A beidou and ins fused real-time high-precision time service method

Abstract

The application relates to the field of satellite navigation and timing technology, in particular to a Beidou and INS (inertial navigation system) fused real-time high-precision timing method, which comprises the following steps: generating pseudo-range residuals and code-minus-carrier based on Beidou satellite signals, and generating satellite channel fuzzy vectors by applying fuzzy reasoning; performing spatial clustering based on the multi-dimensional features of satellite signals, and configuring parallel sub-filters for satellite clusters; the sub-filters utilize satellite channel fuzzy vectors and carrier dynamic information to output error correction amounts and uncertainty variances; the satellite observations are corrected by using the error correction amounts, and a composite noise covariance matrix is constructed based on the uncertainty variances; the corrected observations and the composite noise covariance matrix are input into a navigation solver to extract high-precision time information. The application significantly improves the positioning and timing precision of a radio navigation system in a complex dynamic environment by hierarchically and cooperatively estimating and compensating the propagation errors of satellite signals.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of satellite navigation and timing technology, in particular to a Beidou and INS integrated real-time high-precision timing method. BACKGROUND

[0002] In radio navigation and high-precision timing applications, such as automatic driving, unmanned aerial vehicle mapping, financial transaction time synchronization and power grid phase measurement, the accuracy and reliability of the time reference and spatial positioning are extremely demanding. The radio signal time and spatial position information obtained based on GNSS (especially Beidou satellite navigation system, BDS) makes it possible to construct a radio positioning and timing space-time reference. However, because BDS works in electromagnetic field environments with strong attenuation characteristics such as urban canyons, under trees or tunnel entrances, it is easily affected by multipath effects, non-line-of-sight (NLOS) transceivers and channel blockage, resulting in serious positioning and timing accuracy degradation or even failure, greatly limiting the application value of BDS in complex dynamic scenarios.

[0003] Although the inertial measurement unit (IMU) can effectively output the information of the carrier motion state, the existing BDS radio navigation related fusion algorithm still has core challenges in solving problems such as analysis and modeling of complex time-varying observation noise characteristics, especially for multipath error elimination. In complex dynamic scenarios, the change of the carrier motion state is often very dramatic, so that using a fixed noise model to characterize the statistical distribution characteristics of the actual radio observation error will cause a large estimation bias, thus greatly reducing the filter tracking effect and even causing divergence, which greatly weakens the reliability of the radio measurement results and directly affects the final timing accuracy.

[0004] Due to the fact that the existing radio navigation filter model has a too simple and static statistical assumption for observation noise, it is difficult to effectively identify, model and suppress non-Gaussian, time-varying noise such as space-time related multipath error in dynamic and complex environments, which further causes the final radio timing result to fail to meet the high-standard application requirements in terms of accuracy, continuity and reliability. Therefore, the present application proposes a Beidou and INS integrated real-time high-precision timing method to solve the above technical problems. SUMMARY

[0005] The purpose of the present application is to provide a Beidou and INS integrated real-time high-precision timing method to solve the problems raised in the background art.

[0006] To achieve the above purpose, the present application provides the following technical solution: a Beidou and INS integrated real-time high-precision timing method, comprising:

[0007] The Beidou observation data, IMU sensor data and real-time carrier state at the last time are acquired, and a carrier state prediction value is calculated; pseudo-range residuals and code-minus-carrier are generated based on the Beidou observation data and the carrier state prediction value; and

[0008] Based on the pseudo-range residuals and code-minus-carrier, a satellite channel fuzzy vector is generated by using fuzzy inference rules;

[0009] Based on the Beidou observation data, positioning satellites are clustered to generate a satellite cluster; the satellite channel fuzzy vector and the carrier state prediction value are combined, and a parallel sub-filter is used to cooperatively correct each satellite in the cluster to output a correction data set; the correction data set includes an error correction amount and an uncertainty variance;

[0010] Based on the Beidou observation data and the correction data set, a corrected observation value and a composite noise covariance matrix are generated by using a confidence coupling method; the carrier state prediction value, the corrected observation value and the composite noise covariance matrix are input into a navigation solver to generate a real-time carrier state, and time data is extracted.

[0011] Preferably, the Beidou observation data, IMU sensor data and real-time carrier state at the last time are acquired, and a carrier state prediction value is calculated; pseudo-range residuals and code-minus-carrier are generated based on the Beidou observation data and the carrier state prediction value, and the specific implementation process includes:

[0012] The Beidou observation data includes measured pseudo-range, carrier phase and signal-to-noise ratio of Beidou satellites; the IMU sensor data is inertial measurement value, specifically including angular increment output by a three-axis gyroscope fixedly connected to the carrier and velocity increment output by a three-axis accelerometer;

[0013] The IMU sensor error estimate and real-time carrier attitude information are extracted from the real-time carrier state at the last time, the angular increment and the velocity increment are compensated and calculated by using the IMU sensor error estimate to generate inertial measurement information; and the real-time carrier attitude information and the inertial measurement information are used to generate a carrier state prediction value by using a strapdown inertial navigation update algorithm;

[0014] Theoretical pseudo-range is calculated according to the carrier state prediction value and satellite position, and then the pseudo-range residuals are generated by subtracting the measured pseudo-range; and the code-minus-carrier is generated according to the measured pseudo-range and carrier phase observation.

