Multi-system fusion positioning method and system of GNSS adaptive screening in interference environment

Abstract

The application discloses a GNSS adaptive screening multi-system fusion positioning method and system in an interference environment, and the method comprises the following steps: interference detection and quality evaluation are performed on original observation data to generate a comprehensive quality score; interference source identification and classification are performed according to the comprehensive quality score and the time sequence characteristics of the observation quantity, signal suppression type and generated deception type interference are distinguished, and disturbed systems, frequency points and observation value types are identified; adaptive observation value screening strategies are executed according to the identification results, a dynamic weight distribution algorithm is used to distribute weights, an interference threshold model is used to set a threshold, and observation values participating in fusion positioning are determined; multi-system and multi-frequency fusion positioning is performed to obtain positioning results and covariance information; and positioning accuracy is verified, and if the positioning accuracy meets the standard, the positioning results are output, and if the positioning accuracy does not meet the standard, the strategies are dynamically adjusted and re-solved.

Description

Technical Field

[0001] This invention relates to the field of Global Navigation Satellite System (GNSS) positioning and anti-interference technology, specifically to a multi-system fusion positioning method and system for adaptive GNSS observation screening under interference environments. This method is used to adaptively screen and weightedly fuse multi-system, multi-frequency observations based on observation quality and interference type in complex electromagnetic environments such as radio frequency interference, signal suppression, and spoofing, thereby improving positioning accuracy and availability. Background Technology

[0002] GNSS is widely used in transportation, energy and power, and emergency services. However, civilian navigation signals have extremely low ground power, making them susceptible to radio frequency interference and deliberate sabotage, leading to decreased positioning accuracy, slower convergence, reduced continuity, and even loss of lock. In real interference events, different GNSS systems, frequencies, and pseudorange / carrier observation types often exhibit significant differences in their responses to interference: for example, suppression interference can cause large-scale loss of observations at some frequencies, a sharp drop in signal-to-noise ratio, and a surge in cycle slips / signal interruptions; while generative spoofing interference may cause significant positional shifts in single-frequency or specific dual-frequency combination solutions while maintaining high data availability.

[0003] The existing fusion positioning methods mainly have the following problems: (1) They use fixed thresholds or uniform weights to treat all systems and frequencies without combining dynamic adjustments such as elevation angle and epoch quality, and easily equate contaminated observations with normal observations in the solution; (2) They do not distinguish the different effects of suppression interference and deception interference on the availability of observations, residual distribution and positioning consistency, and it is difficult to deal with the two types of risks of "no solution / lost lock" and "solution but serious bias" respectively; (3) When fusion of multiple systems and multiple frequencies, they do not make full use of multi-dimensional quality indicators such as signal-to-noise ratio, multipath, cycle slip ratio, pseudorange residuals and other multi-dimensional quality indicators for comprehensive scoring and observation level screening; (4) After the solution, there is a lack of closed-loop feedback linked with interference detection and weighting strategy, and it is impossible to automatically call back the screening strategy or detection threshold when the positioning accuracy is not up to standard.

[0004] Therefore, how to adaptively filter observations in a noisy environment and form a closed loop with multi-system fusion positioning and result verification is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] To address the shortcomings of existing technologies, such as insufficient anti-interference capability due to fixed thresholds and uniform weights, confusion between suppression and deception interference handling, lack of use of multi-dimensional quality for observation-level screening, and disconnect between calculation and detection, this invention proposes a multi-system fusion positioning method and system for adaptive GNSS observation screening under interference environments. This method improves positioning accuracy and robustness under interference environments by employing interference detection and comprehensive quality scoring, identification of interference source types and affected systems / frequency points / observations, observation screening driven by dynamic weights and interference threshold models, multi-system multi-frequency fusion calculation using weighted least squares and Kalman filtering, and multi-dimensional verification and feedback adjustment such as residuals and precision geometry. It also adapts to the differential mechanism of suppression / deception.

[0006] According to one aspect of the present invention, a multi-system fusion positioning method with GNSS adaptive filtering under interference environment is provided, comprising: Interference detection and quality assessment are performed on the raw observation data within the current epoch and sliding time window to generate a comprehensive quality score that characterizes the reliability of a single satellite, a single frequency point, and a single type of observation. Based on the comprehensive quality score and the time series characteristics of the observations, interference sources are identified and classified, at least distinguishing between signal suppression interference and generative deception interference, and identifying GNSS systems, frequencies and observation types whose interference levels exceed a preset threshold. Based on the results of interference source identification and classification and the comprehensive quality score, an adaptive observation screening strategy is implemented. The strategy uses a dynamic weight allocation algorithm to assign weights to the screened observations and uses an interference threshold model to set detection and elimination thresholds, thereby determining the observations corresponding to the system, satellite, frequency point and pseudorange / carrier combination participating in the fusion positioning. Multi-system, multi-frequency fusion positioning is performed on the selected observations to obtain the positioning results and covariance information for the current epoch. If the positioning accuracy of the positioning result meets the standard, the positioning result is output; if it does not meet the standard, dynamic strategy adjustment is performed, and the adaptive observation filtering step is returned to recalculate.

