satellites relative to their respective historical backgrounds. Figure 3 It is evident that during non-abnormal periods, F1 remained at a low level close to 0, indicating that ordinary background fluctuations, short-term blockages by single satellites, or fluctuations in individual satellite signals would not pose a significant risk response. However, around 09:12, 09:24, 09:30, and 09:40, F1 showed a significant increase, with the peak value exceeding 200 around 09:24 and exceeding 500 around 09:40, indicating that multiple satellites experienced a stronger and more synchronized carrier-to-noise ratio common-mode degradation during these periods.

[0173] Combination Figure 2 It can be seen that the high value range of F1 corresponds well with the interference time period marked by red shading. Especially around 09:40, Figure 2 Multiple satellite systems in China simultaneously experienced significant fluctuations and declines in the C / N0 ratio. Figure 3 The F1 value also reached its highest level throughout the entire time period, indicating that this method can compress the complex C / N0 anomalies of multiple systems and multiple satellites into a clear common-mode degradation risk indicator. This indicator can reflect the combined effect of the number of affected satellites, the magnitude of degradation, and the degree of synchronization change. When the degradation range of multiple satellites expands, the decline deepens, or the common-mode drop becomes more concentrated, the F1 value increases significantly.

[0174] The results show that the method of the present invention differs from fixed C / N0 threshold detection or single-satellite historical background comparison methods. Instead, it calculates the degradation amount of a single satellite, constrains the synchronization of multiple satellites, and fuses the common-mode degradation risk. This allows F1 to produce a significant response only when multiple satellites exhibit coordinated degradation at the same time. Thus, it can effectively distinguish between sporadic fluctuations of a single satellite and multi-satellite common-mode anomalies caused by suppression interference or signal power degradation.

[0175] like Figure 4 As shown in (a) to (c), This is a fusion risk index constructed based on the multi-satellite carrier-to-noise ratio (CNR) common-mode degradation mechanism. It consists of the single-satellite CNR degradation, the proportion of affected satellites, and the decreasing fraction of the common-mode platform, and is obtained through weighted fusion based on robust normalization. Based on this, analysis of the three typical event intervals E1, E2, and E3 in the figure reveals that… The event exhibited significant, phased increases at each stage, consistently exceeding the warning threshold adaptively constructed based on historical background. Among them, event E1 lasted approximately 241 seconds, event E2 lasted approximately 690 seconds, and event E3 lasted approximately 1261 seconds. It could be stably triggered at different duration scales, indicating that the indicator has a consistent response capability to both short-term sudden disturbances and long-term persistent disturbances. Meanwhile, the corresponding Qevent90(R) values ​​were 0.4023, 0.5145, and 0.4758, respectively, all significantly higher than the background level, further verifying the significant separability between the abnormal phase and the normal observation state from a statistical distribution perspective; where Qevent90(R) represents the joint early warning risk score within the effective duration of the abnormal event. The 90th percentile value is used to characterize the typical intensity of the high-risk phase, in order to help determine the rationality of the severe warning threshold.

[0176] Further integration Figure 2 The original C / N0 observation results shown can be seen that, The high-value range and the synchronous decline period of the multi-satellite carrier-to-noise ratio are well consistent on the time axis, and the synchronous response characteristics are shown in multiple systems such as GPS, BDS, GLONASS and Galileo. This method does not rely on the local changes of a single satellite or a single system, but rather uses the joint degradation information of multiple satellites to model the common-mode anomaly as a whole, thereby achieving a unified characterization and compressed expression of the impact of suppressive interference.

[0177] In terms of the characteristics of temporal evolution It can continuously characterize the complete dynamic evolution of the interference process, from the initial edge drop, to the plateau maintenance in the middle stage, and then to the gradual recovery in the final stage. In the early stage of interference, the indicator rises rapidly and triggers adaptive threshold judgment; in the sustained stage, the indicator remains above the threshold for a long time, forming a stable abnormal range; after the interference ends, it quickly falls back to near the background level, thus verifying the effectiveness and stability of the fused risk indicator in event-level detection from the perspective of temporal structure.