[0015] Preferably, based on the pseudo-range residuals and code-minus-carrier, a satellite channel fuzzy vector is generated by using fuzzy inference rules, and the specific implementation process includes:

[0016] The pseudo-range residual and code-minus-carrier are mapped into fuzzy membership degrees using a dynamic membership function based on the change of carrier state; the fuzzy membership degrees are input into a fuzzy inference rule base, fuzzy inference rules in the form of IF-THEN are applied, and a satellite channel fuzzy vector is quantitatively generated by synthesizing all activated fuzzy inference rules.

[0017] Preferably, the dynamic membership function based on the change of carrier state is specifically:

[0018] The pseudo-range residual is fuzzily processed to establish a triangular membership function with a zero-value central peak, and the position of a boundary point of the triangular membership function is dynamically adjusted according to a carrier velocity in the inertial measurement value; when the carrier velocity increases, the distance between the boundary point and the central peak is reduced.

[0019] The code-minus-carrier is fuzzily processed to establish a trapezoidal membership function covering a zero-value region, the trapezoidal membership function including a completely membership platform region and an outwardly extending transition slope region; the position of an outer boundary point of the platform region is dynamically adjusted according to a carrier angular velocity and acceleration in the inertial measurement value; when the modulus of the carrier angular velocity and acceleration increases, the outer boundary point is moved towards the zero-value direction.

[0020] Preferably, the positioning satellites are clustered based on the Beidou observation data to generate satellite clusters, and the specific implementation process includes:

[0021] The azimuth and elevation angles of each satellite at the current time are calculated according to satellite ephemeris and carrier state prediction values; a multi-dimensional feature time series vector is constructed for each satellite in combination with the azimuth, elevation angle, signal-to-noise ratio and pseudo-range residual of each satellite in the past sampling period; and the satellites are clustered to form satellite clusters by using a spatial clustering algorithm according to the multi-dimensional feature time series vector.

[0022] Preferably, the satellites in each cluster are collaboratively corrected using parallel sub-filters in combination with the satellite channel fuzzy vector and carrier state prediction values, and a corrected data set is output, and the specific implementation process includes:

[0023] A parallel sub-filter based on a Gaussian-Markov model is set for each cluster of satellites, the Gaussian-Markov model parameters in the parallel sub-filter are dynamically adjusted based on the satellite channel fuzzy vector and carrier state prediction values; the collaborative correction of satellites in the same cluster is realized by setting a same-cluster covariance correlation term in the noise covariance matrix of the parallel sub-filter; and the multipath error correction amount and uncertainty variance are obtained through the parallel sub-filter to constitute a corrected data set.

[0024] Preferably, the Gaussian-Markov model parameters in the parallel sub-filter are dynamically adjusted based on the satellite channel fuzzy vector and carrier state prediction values, and the specific process is as follows:

[0025] Extracting speed information from the carrier state prediction value, constructing a related time parameter representing the change speed of the multipath error, analyzing the satellite channel ambiguity vector to obtain the confidence of different quality levels of the satellite channel, and combining the driving noise variance to generate a weighted average of the basic variance parameter; the related time parameter and the basic variance parameter are used as core parameters of the Gaussian-Markov model.

[0026] Preferably, based on the Beidou observation data and the correction data set, a confidence coupling method is used to generate corrected observation values and a composite noise covariance matrix, and the specific implementation process includes:

[0027] Based on the Beidou observation value, subtract the multipath error correction amount to obtain the corrected observation value;

[0028] According to the carrier state prediction value, a confidence penalty factor is generated to reflect the change amplitude of the satellite signal measurement environment; the inherent noise variance of the original Beidou observation value is calculated, and the confidence penalty factor is coupled with the uncertainty variance in the correction data set to generate a composite noise covariance matrix.

[0029] Preferably, the carrier state prediction value, the corrected observation value and the composite noise covariance matrix are input into a navigation solver to generate real-time carrier state and extract time data, and the specific implementation process includes:

[0030] Based on the corrected observation value, the carrier state prediction value and the composite noise covariance matrix, the navigation solver is used to update and correct the carrier state prediction value to generate real-time carrier state; the receiver clock error and clock drift are extracted from the real-time carrier state to form time data.

[0031] Compared with the prior art, the beneficial effects of the present application are:

[0032] 1. The intelligent and situational perception of the satellite channel quality from hard decision to soft quantization is realized. The fuzzy inference system based on pseudo-range residual and code-minus-carrier is creatively introduced, and the membership function thereof is linked with the real-time dynamics (speed, angular velocity and acceleration) of the carrier. This makes the evaluation of the channel quality no longer a simple binary theory, but a fuzzy vector containing multiple levels of confidence (good, medium and poor), which realizes the "soft quantization" of the signal quality which is more fine and closer to the human expert experience. Especially in high-speed maneuvering and other harsh dynamics, the system can automatically tighten the evaluation standard, significantly improving the accuracy and robustness of error source identification in complex environments.

[0033] 2. A new architecture of hierarchical and parallel error processing from independent correction to collaborative suppression is constructed. A satellite spatial clustering algorithm based on multi-dimensional feature time series vector is introduced to identify satellite clusters with similar error characteristics. Then, a dedicated parallel sub-filter is configured for each cluster, and the collaborative estimation of multi-path errors in the cluster is realized by setting non-diagonal correlation items in the process noise covariance matrix. This hierarchical and collaborative architecture can effectively utilize the correlation information among satellites for complementary correction, thereby accurately suppressing the strong correlation errors that are difficult to handle by a single satellite, and greatly improving the modeling accuracy and suppression effect of the multi-path error model.