[0007] As a further technical solution, the evaluation dimensions of the interference detection and quality assessment include at least the availability of observations, the deviation of the signal-to-noise ratio from the threshold, the cycle slip ratio or carrier phase discontinuity statistics, multipath error and pseudorange residual; the comprehensive quality score is obtained by weighted summation of the quantitative scores of each dimension, and the weight coefficients are calibrated according to the application scenario.

[0008] As a further technical solution, the interference source identification and classification includes: Within a sliding time window, if the same system or frequency point meets at least two of the following conditions and the residual does not exhibit a stable single-point shift, it is determined to be a signal suppression type of interference: the proportion of missing observations is greater than or equal to the first missing threshold, or the length of consecutive missing observations is greater than or equal to the first consecutive missing threshold; the proportion of epochs with a signal-to-noise ratio below the dynamic threshold is greater than or equal to the first low signal-to-noise ratio proportion threshold, and the length of the longest consecutive low signal-to-noise ratio segment is greater than or equal to the first consecutive low signal-to-noise ratio length threshold; the cycle slip or interruption event rate is greater than or equal to the first multiple threshold of the interference-free baseline value or greater than or equal to the baseline mean plus three standard deviations; Within a sliding time window, if the same system or frequency point satisfies the condition that the data availability rate is greater than or equal to the first availability rate threshold, and the positioning deviation magnitude is greater than or equal to the first deviation magnitude threshold in multiple consecutive epochs, or the differential deviation relative to other frequency point combinations exceeds the historical mean plus three times the standard deviation, and the residual test and the multiple solution consistency test fail, it is judged as generative deceptive interference.

[0009] As a further technical solution, the adaptive observation filtering strategy includes: For scenarios dominated by suppression-type interference, priority should be given to eliminating satellite links with an overall quality score below the third threshold, an observation missing ratio exceeding the fourth threshold, a consecutive missing length exceeding the fifth threshold, or those that are only available on a single frequency. For scenarios dominated by deceptive interference, priority should be given to removing observations whose residuals exceed the limit after participating in the solution or whose results are inconsistent with those of independent subsets. Additionally, the system and frequency combination that can maintain continuous carrier phase tracking and pseudorange accuracy better than the meter level in this interference event is retained as a priori preference to participate in weight fine-tuning.

[0010] As a further technical solution, in the dynamic weight allocation algorithm, the weight of the pseudorange observation is related to the power function of the comprehensive quality score, the power of the sine function of the satellite elevation angle, and the prior variance of the pseudorange measurement noise; the weight of the phase observation is related to the power function of the comprehensive quality score, the power of the sine function of the satellite elevation angle, the cycle slip risk suppression factor, and the prior variance of the phase measurement noise; the cycle slip risk suppression factor is negatively correlated with the cycle slip risk probability.

[0011] As a further technical solution, the verification of the positioning results includes one or more of the following methods: residual analysis, accuracy factor evaluation, positioning consistency test, and multi-solution consistency test; the multi-solution consistency test includes: comparing the full observation solution with multiple candidate location estimates obtained after removing high-risk subsets, calculating the sum of the squared distances between each candidate location estimate and the average location estimate, divided by the reference variance, as the consistency score.

[0012] As a further technical solution, the dynamic strategy adjustment includes one or more of the following: Adjust the positioning strategy, including changing the number of weighted least squares iterations, robust estimation function, or Kalman filter update strategy; Adjust the interference detection threshold, including correcting the elevation angle-related parameters or individual thresholds in the interference threshold model; Adjusting the weighting coefficients includes correcting the mapping coefficients of the comprehensive quality score in the dynamic weighting allocation algorithm or the prior noise ratio of each satellite navigation system.

[0013] According to one aspect of the present invention, a multi-system fusion positioning system with GNSS adaptive filtering under interference environment is provided, comprising: The quality assessment module is used to perform interference detection and quality assessment on the raw observation data within the current epoch and sliding time window, and generate a comprehensive quality score. The interference identification module is used to identify and classify interference sources based on the comprehensive quality score and the time series characteristics of the observations, at least distinguishing between signal suppression interference and generative deception interference, and identifying GNSS systems, frequencies and observation types whose interference level exceeds a preset threshold. The filtering module is used to execute an adaptive observation filtering strategy based on the results of interference source identification and classification and the comprehensive quality score, to determine the observations corresponding to the systems, satellites, frequency points and pseudorange / carrier combinations participating in the fusion positioning; The fusion positioning module is used to perform multi-system, multi-frequency fusion positioning on the filtered observations to obtain the positioning results and covariance information of the current epoch. The verification feedback module is used to output the positioning result when the positioning accuracy of the positioning result meets the standard, and to perform dynamic strategy adjustment when the accuracy does not meet the standard, and to trigger the filtering module and the fusion positioning module to recalculate.