[0178] The above embodiments are merely preferred examples of the present invention and are not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A GNSS suppression-based interference early warning method based on multi-satellite carrier-to-noise ratio common-mode degradation, characterized in that, Includes the following steps: Acquire GNSS observation data, parse the carrier-to-noise ratio (CNR) observation values ​​of each satellite at each epoch from the GNSS observation data, and construct a CNR observation matrix with the observation epoch as the row index and the satellite number as the column index; Based on the carrier-to-noise ratio observation matrix, the effective satellite set for the current epoch is determined. The carrier-to-noise ratio degradation of each satellite is calculated using the sliding background window before the current epoch. The set of affected satellites is determined based on the single-satellite degradation threshold. Based on the number of affected satellites and the number of effective satellites, determine whether the current epoch meets the multi-satellite synchronous degradation condition, and identify the epoch that meets the multi-satellite synchronous degradation condition as candidate epochs for multi-satellite carrier-to-noise ratio common-mode degradation. Within the candidate epochs of the multi-satellite carrier-to-noise ratio common-mode degradation, construct the common-mode edge drop subquant, the affected satellite proportion subquant, and the common-mode platform drop subquant; Robust normalization is performed on the common-mode edge drop subquant, the affected satellite proportion subquant, and the common-mode platform drop subquant, respectively. The three subquants are then weighted and fused to obtain a multi-satellite carrier-to-noise ratio common-mode degradation risk index. The formula for calculating the common-mode edge drop subquant is: ； in, Represents the epoch The common-mode edge sudden drop in quantum quantity, This indicates the epoch index within the window. Indicates the epoch index of the observation. Indicates the length of the short-term statistical window. Indicates the epoch The median of the positive decrease in carrier-to-noise ratio for all affected satellites. Indicates satellite In the calendar The positive decrease in carrier-to-noise ratio, Represents the epoch The affected satellite set; The formula for calculating the proportional quantum of the affected satellite is: ； in, Represents the epoch The proportion of satellites affected Represents the epoch The number of satellites affected Represents the epoch The number of effective satellites, Indicates the minimum affected percentage. This indicates taking the non-negative part; The formula for calculating the common-mode platform descending quantum is: ； in, Represents the epoch The common-mode platform reduces the number of sub-units. Indicates the epoch Carrier-to-noise ratio degradation of all affected satellites the median of Represents the epoch The affected satellite set Indicates satellite In the calendar The carrier-to-noise ratio degradation; Based on the multi-satellite carrier-to-noise ratio common-mode degradation risk index and preset persistence judgment conditions, early events of suppression interference or signal power degradation are extracted, and early warning information of the early events is output.

2. The GNSS suppression interference early warning method based on multi-satellite carrier-to-noise ratio common-mode degradation according to claim 1, characterized in that, The method for constructing the carrier-to-noise ratio (CNR) observation matrix includes: constructing the CNR observation matrix with the observation epoch as the row index and the satellite number as the column index, wherein each element in the CNR observation matrix uniquely corresponds to a CNR observation value of a epoch and a satellite; and marking missing values ​​in the CNR observation matrix for cases where valid CNR observation values ​​cannot be obtained.

3. The GNSS suppression interference early warning method based on multi-satellite carrier-to-noise ratio common-mode degradation according to claim 2, characterized in that, The method for calculating the carrier-to-noise ratio degradation is as follows: ； in, Indicates satellite In the calendar The carrier-to-noise ratio degradation Indicates satellite In the sliding background window The median of all carrier-to-noise ratio observations. Indicates the epoch index within the window; This represents a sliding background window used to estimate the normal signal level of a satellite, containing epochs. Several previous historical periods; Indicates satellite Eratosthenes in the background window The observed carrier-to-noise ratio, Indicates satellite In the current epoch The observed carrier-to-noise ratio, This indicates taking the non-negative part.