[0034] 3. A confidence coupling method is proposed to construct a composite noise covariance matrix. Three dimensions of information are fused: first, the inherent noise variance calculated according to the signal quality; second, the uncertainty variance of the error correction output by the parallel sub-filter; and third, the confidence penalty factor generated based on the degree of carrier motion. This method ensures that the random model input to the main navigation solver can more truly reflect the confidence of each observation information at the current time, significantly improves the stability of the entire fusion system in complex dynamic environments and the reliability of the final timing result, and effectively avoids filter divergence. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 A Beidou, INS integrated real-time high-precision timing method is provided for the embodiments of the present application.

[0036] Figure 2 A parallel sub-filter work flow chart is provided for the embodiments of the present application.

[0037] Figure 3 A navigation solver work flow chart is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0039] The present application provides a Beidou, INS integrated real-time high-precision timing method, and the technical solutions are as follows:

[0040] Embodiment one

[0041] This embodiment provides a specific application of a real-time, high-precision timing method that integrates Beidou and INS. A typical application scenario involves a test vehicle equipped with an autonomous driving system, operating in a densely populated "urban canyon" environment. This requires providing uninterrupted, nanosecond-level timing services for the vehicle's V2X communication module and high-precision map matching module.

[0042] The test vehicle is equipped with a multi-frequency, multi-system GNSS receiver and an automotive-grade inertial measurement unit (IMU).

[0043] Reference Figure 1 , the method comprising:

[0044] S1. Obtain Beidou observation data, IMU sensor data, and the real-time carrier state at the previous moment, and calculate the carrier state prediction value; generate pseudorange residuals and code subtraction carriers based on the Beidou observation data and the carrier state prediction value;

[0045] S2. Based on the pseudorange residual and the code-subtracted carrier, a fuzzy inference rule is used to generate a satellite channel ambiguity vector;

[0046] S3. Cluster positioning satellites based on Beidou observation data to generate satellite clusters;

[0047] S4. Combining the satellite channel ambiguity vector and the carrier state prediction value, using parallel sub-filters to perform collaborative correction on each satellite in the cluster, and outputting a correction data set; the correction data set includes an error correction amount and an uncertainty variance;

[0048] S5. Based on the Beidou observation data and the corrected data set, a confidence coupling method is used to generate corrected observation values ​​and a composite noise covariance matrix; the carrier state prediction value, the corrected observation value and the composite noise covariance matrix are input into the navigation solver to generate real-time carrier state and extract time data.

[0049] Furthermore, Beidou observation data, IMU sensor data, and the real-time carrier state at the previous moment are obtained, and a carrier state prediction value is calculated; pseudorange residuals and code-subtracted carriers are generated based on the Beidou observation data and the carrier state prediction value. Corresponding to the above step S1, the specific process includes:

[0050] In a specific processing cycle, this example adopts an update frequency of 10 Hz, that is, a period of 0.1 seconds. The system first performs data acquisition and state prediction.

[0051] Obtain BeiDou observation data for the current epoch from the GNSS receiver, including the measured pseudoranges, carrier phases, and signal-to-noise ratios of all visible satellites The angular increment output by the three-axis gyroscope and the velocity increment output by the three-axis accelerometer fixed to the carrier in the past 0.1 second are obtained from the IMU.

[0052] From the real-time carrier state of the last time (t-1), the estimated IMU sensor error, i.e. the zero offset of the gyroscope and the accelerometer, is extracted to compensate the current obtained angular increment and velocity increment, and more accurate inertial measurement information is obtained.

[0053] According to the real-time carrier attitude information at t-1 and the compensated inertial measurement information, the quaternion-based strapdown inertial navigation update algorithm is used to integrate and calculate the carrier state prediction value at the current time (t), which contains the predicted position, velocity and attitude of the carrier.

[0054] According to the carrier state prediction value and the satellite position calculated from the ephemeris, the theoretical pseudo-range is calculated, and the pseudo-range residual is generated by subtracting the measured pseudo-range. According to the measured pseudo-range and carrier phase observation, code-minus-carrier (CMC) data is generated.

[0055] By using high-frequency IMU data for strapdown inertial navigation update, a high-frequency continuous dynamic reference and prediction information is provided for subsequent observation correction and filtering.

[0056] Step S1 provides a more accurate, less drift and higher frequency dynamic state prediction reference for subsequent GNSS signal processing and data fusion by closed-loop compensation of IMU data and accurate strapdown inertial navigation calculation, which is the key basis for ensuring high-precision, high-reliability and high-continuity timing of the entire method.

[0057] Further, based on the pseudo-range residual and code-minus-carrier, a satellite channel fuzzy vector is generated using fuzzy inference rules, corresponding to the above S2 step, and the specific implementation process includes:

[0058] A dynamic membership function is established, and after the pseudo-range residual and code-minus-carrier are fuzzed by the dynamic membership function, a fuzzy feature quantity is generated; the feature quantity is input to the fuzzy inference rule base in the form of IF-THEN.

[0059] Specifically, the fuzzy inference rule base of the embodiment is:

[0060] The fuzzy set of channel quality is defined as (poor, medium, good).

[0061] Rule one: IF (pseudo-range residual is small) AND (code-minus-carrier is stable) THEN (channel quality is good);

[0062] Rule two: IF (pseudo-range residual is large) AND (code-minus-carrier is slow drift) THEN (channel quality is poor);

[0063] ​Rule Three: IF (Pseudo-range residual is small) AND (Code-minus-carrier is jittery) THEN (Channel quality is poor).

[0064] Rule Four: IF (Pseudo-range residual is small) AND (Code-minus-carrier is jittery) THEN (Channel quality is fair).