[0014] According to one aspect of the present invention, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the multi-system fusion positioning method for GNSS adaptive screening under interference environment.

[0015] According to one aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the multi-system fusion positioning method for GNSS adaptive screening under interference conditions.

[0016] This invention, through a closed loop of "detection—identification—screening—fusion—verification—feedback," has the following beneficial effects: (1) A comprehensive quality score is formed by combining multiple indicators such as observation availability, signal-to-noise ratio, cycle slip, multipath and pseudorange residuals, which can better reflect the true reliability of observation under interference conditions than a single indicator threshold. (2) Distinguish between suppression and deception and identify the disturbed system, frequency and observation type to make the screening strategy consistent with the physical mechanism of interference and alleviate problems such as "large deviation under high availability" or "frequent no solution". (3) Dynamic weight and interference threshold model work together to achieve adaptive screening of observation values ​​and fusion of multiple systems and frequencies, thereby improving the positioning accuracy and availability under complex interference. (4) When the verification fails, multiple paths are fed back to the threshold, weight and solution strategy to enhance the system’s adaptability in time-varying disturbances. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the multi-system fusion positioning method for GNSS adaptive screening under interference conditions provided in an embodiment of the present invention. Detailed Implementation

[0019] The terms “comprising” and “having”, and any variations thereof, in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In addition, the technical features of the various embodiments or individual embodiments provided by the present invention can be arbitrarily combined to form new technical solutions. Such combinations are not bound by the order of steps and / or structural composition patterns, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0021] Figure 1This invention provides a schematic flowchart of a multi-system fusion positioning method for adaptive GNSS selection under interference environments. The flowchart sequentially shows: interference detection and quality assessment; interference source identification and classification; adaptive observation selection strategy (including dynamic weight allocation algorithm, interference threshold model, and observation selection process); multi-system multi-frequency fusion positioning (weighted least squares method, Kalman filtering); positioning result verification and feedback (residual analysis, DOP evaluation, positioning consistency check, and multi-solution consistency check); determining whether the positioning accuracy meets the standard; if it does, output the result; otherwise, adjust the dynamic strategy and feed it back to the weighted least squares method, interference threshold model, and dynamic weight allocation algorithm.

[0022] See appendix Figure 1 As can be seen, the implementation process of the multi-system fusion positioning method for GNSS adaptive screening under interference environment described in the embodiments of the present invention includes the following steps.

[0023] Step 1: Interference Detection and Quality Assessment. The raw observation data is organized by epoch and satellite-signal-observation type. Within a sliding time window, the following quantities are calculated or read, normalized, and then fused into a comprehensive quality score. The comprehensive quality score is used to characterize the reliability of a single satellite, single frequency, and single type of observation (code pseudorange / phase). The comprehensive quality score employs a weighted summation method, with weighting coefficients calibrated according to the application scenario. It includes: observation availability (effective epoch ratio, dual-frequency availability, etc.); signal-to-noise ratio threshold analysis (using a dynamic signal-to-noise ratio threshold that varies with the satellite elevation angle; values ​​below the threshold are considered anomalous components); cycle slip ratio or carrier phase discontinuity events (including cycle slips, missing epochs, missing signals, etc.); multipath error; and pseudorange residuals. The larger the value, the more reliable the link.

[0024] One specific method for constructing the comprehensive quality score and sub-indicators described in step one is as follows: Let the normalized component score for a certain satellite signal at a certain epoch be... (Availability) (Signal-to-noise ratio) (Week slips / discontinuities, values ​​are negatively correlated with the week slip ratio or interruption rate) (Multipath, the value is negatively correlated with the multipath error) (Pseudorange residuals or code noise), then the overall quality score is expressed as: (1), in ~ The weights are non-negative and sum to 1, calibrated offline based on the interference environment. Signal-to-noise ratio analysis uses elevation angle. (Unit: degrees) Related dynamic threshold When the measured signal-to-noise ratio is lower than Time reduction ; Adopting random The monotonic function form is increased to balance the attenuation at low elevation angles and the quality requirements at high elevation angles.

[0025] To facilitate project implementation, a comprehensive quality score was used. The sub-scores can be further defined in the following form (in units of single epoch, single satellite, and single signal link): 1) Availability score: (2), in This represents the number of effective observation epochs for this link within the sliding time window. This is the theoretically visible epoch number.

[0026] 2) Signal-to-noise ratio score: First define the dynamic threshold. (3), In the formula, This is the minimum signal-to-noise ratio threshold at the low elevation angle. This is the maximum signal-to-noise ratio threshold at the high elevation angle. The elevation angle of the satellite (unit: degrees).

[0027] And the signal-to-noise ratio observation Mapped to normalized score (4), in To normalize the scores, For shape parameters.

[0028] 3) Cycle slip / discontinuity score: The cycle slip ratio or discontinuity rate of the link within the sliding time window. As an indicator, it can be defined as: (5), In the formula, This is a reference scale parameter (normalized threshold) for the cycle slip ratio or discontinuity rate, used to control... When it increases The rate of decay.