4. The GNSS suppression interference early warning method based on multi-satellite carrier-to-noise ratio common-mode degradation according to claim 3, characterized in that, The method for determining the set of affected satellites is as follows: ； in, Represents the epoch The affected satellite set Indicates the satellite number, Represents the epoch The effective set of satellites participating in the carrier-to-noise ratio degradation determination Indicates satellite In the calendar The carrier-to-noise ratio degradation This indicates the threshold for single-star degradation.

5. The GNSS suppression interference early warning method based on multi-satellite carrier-to-noise ratio common-mode degradation according to claim 4, characterized in that, The conditions for multi-satellite synchronous degradation include: the number of affected satellites is not less than the minimum threshold for the number of affected satellites, and the ratio of the number of affected satellites to the number of effective satellites is not less than the minimum threshold for the proportion of affected satellites.

6. The GNSS suppression interference early warning method based on multi-satellite carrier-to-noise ratio common-mode degradation according to claim 1, characterized in that, The robust normalization process includes: letting Representing the three sub-quantities respectively, calculate their background median and robustness scale: ； ； in, Indicates the first The common-mode degenerate subquantum in the epoch The background median, Indicates the first A common-mode degenerate subquantum in its sliding background window The median of all values ​​in the range. Indicates the first Each common-mode degenerate subquantity is within the epoch of the background window. The value, Indicates the use of estimating the first A sliding background window with normal common-mode degenerate subquantities. This indicates the epoch index within the window. Index representing the common-mode degenerate subquantity. ; Indicates the first Each common-mode degenerate subquantum in the epoch robustness scale Indicates the first The median of the absolute deviations of each common-mode degenerate subquantity from its median within the background window. This is a proportionality constant used to convert the median absolute deviation to a scale consistent with the standard deviation; Standardized common-mode degenerate subquantity: ； in, Indicates the first Robust normalization results of common-mode degenerate subquantities Indicates the first The common-mode degenerate subquantes in the current epoch The value, This indicates a positive number that prevents the denominator from being zero.

7. The GNSS suppression interference early warning method based on multi-satellite carrier-to-noise ratio common-mode degradation according to claim 6, characterized in that, The multi-satellite carrier-to-noise ratio common-mode degradation risk index is a weighted sum of three standardized components: ； in, Represents the epoch Multi-satellite carrier-to-noise ratio common-mode degradation risk indicator. The weighting coefficients represent the common-mode edge drop subquantities. The weighting coefficients representing the proportions of affected satellites. This represents the weighting coefficient of the common-mode platform's descent component. This represents the robust normalization result of the common-mode edge drop subquantity. This indicates the robust normalization result of the proportion of affected satellites. This represents the robust normalization result of the common-mode platform decreasing the number of subquantities.

8. The GNSS suppression interference early warning method based on multi-satellite carrier-to-noise ratio common-mode degradation according to claim 7, characterized in that, The method for extracting early events of suppression interference or signal power degradation includes: A risk background window is selected before the current epoch. Within the risk background window, the background median and robust fluctuation scale of the multi-satellite carrier-to-noise ratio common-mode degradation risk index are calculated. An early warning judgment threshold is constructed based on the weighted sum of the background median and the robust fluctuation scale, wherein the weight of the robust fluctuation scale is determined by the early warning threshold adjustment coefficient. Candidate event flags are generated based on the comparison results between the multi-satellite carrier-to-noise ratio common-mode degradation risk index and the early warning judgment threshold: when the risk index is greater than or equal to the early warning judgment threshold, the current epoch is marked as a candidate early warning epoch; otherwise, it is marked as a non-candidate early warning epoch. When the number of consecutive values ​​of the candidate event flag as candidate warning epochs is not less than the preset start duration epoch, the suppression interference or signal power degradation early event is determined to have started; after the early event has started, when the number of consecutive values ​​of the candidate event flag as non-candidate warning epochs is not less than the preset end duration epoch, the early event is determined to have ended. Based on the epochs corresponding to the start and end of the early event, determine the start time, end time, and duration of the early event; based on the set of affected satellites at each epoch during the duration of the early event, determine the set of affected satellites corresponding to the early event, and output the early warning information of the early event.

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