[0065] Rule Five: IF (Pseudo-range residual is large) AND (Code-minus-carrier is stable) THEN (Channel quality is fair).

[0066] By integrating all the activated rules, a satellite channel ambiguity vector is finally generated for each satellite, where a satellite channel ambiguity vector is [excellent: 0.1, fair: 0.8, poor: 0.1].

[0067] The fuzzy inference rules are introduced to evaluate the satellite channel quality. Compared with the traditional hard threshold decision method based on signal-to-noise ratio and pseudo-range residual, fuzzy inference is a soft decision, which can quantize signal quality more finely, more intelligently, and more close to human expert experience, avoiding misjudgment and omission caused by improper threshold setting.

[0068] Specifically, the dynamic membership function is specifically:

[0069] The fuzzy set of pseudo-range residual is defined as (small, medium, large).

[0070] A triangular membership function with a zero-value central peak and a linear decline to both sides is established to describe the pseudo-range residual. The triangle is defined by three points: the left boundary point a, the central peak point b = 0, and the right boundary point c. The positions of boundary points a and c are dynamically adjusted in real time according to the carrier speed v extracted from the carrier state prediction value.

[0071] ,

[0072] ,

[0073] wherein, is a basic width constant, which is set to 2.0 in this example; is a speed influence coefficient, which is set to 0.9 in this example. When the carrier speed increases, the boundary points c and a will shrink towards the central zero point, making the entire triangular membership function narrower and more acute.

[0074] The fuzzy set of code-minus-carrier is defined as (jittery, stable, slow drift).

[0075] A trapezoidal membership function is established to describe the code minus the carrier, which is defined by four points: the left outer boundary point a, the left inner platform point b, the right inner platform point c, and the right outer boundary point d. The interval from b to c is completely subordinate to the stable platform area. The platform area boundary points b and c of the function are dynamically adjusted in real time according to the degree of severity of the carrier motion state extracted from the carrier state prediction value. The degree of severity can be jointly represented by the modulus of the acceleration and the angular velocity .

[0076]

[0077] wherein, is the basic platform width, which is set to 0.5 in this example; and are the corresponding dynamic influence coefficients, which are set to 1.0 and 0.8, respectively, in this example. When the modulus of the carrier angular velocity and acceleration increases, the platform area boundary points b and c will shrink towards the center zero point, making the stable identification range narrower.

[0078] The dynamic adaptability of the membership function of fuzzy reasoning is achieved by linking the evaluation of signal quality with the real-time motion state (speed, angular velocity, acceleration) of the carrier, so that the system can automatically tighten the judgment standard under harsh dynamics such as high speed and maneuvering, and improve the sensitivity and adaptability of the model to changes in the real physical environment.

[0079] Based on the Beidou observation data, the positioning satellites are clustered to generate satellite clusters, corresponding to the above S3 step, the specific implementation process is as follows:

[0080] For each satellite, the azimuth angle, elevation angle, signal-to-noise ratio and pseudo-range residual within the past 1 second, i.e. 10 sampling periods, are collected to form a multi-dimensional feature time series vector.

[0081] The dynamic time warping (DTW) algorithm is used to calculate the similarity distance between any two multi-dimensional feature time series vectors to identify satellites with similar signal behavior patterns.

[0082] Feature analysis sequence of satellite A in the past 1 second ; feature analysis sequence of satellite B in the past 1 second . A 10x10 cost matrix D is created, where each element in the matrix represents the distance between the feature processing at the i-th moment in sequence and the feature processing at the j-th moment in sequence .

[0083] Create a 10x10 computational cost matrix Y, each element in the matrix The calculation follows a gradient rule: it is equal to the local cost of the current point , plus the minimum cumulative cost of its three adjacent predecessor nodes to the left, bottom, and left bottom. Start by gradually filling up the entire cost matrix. When the total cost matrix Y is calculated, the element in the upper right corner is The value of is the sequence and The final DTW similarity distance between them.

[0084] The above DTW calculation process is repeated for all visible satellite pairs to generate a distance matrix containing all satellite distances between each two DTW.

[0085] This paper constructs a multidimensional feature time series vector for each satellite and creatively incorporates the DTW algorithm. This allows similarity determination to move beyond the numerical value at a single point in time and instead focuses on the evolutionary trends of signal behavior patterns over time. This process-oriented analysis can more deeply and comprehensively reveal the inherent correlations of satellite signals.

[0086] The distance matrix calculated by DTW is then input into the density-based spatial clustering algorithm (DBSCAN) for clustering. In this embodiment, the parameters of DBSCAN are set to the neighborhood radius. is 0.4, the minimum number of neighbors of the core point is 2.

[0087] A satellite clustering method based on multidimensional feature time series vectors was proposed, enabling the system to identify and group satellites with similar error characteristics, especially those affected by the same local environment. This laid the foundation for subsequent targeted and efficient collaborative corrections.

[0088] Reference Figure 2 , combining the satellite channel ambiguity vector and the carrier state prediction value, using parallel sub-filters to perform collaborative correction on each satellite in the cluster, and outputting a corrected data set, corresponding to the above step S4, the specific implementation process is:

[0089] For each satellite cluster generated in the previous step, configure a parallel sub-filter based on the Gauss-Markov model.

[0090] Extract the velocity modulus from the carrier state prediction value and the angular velocity vector , combined into a comprehensive dynamic factor through coupling functions.

[0091]

[0092] in, and are weight coefficients, which are set to 0.5 and 2.0 in this example. is the equivalent radius, which is set to 1.5 in this example, for converting the impact of angular velocity into equivalent linear velocity change, so as to unify the dimension.