[0029] 4) Multipath score: Estimated magnitude based on the multipath propagation of the link. As an indicator (6), In the formula, This is a reference scale parameter for multipath error (in terms of standard deviation), used to measure the magnitude of multipath estimation. Normalize and adjust the fraction decay rate.

[0030] 5) Pseudorange residual / code noise fraction: Using the equivalent code noise amplitude as an indicator, it can be defined as: (7), In the formula, The score is for the pseudorange residual / code noise component. The equivalent code noise amplitude, This is the reference scale parameter for code noise (in terms of standard deviation).

[0031] The above sub-item definitions guarantee that when suppressive interference leads to missing observations, decreased SNR, increased cycle slips / interruptions, or deteriorated residuals, the scores of each sub-item will decrease accordingly, thus affecting the overall performance. The differences are quantifiable.

[0032] Step Two: Interference Source Identification and Classification. This is combined with a comprehensive quality score. And identify interference types based on the time series characteristics of each observation. Let the sliding window length be... (600 s), the total number of epochs within the window is The following quantification criteria are defined, and each threshold can be calibrated offline.

[0033] (1) Proportion of missing observations or consecutive missing epoch length At that time, it was determined to be a large area of ​​missing tissue; among which N represents the number of missing epochs in the window, and N represents the total number of epochs in the window. (2) Proportion of epochs with signal-to-noise ratio below the threshold And the longest continuous low signal-to-noise ratio segment When the signal-to-noise ratio is consistently below the dynamic threshold, it is determined that the signal-to-noise ratio remains below the threshold. For window signal-to-noise ratio below dynamic threshold The epoch number; (3) Cycle slip / interruption event rate satisfy or At that time, it was determined that cycle slips and signal interruptions increased significantly; among them The mean, standard deviation, and baseline values ​​are obtained from statistics based on an interference-free baseline. (4) Data availability (Dual-band scenario with additional dual-band availability) ); The number of effective observation epochs within the window; (5) Positioning deviation modulus In continuous Individual calendar ( )satisfy Or, the differential deviation relative to other frequency combinations exceeds its historical average. ; This is the estimated standard deviation of the positioning error.

[0034] When the same system or frequency point simultaneously satisfies at least two of (1) to (3) and the residual does not show a stable single-point offset, it tends to be judged as signal suppression interference; when (4) and (5) are satisfied, and the residual test and the multiple solution consistency test both fail, it tends to be judged as generative deception interference. At the same time, the GNSS system identifier, frequency point identifier, and observation type identifier (the affected set of code observation C and carrier phase L) that are more severely affected by interference are output.

[0035] One implementation method for distinguishing between suppression and deception is as follows: For candidate links within the same time window, first construct a link risk score based on the sub-item scores, and then make a judgment based on the consistency difference between the "full observation solution and the high-quality subset solution".

[0036] 1) Suppression risk score (mainly based on availability / signal-to-noise ratio / discontinuous degradation): (8), in This represents the average sub-score within the current link set (the set of the same system / frequency / observation type). These are non-negative weighting coefficients.

[0037] 2) Deception Risk Score (primarily based on solution consistency failure): Before obtaining the dynamic selection, two types of "trial solutions" can be calculated: (9), In the formula, For the full observation set The weighted least squares trial solution For high-quality observation sets The weighted least squares trial solution This indicates that a weighted least squares solution is performed using the corresponding observation set.

[0038] in For a high-quality subset. Define the consistency metric as... (10) In the formula, To deceive the consistency of risk measurement, Describing the Euclidean norm, The estimated covariance matrix for the trial solution of a high-quality subset. Represents the trace of a matrix. For the first The overall quality score of the observation links, Thresholds are used to determine high-quality observations.

[0039] in This is the estimated covariance for a high-quality subset. A composite criterion can also be constructed by combining residual statistics. (11), in The cost of weighted residuals for all observations, For equivalent degrees of freedom, These are the weighting coefficients.

[0040] 3) Classification decision-making: (12) (13) In the formula, To suppress risk scores, To deceive the risk composite score, and These are the thresholds for judgments of suppression and deception, respectively. This is the result of the interference category determination.

[0041] The affected system, frequency point, and observation type are obtained by aggregating the risk scores of each link. For example, for a certain system... : (14) In the formula, and For each system The average suppression risk score and the average deception risk score, For the system The corresponding set of observation links, The number of elements in the set. For link index, and The first The combined score of suppression risk and deception risk for each link.

[0042] when or When the threshold is exceeded, the system is placed into a set of reduced weights or more strictly excluded.

[0043] Step 3: Adaptive Observation Selection Strategy. Establish a dynamic weight allocation algorithm: For observations that pass the threshold selection, both the pseudorange and phase weights are correlated with the overall quality score. It is related to the prior measurement of noise and state risk factors.

[0044] For pseudorange observations pseudorange observation rights ,in For pseudorange measurement noise prior variance, The satellite elevation angle. These are calibration coefficients used to adjust the influence of mass and elevation angle on the weights; the observation noise covariance in Kalman filtering. and Mapping proportionally or according to empirical factors allows high-quality observations to account for a larger proportion of updates.

[0045] Phase observation rights In addition to the pseudorange weights, a cycle slip risk suppression factor is considered, in which... The prior variance of phase measurement noise. A quality scoring index used to adjust weight pairs Sensitivity The elevation angle index is used to adjust the weights of the elevation angle term. Sensitivity Let the cycle slip risk suppression factor be (set as follows) , For the probability of a weekly jump, (This is a calibration coefficient). When a cycle slip or high-risk discontinuity is detected, it is reduced by... Phase observations are downweighted and removed directly if necessary.

[0046] Establish an interference threshold model: the signal-to-noise ratio threshold and multipath threshold change dynamically with the satellite elevation angle. At low elevation angles, the thresholds are appropriately relaxed to adapt to the multipath environment. At the same time, when the number of effective available satellites in a certain frequency band is lower than the minimum requirement (e.g., less than 4), the removal of observations in that frequency band or overall downweighting is triggered.

[0047] The following threshold and iterative reweighting rules are used to filter observations: 1) Standardized observation residuals: (15) In the formula, For the first Observations, In order to estimate the state The theoretical observation value calculated by the observation model, To observe the residuals, To standardize the residuals, For the first The standard deviation of each observation The standard deviation is the unit weight. For the first Observation weights.

[0048] in , This is the estimate for the current iteration. Therefore... (16).

[0049] 2) Accept / Reject Threshold (Suppression / Spoofing categories can be mapped to different threshold coefficients): If and Then the observed value The dynamic quality threshold can be set as follows: (17) In the formula, For the first The overall quality score of the observation links, This refers to the dynamic quality threshold that varies with the satellite's elevation angle. To standardize the residual threshold coefficient, This is the set of observations received after filtering. The satellite elevation angle. To normalize the elevation angle scale, As the benchmark quality threshold, This is the elevation angle-related threshold adjustment coefficient.

[0050] 3) Iterative reweighting (robustization): (18) in For the number of iterations, The intensity coefficient is used to suppress excessively large residual observations.

[0051] Execute the observation screening process: prioritize the elimination of dominant suppression scenarios. Satellite links below the threshold, severely missing, or only available on a single frequency; observations with residuals exceeding the threshold or inconsistent with independent subset calculation results after participating in the calculation are preferentially excluded for scenarios with spoofing risk; relatively robust system and frequency combinations in this interference event are retained (in similar real interference cases, some constellations or frequencies may still maintain relatively stable carrier tracking and meter-level accuracy, and participate in weight fine-tuning as prior preferences).

[0052] Step 4: Multi-system, multi-frequency fusion positioning. Using the filtered pseudorange and / or carrier phase observations, the receiver position, receiver clock bias, and necessary ambiguities (selected according to the solution mode) are estimated using weighted least squares method. Kalman filtering is applied to the moving vehicle to predict and update the state vectors such as position, velocity, clock bias, and clock drift. The covariance of process noise and observation noise is consistent with or proportional to the weights and quality scores in Step 3, realizing multi-system, multi-frequency fusion positioning and outputting state estimates and covariance matrices.

[0053] One implementation method of the multi-system, multi-frequency fusion positioning is as follows: 1) Weighted Least Squares (WLS) Fusion Objective: To stack selected observations from multiple systems and frequencies into an observation vector. , The corresponding linearized observation equation is written as (19) In the formula, For a moment The observation vector, To design a matrix for linearized observations, Let be the state vector to be estimated. This is the observed noise vector.

[0054] in The covariance is , The weighted least squares estimate is: (20) In the linear case, it can be written as a closed-form solution: (twenty one), If organized separately according to the system, it can be written in a block-stacking form: (twenty two).

[0055] 2) Kalman Filter (KF) Recursive Update: This updates the status of position, velocity, clock difference, etc. , Let the state transition equation be (twenty three), in For state covariance, This is the process noise covariance. Measurement updates are as follows: (twenty four), In the formula, For a moment The prior (predicted) state, For a moment The posterior (updated) state, This is the state transition matrix; and These are the prediction and update state covariance matrices, respectively. To innovate the covariance matrix, This is the Kalman gain matrix.

[0056] Step 5: Verification and Feedback of Positioning Results. Verify the output of Step 4: Residual analysis checks if the standardized residuals exceed the threshold; DOP assessment determines if the current geometric strength is sufficient to support the nominal accuracy; positioning consistency check compares the current solution with short-term historical solutions or kinematic model predictions; multi-solution consistency check compares the combined solution with the subset solution after removing suspicious systems / frequency points. If the overall judgment indicates that the positioning accuracy meets the standard, output the position, velocity, accuracy indicators, and health indicators; if not, execute dynamic strategy adjustments: adjust the positioning strategy (change the number of weighted least squares iterations, robust estimation function, or Kalman filter update strategy); adjust the interference detection threshold (correct the elevation angle-related parameters or individual thresholds in the interference threshold model); adjust the weight coefficients (correct the dynamic weight allocation algorithm). The mapping coefficients or prior noise ratios of each system are calculated, and the process returns to steps three and four to recalculate until the target is met or the preset maximum number of feedbacks is reached.