[0093] Substitute this comprehensive dynamic factor D into the inverse model

[0094]

[0095] where C is the basic correlation time constant, which is set to 20 in this example.

[0096] The correlation time parameter τ obtained is used to characterize the speed of change of the multipath error.

[0097] The satellite channel ambiguity vector is analyzed, and in this example, the satellite channel ambiguity vector of one of the satellites is [excellent: 0.1, medium: 0.8, poor: 0.1], combined with the inherent noise variance of each quality level, which is set to (excellent = 0.01, medium = 0.2, poor = 1.0) in this example, the weighted average noise variance 0.261 is obtained by weighted average.

[0098] The satellite ambiguity entropy is calculated,

[0099] ,

[0100] Since the classification is relatively certain, the entropy value is relatively low, which is 0.922 in this example.

[0101] The risk penalty factor is set for the ambiguity entropy , and the final basic variance parameter

[0102]

[0103] The multipath error is not constant, and its change speed is closely related to the dynamic environment of the carrier. By designing the correlation time as the inverse function of the comprehensive dynamic factor, the parallel sub-filter can more effectively track and suppress the time-varying multipath error in the dynamic scene.

[0104] The ambiguity entropy is introduced to quantify the uncertainty of the expected value. It can effectively prevent the filter from becoming overconfident when receiving ambiguous quality evaluation information, thereby avoiding the decline or even divergence of the filtering performance due to the wrong model parameters, and improving the stability and reliability of the entire fusion system.

[0105] The process noise covariance matrix is constructed, and in this embodiment, a satellite cluster containing three satellites A, B, and C is used.

[0106] ​The state vector in the parallel sub-filter is

[0107]

[0108] wherein, denotes the multipath error of satellite A, denotes the multipath error of satellite B, denotes the multipath error of satellite C.

[0109] The 3x3 size process noise covariance matrix corresponding to it describes the variance and covariance of the error change amount at each time step. The diagonal elements represent the process noise variance of the multipath error of each satellite. The non-diagonal elements represent the covariance between the process noise of two different satellites, indicating that the random error change of satellite A is statistically related to the random error change of satellite B.

[0110] ,

[0111] wherein, the cross-correlation coefficient is set to 0.7 in this embodiment; denotes the process noise variance of the multipath error of the satellite.

[0112] The final process noise covariance matrix in this embodiment is

[0113]

[0114] According to the process noise covariance matrix, the iterative update of the parallel sub-filter is realized to achieve the cooperative correction of the satellites in the satellite cluster, and finally the correction data set containing the multipath error correction amount and the uncertainty variance is output.

[0115] The final error correction amount in this embodiment is , and the uncertainty variance is .

[0116] The parallel sub-filter and the in-cluster cooperative correction mechanism are designed. This architecture not only reduces the computational burden of the main filter (divides the complex observation error processing), but more importantly, by establishing the covariance correlation term in the cluster, it realizes the sharing and complementation of information between the satellites in the cluster, can effectively correct the strongly correlated errors that a single satellite cannot handle, and improves the overall error suppression effect.

[0117] The parameter of the multipath model in the parallel sub-filter is dynamically changed. By combining the carrier velocity and the channel quality evaluation result of fuzzy reasoning, the core parameter of the Gaussian-Markov model is dynamically adjusted, so that the multipath error model is no longer static and empirical, but a highly situational model that reflects the current dynamics and channel environment in real time, thereby greatly improving the accuracy of multipath error estimation.

[0118] With reference to Figure 3 , based on the Beidou observation data and the correction data set, the corrected observation value and the composite noise covariance matrix are generated by using the confidence coupling method, and the specific implementation process corresponding to the above S5 step is as follows:

[0119] The original pseudo-range from the Beidou observation data is obtained, and the multipath error correction provided by the parallel sub-filter is subtracted to obtain the corrected observation value.

[0120] According to the acceleration and angular velocity modulus in the carrier state prediction value, the penalty factor function is:

[0121]

[0122] wherein, and are preset weight coefficients for adjusting the contribution ratio of acceleration and angular velocity, and in this example, , ; is a hyperbolic tangent function; and are threshold values, i.e. saturation adjustment parameters, and in this example, , .

[0123] This factor reflects the degree of deterioration of the overall measurement environment caused by the carrier's violent movement, and is a penalty applied uniformly to all satellite observations.

[0124] The physical motion state of the carrier is directly linked to the reliability evaluation of the satellite observation data, which can reflect the deterioration of the measurement environment caused by the carrier's violent maneuver in real time and quantitatively, so that the random model in the navigation solver can more truly reflect the instantaneous changes of the physical world.

[0125] The composite noise variance is generated by coupling the inherent noise variance and the uncertainty variance based on the confidence penalty factor

[0126]

[0127] wherein, is the composite noise variance of satellite C20; is the inherent noise variance; is the uncertainty variance; a confidence penalty factor.

[0128] This operation is performed for all satellites, and a composite noise covariance matrix of the navigation solver is constructed, the diagonal elements of which are the composite noise variances of the satellites.

[0129] The present application proposes a confidence coupling method to generate the final composite noise covariance matrix. This method ingeniously integrates the original observation noise, the corrected residual uncertainty and the penalty factor based on the carrier dynamics, realizing the fine, intelligent and robust allocation of the most optimal weight of the BDS observation information. It ensures that the navigation solver can obtain the random model most consistent with the current real situation in any case, which is a key link to prevent filter divergence and ensure the final stability of the system.

[0130] The carrier state prediction value, the corrected observation value and the composite noise covariance matrix are input into the navigation solver to generate real-time carrier state, and the time data is extracted, corresponding to the above S5 step, and the specific implementation process is as follows:

[0131] The navigation solver is constructed based on an extended Kalman filter, and the state vector contains position error, velocity error, attitude error, IMU sensor error and receiver clock error and clock drift;

[0132] The corrected observation value and the carrier state prediction value are subtracted to generate an innovation vector; and the carrier state prediction value and the composite noise covariance matrix are calculated to generate a Kalman gain;

[0133] According to the innovation vector and the Kalman gain, the carrier state prediction value is updated by using the master navigation filter based on the extended Kalman filter to generate real-time carrier state; and the receiver clock error and clock drift in the real-time carrier state are extracted to jointly constitute time data.

[0134] Step S5 clearly defines the generation path of the final high-precision time data, ensuring that all complex corrections and optimization processing in the front end of the entire system can be seamlessly applied to the navigation solver, and the core time synchronization information, i.e. clock error and clock drift, can be accurately and reliably extracted from the optimal state estimation, completing the closed loop of the entire method.

[0135] The present application proposes a new systematic Beidou / INS integrated time service framework. The framework fundamentally improves the perception, modeling and suppression ability of satellite observation errors in complex environments by introducing fuzzy reasoning, satellite clustering, parallel collaborative correction and confidence coupling, thereby significantly improving the accuracy, continuity and robustness of real-time high-precision time service.

[0136] Embodiment two

[0137] The second embodiment of the present invention specifically involves obtaining Beidou observation data, IMU sensor data and the real-time carrier status at the previous moment, and calculating the carrier status prediction value; and generating the pseudorange residual and code subtraction carrier based on the Beidou observation data and the carrier status prediction value.

[0138] The specific steps of this embodiment are as follows: At this moment, execute as follows:

[0139] The system controller obtains the data required for the current solution cycle from the vehicle sensor network and memory. A set of observation data is collected at all times. Taking the visible Beidou C20 satellite as an example, due to the reflection of the high-rise building on the side of the vehicle, the satellite signal is subject to obvious multipath interference. Its observation data is:

[0140] ;

[0141] ;

[0142] .

[0143] The IMU original incremental data is:

[0144] ;

[0145] .

[0146] Retrieved from the navigation computer The real-time carrier state vector at time t is the optimal estimation result of the fusion filter in the previous cycle. IMU sensor error estimation at time:

[0147] ;

[0148] ;

[0149] .

[0150] use The IMU error estimated at each moment is used to compensate the newly acquired raw incremental data online to generate the corrected inertial measurement information.

[0151] ;

[0152] .

[0153] by The carrier state at the moment is taken as a starting point, the compensated inertial measurement information is applied, the mechanics programming equation of the strapdown inertial navigation is executed, and the carrier state prediction value at the moment is recursively obtained The carrier state prediction value at the moment.

[0154] By using The vehicle prediction position at the moment and the space position of the satellite No. 20 calculated from the ephemeris are used to calculate the theoretical geometric distance between the two. The Saastamoinen model and the ephemeris parameters are used to calculate and compensate the tropospheric delay, the ionospheric delay and the satellite clock error. The theoretical pseudo-range .

[0155] The measured pseudo-range is subtracted from the theoretical pseudo-range to obtain the pseudo-range residual reflecting the current observation quality, , which reveals that the satellite signal No. 20 is suffering from serious multipath pollution at the moment.

[0156] The code-minus-carrier (CMC) calculation formula is:

[0157] ,

[0158] Wherein λ is the wavelength of the satellite signal No. 20, which is 0.1903 meters.

[0159] The absolute value of the CMC is a key basis for judging the strength and dynamic characteristics of the multipath effect.

[0160] Embodiment Three

[0161] In order to make the purpose, technical scheme and advantages of the present application more clear, the technical scheme of the present application will be further described in combination with specific embodiments. The present embodiment specifically relates to a technical scheme for generating a satellite channel fuzzy vector by using fuzzy inference rules based on the pseudo-range residual and the code-minus-carrier, and the present embodiment continues the application scenario of the embodiment two.

[0162] The pseudo-range residual

[0163] The code-minus-carrier change amount

[0164] The dynamic information extracted from the carrier state prediction value: the vehicle speed module

[0165] The vehicle angular velocity module

[0166] The vehicle acceleration module

[0167] The system fuzzifies the pseudorange residuals and code-subtraction carrier variations and maps them to memberships of different fuzzy sets. Three fuzzy sets are defined for the pseudorange residuals: negative large (NB), zero (ZO), and positive large (PB). The membership function of the zero (ZO) set adopts a triangular membership function with a peak centered at zero. The boundary points of the triangular membership function are The position of the vehicle is determined by the vehicle speed. Dynamic adjustment. The adjustment rule is to reduce the distance between the boundary point and the central peak when the carrier speed increases, that is, to tighten the interval determined as zero and improve the strictness of the judgment. The dynamic adjustment formula is designed as follows:

[0168] ,

[0169] ,

[0170] in, is the basic boundary value in the stable state, which is set to rice. is the speed influence coefficient, which is set as =0.1.

[0171] According to the current speed , calculate the dynamic boundary point to be 1.2 meters

[0172] This means that at the current At a speed of When the probability is zero, it is considered to be zero.

[0173] The input pseudorange residual =−4.58 m, substituted into the membership function after dynamic adjustment.