[0057] One implementation method for verifying and providing feedback on the positioning results is as follows: 1) Standardized Residual Test (NIS / Chi-square Test): For vector... Define covariance As shown in step four, the normalized square is: (25) when The epoch is considered to be consistent with the observation statistics; otherwise, feedback readjustment is triggered. Given a degree of freedom and significance level (Chi-square quantiles).

[0058] 2) DOP / Geometric Solvability Evaluation: Based on the geometric matrix (or by) Extracted position sub-block) calculation Then it can be adopted or general form and set As one of the conditions for meeting the standard.

[0059] 3) Position accuracy criterion (circular probability error): Obtain the 1-sigma or circular probability value of the horizontal plane error from the covariance, for example, the circular probability error. (26) In the formula, The probability error index is a circularity indicator. This represents the standard deviation of the horizontal position.

[0060] in ( and (These are the standard deviations of the position errors in the two directions of the horizontal coordinate, respectively). If the circular probability error reaches the standard threshold, then the positioning accuracy is considered to be up to standard.

[0061] 4) Consistency test of multiple solutions: Compare the full observation solution with the multiple candidate location estimates obtained by removing the high-risk subset. Define the consistency score: (27) And set As one of the conditions for passing.

[0062] 5) Feedback update rules (adjust threshold and weight parameters if criteria are not met): Adaptive scaling can be used. (28) In the formula, For the updated quality threshold, To update the previous quality threshold, This is the threshold adjustment step size coefficient. For degrees of freedom Significance level The chi-square quantile value below, This is the updated observation noise covariance matrix. This is the covariance scaling factor.

[0063] (29) In the formula, This is the maximum scaling value. The consistency-driven adjustment coefficient, The consistency criterion threshold, and These represent operations to retrieve the maximum and minimum values, respectively.

[0064] It can simultaneously tighten the screening threshold or increase the residual threshold weight, and update the parameters. : (30) If the above feedback adjustment still fails to meet the pass conditions, then the maximum number of iterations will be limited or a "degraded / unreliable" flag will be output.

[0065] Based on the same inventive concept as the aforementioned method embodiments, this invention also provides a multi-system fusion positioning system for GNSS adaptive filtering under interference environments, the system comprising the following modules:

[0066] The quality assessment module performs interference detection and quality assessment on the raw observation data within the current epoch and sliding time window, generating a comprehensive quality score characterizing the reliability of individual satellites, single frequency points, and single types of observations. Specifically, the assessment dimensions include at least observation availability, deviation of the signal-to-noise ratio from the threshold, cycle slip ratio or carrier phase discontinuity statistics, multipath error, and pseudorange residuals. The comprehensive quality score is obtained by weighted summation of the quantified scores of each dimension, with the weighting coefficients calibrated according to the application scenario.

[0067] The interference identification module is used to identify and classify interference sources based on the comprehensive quality score and the time series characteristics of the observations, at least distinguishing between signal suppression interference and generative deception interference, and identifying GNSS systems, frequencies, and observation types whose interference levels exceed a preset threshold. Specifically, within a sliding time window, if the same system or frequency meets at least two of the following conditions: observation missing ratio or continuous missing length, persistently low signal-to-noise ratio, and cycle slip / interruption event rate, and the residual does not show a stable single-point shift, it is determined to be signal suppression interference; if the same system or frequency meets the data availability rate condition and the positioning deviation condition, and the residual test and multi-solution consistency test fail, it is determined to be generative deception interference.

[0068] The filtering module, based on the results of interference source identification and classification and the comprehensive quality score, executes an adaptive observation filtering strategy to determine the observations corresponding to the systems, satellites, frequencies, and pseudorange / carrier combinations participating in the fusion positioning. The strategy employs a dynamic weight allocation algorithm to assign weights to the filtered observations and uses an interference threshold model to set detection and rejection thresholds. Specifically, the weights of pseudorange observations are related to the power function of the comprehensive quality score, the power of the sine function of the satellite elevation angle, and the prior variance of the pseudorange measurement noise; the weights of phase observations additionally consider a cycle slip risk suppression factor. For scenarios dominated by suppression-type interference, satellite links with comprehensive quality scores below the threshold, severe observation gaps, or only single-frequency usable links are preferentially rejected; for scenarios dominated by deceptive interference, observations whose residuals exceed limits after calculation or are inconsistent with the independent subset calculation results are preferentially rejected.

[0069] The fusion positioning module performs multi-system, multi-frequency fusion positioning on the filtered observations to obtain the current epoch positioning result and covariance information. Specifically, it employs weighted least squares method for epochal or batch processing solutions, and uses Kalman filtering to perform time-based recursion and smoothing of position, velocity, clock bias, and other states. The weight matrix of the weighted least squares method is composed of the observation weights determined by the filtering module, and the observation noise covariance of the Kalman filter is proportional to these weights.