[0174] is the code-to-carrier variation Two fuzzy sets are defined: stable and fluctuating. The stable set uses a trapezoidal membership function that covers the zero value region.

[0175] The outer boundary points of the complete membership platform area of ​​the trapezoidal membership function are based on the angular velocity of the vehicle. and acceleration Dynamic adjustment is performed. The rule is: when the angular velocity and acceleration modulus increase, the outer boundary point is moved toward the zero value direction, that is, the stable platform area is tightened. The specific dynamic adjustment formula can be designed as:

[0176]

[0177] in, is the base boundary value in static, in this case meters. and are the influence coefficients of angular velocity and acceleration respectively, in this case they are set as , .

[0178] According to the current vehicle dynamics , , the dynamic platform region boundary is calculated as 0.28 meters.

[0179] The input code is reduced by the carrier variation = 0.15 meters, and the dynamic adjusted membership function is substituted.

[0180] The calculated fuzzy membership is input to the fuzzy inference rule base to generate the final satellite channel fuzzy vector.

[0181] The fuzzy inference rule base is built-in an IF-THEN rule base based on expert knowledge, which is used to evaluate the satellite channel quality. The channel quality can be divided into three levels: Good, Medium, and Poor. The rule base is as follows:

[0182] Rule 1: IF (pseudo-range residual is zero) AND (CMC variation is stable) THEN (channel quality is good);

[0183] Rule 2: IF (pseudo-range residual is zero) AND (CMC variation is fluctuating) THEN (channel quality is medium);

[0184] Rule 3: IF (pseudo-range residual is negative large) AND (CMC variation is stable) THEN (channel quality is poor);

[0185] Rule 4: IF (pseudo-range residual is positive large) AND (CMC variation is stable) THEN (channel quality is poor);

[0186] Rule 5: IF (pseudo-range residual is negative large) AND (CMC variation is fluctuating) THEN (channel quality is poor);

[0187] Rule 6: IF (pseudo-range residual is positive large) AND (CMC variation is fluctuating) THEN (channel quality is poor).

[0188] The membership of C20 satellite is input to the rule base, the pseudo-range residual is significantly negative, and the CMC variation is stable.

[0189] The activation strength of rule 1 is 0, and the contribution to the final conclusion of good is zero.

[0190] The activation strength of Rule 2 is 1. This rule is fully activated and its confidence in concluding that the channel quality is poor is 1.0.

[0191] Combining all activated rules, defuzzification is performed through weighted average method, and finally a vector representing the quality of C20 satellite channel is quantified. Based on the above reasoning, the fuzzy vector of C20 satellite is: =[Confidence (excellent), Confidence (medium), Confidence (poor)] = [0.0, 0.1, 0.9].

[0192] Here, [0.0, 0.1, 0.9] means that there is a 90% confidence that the channel quality of the C20 satellite is poor, a 10% confidence that it is medium, and a 0% confidence that it is excellent.

[0193] Example 4

[0194] This embodiment specifically describes the technical solution process of clustering positioning satellites, using parallel sub-filters to perform collaborative correction on satellites in the cluster, and dynamically adjusting Gauss-Markov model parameters in the parallel sub-filters.

[0195] In complex environments like urban canyons, satellite signals from different directions experience significantly different interference characteristics. Signals from multiple satellites on the same narrow street may be simultaneously reflected by the glass curtain wall of a particular building, causing their multipath errors to exhibit high correlation. Treating all satellite errors as independent events loses this valuable information, affecting the accuracy of error estimates.

[0196] This embodiment continues the scenario of an autonomous vehicle driving in a city CBD. Based on the previous embodiment, the specific process of satellite clustering, dynamic adjustment of filter parameters, and collaborative error correction is described in detail.

[0197] exist At this moment, there are 5 BeiDou satellites visible to the vehicle: C19, C20, C21, C28, and C30. Based on the satellite ephemeris and the vehicle's predicted position, the azimuth angle at the current moment is calculated. and altitude angle , extract the observation data sequence of the past 10 sampling periods to form time series features.

[0198] Specifically, the feature vector constructed for each satellite is The form is

[0199] ;

[0200] in, are the current azimuth and altitude angles, is the average signal-to-noise ratio over the past 10 periods, is the average pseudo-range residual of the past 10 cycles, is the pseudo-range residual standard deviation of the past 10 cycles, reflecting its stability.

[0201] After the system calculation and integration, the characteristic vector data of each satellite is obtained:

[0202] C19: [290°, 25°, 40dB, -2.5m, 1.8m];

[0203] C20: [110°, 35°, 38dB, -4.2m, 2.5m];

[0204] C21: [115°, 38°, 39dB, -3.9m, 2.2m];

[0205] C28: [180°, 75°, 48dB, 0.2m, 0.5m];

[0206] C30: [210°, 70°, 47dB, 0.4m, 0.6m].

[0207] The above five multi-dimensional characteristic vectors are used as input, and DBSCAN spatial clustering algorithm is used for clustering. Based on the above data, the DBSCAN algorithm identifies the following three satellite clusters:

[0208] Satellite cluster A (low altitude, poor quality): {C20, C21}, the two satellites have similar azimuth and elevation angles, and the historical pseudo-range residuals show significant negative offset and large fluctuations, indicating that they may be affected by the same set of reflectors.

[0209] Satellite cluster B (high altitude, high quality): {C28, C30}, the two satellites have high elevation angles and good signal-to-noise ratios, with small and stable pseudo-range residuals.