[0070] The verification feedback module verifies the positioning results and outputs the positioning result when the positioning accuracy meets the standard; if it does not meet the standard, it performs dynamic strategy adjustment and triggers the screening module and fusion positioning module to recalculate. The verification includes one or more of the following methods: residual analysis, accuracy factor evaluation, positioning consistency check, and multi-solution consistency check. The multi-solution consistency check includes comparing the full observation solution with multiple candidate position estimates obtained after removing high-risk subsets, calculating the sum of the squared distances between each candidate position estimate and the average position estimate, divided by the reference variance, as the consistency score. The dynamic strategy adjustment includes adjusting the positioning strategy (changing the number of weighted least squares iterations, robust estimation function, or Kalman filter update strategy), adjusting the interference detection threshold (correcting the elevation angle-related parameters or individual thresholds in the interference threshold model), and adjusting the weight coefficients (correcting the mapping coefficients of the comprehensive quality score in the dynamic weight allocation algorithm or the prior noise ratio of each satellite navigation system).

[0071] The modules are connected sequentially to form a closed-loop processing flow of "detection-identification-screening-fusion-verification-feedback". This can adaptively adjust the observation screening and weight allocation strategy according to the type of interference, thereby improving the positioning accuracy and availability in complex electromagnetic environments.

[0072] Based on the same inventive concept as the foregoing embodiments, this embodiment of the invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the multi-system fusion positioning method for GNSS adaptive screening under interference environment described above.

[0073] The computer equipment can be a GNSS receiver, vehicle-mounted terminal, handheld measuring device, base station equipment, server, or other electronic device with computing capabilities. The processor can be a central processing unit (CPU), digital signal processor (DSP), field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), or other programmable logic device. The memory can be read-only memory (ROM), random access memory (RAM), flash memory, hard disk, solid-state drive, or other media suitable for storing program code. When the processor executes the program stored in the memory, it can perform the steps described in the foregoing method embodiments, such as interference detection and quality assessment, interference source identification and classification, adaptive observation screening, multi-system multi-frequency fusion positioning, and positioning result verification and feedback.

[0074] Based on the same inventive concept as the foregoing embodiments, this embodiment of the invention also provides a computer-readable storage medium storing a computer program thereon, wherein when the computer program is executed by a processor, it implements the steps of the multi-system fusion positioning method for GNSS adaptive screening under interference environment described above.

[0075] The computer-readable storage medium can be any tangible medium that contains or stores program instructions, including but not limited to: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, solid-state drives (SSDs), etc. When the program in the storage medium is executed by a processor, the processor performs all or part of the steps in the foregoing method embodiments. The storage medium can be sold or used independently, or it can be built into the foregoing computer device.

[0076] In summary, this invention drives adaptive observation screening and multi-system multi-frequency fusion positioning through multi-dimensional quality assessment and interference type identification under interference environment, and dynamically adjusts thresholds and weights through verification feedback closed loop. It can improve the accuracy and reliability of terminal or reference station-level fusion positioning in complex electromagnetic environment without relying on interference spatial geometric inversion. It is suitable for application scenarios that require robust PNT capabilities, such as vehicle-mounted, surveying and mapping, and continuously operating reference stations.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A multi-system fusion positioning method for GNSS adaptive selection under interference environment, characterized in that, include: Interference detection and quality assessment are performed on the raw observation data within the current epoch and sliding time window to generate a comprehensive quality score that characterizes the reliability of a single satellite, a single frequency point, and a single type of observation. Based on the comprehensive quality score and the time series characteristics of the observations, interference sources are identified and classified, at least distinguishing between signal suppression interference and generative deception interference, and identifying GNSS systems, frequencies and observation types whose interference levels exceed a preset threshold. Based on the results of interference source identification and classification and the comprehensive quality score, an adaptive observation screening strategy is implemented. The strategy uses a dynamic weight allocation algorithm to assign weights to the screened observations and uses an interference threshold model to set detection and elimination thresholds, thereby determining the observations corresponding to the system, satellite, frequency point and pseudorange / carrier combination participating in the fusion positioning. Multi-system, multi-frequency fusion positioning is performed on the selected observations to obtain the positioning results and covariance information for the current epoch. If the positioning accuracy of the positioning result meets the standard, the positioning result is output; if it does not meet the standard, dynamic strategy adjustment is performed, and the adaptive observation filtering step is returned to recalculate.

2. The multi-system fusion positioning method for GNSS adaptive screening under interference environment according to claim 1, characterized in that, The evaluation dimensions of the interference detection and quality assessment include at least the availability of observations, the deviation of the signal-to-noise ratio from the threshold, the cycle slip ratio or carrier phase discontinuity statistics, multipath error, and pseudorange residual; the comprehensive quality score is obtained by weighted summation of the quantitative scores of each dimension, and the weight coefficients are calibrated according to the application scenario.

3. The multi-system fusion positioning method for GNSS adaptive screening under interference environment according to claim 1, characterized in that, The identification and classification of interference sources includes: Within a sliding time window, if the same system or frequency point meets at least two of the following conditions and the residual does not exhibit a stable single-point shift, it is determined to be a signal suppression type of interference: the proportion of missing observations is greater than or equal to the first missing threshold, or the length of consecutive missing observations is greater than or equal to the first consecutive missing threshold; the proportion of epochs with a signal-to-noise ratio below the dynamic threshold is greater than or equal to the first low signal-to-noise ratio proportion threshold, and the length of the longest consecutive low signal-to-noise ratio segment is greater than or equal to the first consecutive low signal-to-noise ratio length threshold; the cycle slip or interruption event rate is greater than or equal to the first multiple threshold of the interference-free baseline value or greater than or equal to the baseline mean plus three standard deviations; Within a sliding time window, if the same system or frequency point satisfies the condition that the data availability rate is greater than or equal to the first availability rate threshold, and the positioning deviation magnitude is greater than or equal to the first deviation magnitude threshold in multiple consecutive epochs, or the differential deviation relative to other frequency point combinations exceeds the historical mean plus three times the standard deviation, and the residual test and the multiple solution consistency test fail, it is judged as generative deceptive interference.

4. The multi-system fusion positioning method for GNSS adaptive screening under interference environment according to claim 1, characterized in that, In the adaptive observation filtering strategy: For scenarios dominated by suppression-type interference, priority should be given to eliminating satellite links with an overall quality score below the third threshold, an observation missing ratio exceeding the fourth threshold, a consecutive missing length exceeding the fifth threshold, or those that are only available on a single frequency. For scenarios dominated by deceptive interference, observations that exceed the residual limit after solution or are inconsistent with the solution results of independent subsets should be removed first. Additionally, the system and frequency combination that can maintain continuous carrier phase tracking and pseudorange accuracy better than the meter level in this interference event is retained as a priori preference to participate in weight fine-tuning.

5. The multi-system fusion positioning method for GNSS adaptive screening under interference environment according to claim 1, characterized in that, In the dynamic weight allocation algorithm, the weights of pseudorange observations are related to the power function of the overall quality score, the power of the sine function of the satellite elevation angle, and the prior variance of pseudorange measurement noise; the weights of phase observations are related to the power function of the overall quality score, the power of the sine function of the satellite elevation angle, the cycle slip risk suppression factor, and the prior variance of phase measurement noise. The cycle slip risk suppression factor is negatively correlated with the cycle slip risk probability.

6. The multi-system fusion positioning method for GNSS adaptive screening under interference environment according to claim 1, characterized in that, The verification of the positioning results includes one or more of the following methods: residual analysis, accuracy factor evaluation, positioning consistency test, and multiple solution consistency test. The consistency test of multiple solutions includes: comparing the full observation solution with multiple candidate location estimates obtained after removing high-risk subsets, and calculating the sum of the squared distances between each candidate location estimate and the average location estimate, divided by the reference variance, as the consistency score.

7. The multi-system fusion positioning method for GNSS adaptive screening under interference environment according to claim 1, characterized in that, The dynamic strategy adjustment includes one or more of the following: Adjust the positioning strategy, including changing the number of weighted least squares iterations, robust estimation function, or Kalman filter update strategy; Adjust the interference detection threshold, including correcting the elevation angle-related parameters or individual thresholds in the interference threshold model; Adjusting the weighting coefficients includes correcting the mapping coefficients of the comprehensive quality score in the dynamic weighting allocation algorithm or the prior noise ratio of each satellite navigation system.

8. A multi-system fusion positioning system with GNSS adaptive selection under interference environment, characterized in that, include: The quality assessment module is used to perform interference detection and quality assessment on the raw observation data within the current epoch and sliding time window, and generate a comprehensive quality score. The interference identification module is used to identify and classify interference sources based on the comprehensive quality score and the time series characteristics of the observations, at least distinguishing between signal suppression interference and generative deception interference, and identifying GNSS systems, frequencies and observation types whose interference level exceeds a preset threshold. The filtering module is used to execute an adaptive observation filtering strategy based on the results of interference source identification and classification and the comprehensive quality score, to determine the observations corresponding to the systems, satellites, frequency points and pseudorange / carrier combinations participating in the fusion positioning; The fusion positioning module is used to perform multi-system, multi-frequency fusion positioning on the filtered observations to obtain the positioning results and covariance information of the current epoch. The verification feedback module is used to output the positioning result when the positioning accuracy of the positioning result meets the standard, and to perform dynamic strategy adjustment when the accuracy does not meet the standard, and to trigger the filtering module and the fusion positioning module to recalculate.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the multi-system fusion positioning method for GNSS adaptive screening under interference environment as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-system fusion positioning method for GNSS adaptive screening under interference environment as described in any one of claims 1 to 7.

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