[0210] Satellite cluster C (isolated point): {C19}, which is significantly different from other satellites and forms a separate category.

[0211] Set an accurate error model for each parallel sub-filter. The present application uses a first-order Gaussian-Markov model to describe the time series characteristics of multipath error , with the core parameters being correlation time and driving noise variance .

[0212] Taking C20 satellite in satellite cluster A as an example:

[0213] From the carrier state prediction value, extract the speed module value and angular velocity vector , and combine them into a comprehensive dynamic factor through a coupling function.

[0214]

[0215] where, and are weight coefficients, which are set to 0.5 and 2.0 in this example. is the equivalent radius, which is set to 1.5 in this example, to convert the angular velocity effect into equivalent linear velocity change, thus unifying the dimension.

[0216] Substitute this comprehensive dynamics factor D into the inverse model

[0217]

[0218] where C is the base correlation time constant, which is set to 20 in this example. The correlation time parameter τ is obtained, which is used to characterize the speed of change of multipath error. In this example, the multipath error correlation time of C20 is 12.5 seconds.

[0219] The ambiguity vector of satellite C20 is , which represents the confidence levels of its channel quality as excellent, medium, and poor, respectively, 0.0, 0.1, and 0.9. The base variance is obtained by weighting and averaging the base driving noise variances corresponding to different quality levels according to the confidence levels in the ambiguity vector. The preset base variances of each level in this example are (excellent = 0.12, medium = 1.02, poor = 3.00), and the weighted average noise variance is calculated to be 2.802.

[0220] Calculate the ambiguity entropy of the satellite:

[0221] ,

[0222] Set the risk penalty factor for the ambiguity entropy , and the final base variance parameter is calculated to be 3.459.

[0223] For each satellite cluster divided, an independent parallel sub-filter is configured. The task of each sub-filter is to estimate the multipath error of all satellites in the cluster.

[0224] In order to realize the cooperative correction of satellites in the same cluster, a cluster covariance related item is set in the process noise covariance matrix of the sub-filter.

[0225] For cluster A, the diagonal elements of satellites C20 and C21 are their respective base variance parameters, while the correlation coefficient establishes the coupling relationship between the error estimates of C20 and C21. When the measurement update of C20 indicates that its multipath error increases, the filter model will synchronize the estimation of the multipath error of C21 through this correlation item.

[0226] Each parallel sub-filter is run. Each filter takes the pseudorange residual for the corresponding satellite as an observation and performs state estimation.

[0227] For C20, the sub-filter of cluster A outputs a multipath error estimate for C20 after filtering.

[0228] Error correction: meters. This is the best estimate of the multipath component in the raw meters pseudorange residual by the filter.

[0229] Uncertainty variance: This is the variance of the error correction estimate itself, representing its reliability.

[0230] Example Five

[0231] Referring to Figure 3 , this example specifically illustrates the technical solution process of generating corrected observations and composite noise covariance matrix, and generating real-time carrier state and extracting time data.

[0232] According to the raw measured pseudorange of C20 satellite = 21877456.22 meters and the error correction of C20 satellite = 0.09 meters, the corrected observation

[0233] Calculate the inherent noise variance of C20 = 0.09 according to the signal-to-noise ratio and the elevation angle, using the standard formula for calculating the inherent noise = 0.09.

[0234] According to the acceleration and angular velocity modulus in the carrier state prediction value, a confidence penalty factor is generated by the penalty factor function:

[0235]

[0236] wherein, and are preset weight coefficients for adjusting the contribution proportion of acceleration and angular velocity, and in this example , ; is a hyperbolic tangent function; and are threshold values, i.e. saturation adjustment parameters, and in this example , .

[0237] This factor reflects the degree of deterioration of the overall measurement environment caused by the violent movement of the carrier, and is a penalty uniformly applied to all satellite observations. The confidence penalty factor

[0238] The composite noise variance of satellite C20 is generated based on the inherent noise variance and the uncertainty variance, coupled with a confidence penalty factor

[0239]

[0240] wherein, is the composite noise variance of satellite C20; is the inherent noise variance; is the uncertainty variance; is the confidence penalty factor.

[0241] This is performed for all satellites, and a composite noise covariance matrix of the navigation solver is constructed, whose diagonal elements are the composite noise variances of the respective satellites.

[0242] The state vector of the navigation solver based on the extended Kalman filter is defined, including position, velocity, attitude error, IMU sensor error and receiver clock error, clock drift. The innovation vector is generated by differencing the corrected observation value and the carrier state prediction value; the Kalman gain is calculated based on the carrier state prediction value and the composite noise covariance matrix.

[0243] According to the innovation vector and the Kalman gain, the carrier state prediction value is corrected and updated using the navigation solver based on the extended Kalman filter, to generate real-time carrier state.

[0244] The navigation solver outputs the updated real-time carrier state, including the optimal estimation vector of the vehicle real-time position, velocity, attitude, IMU error and receiver clock error and clock drift.

[0245] From the state vector, the state quantities related to time service are directly extracted:

[0246] Receiver clock error seconds;

[0247] Receiver clock drift seconds.

[0248] The final output is the real-time time data composed of the receiver clock error and the receiver clock drift.

[0249] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A real-time high-precision timing method integrating BeiDou and INS, characterized in that: include: Obtain BeiDou observation data, IMU sensor data, and the real-time carrier status at the previous moment, and calculate the carrier status prediction value; Generate a pseudorange residual and a code-subtracted carrier based on the BeiDou observation data and the carrier state prediction value; Based on the pseudorange residual and the code-subtracted carrier, a fuzzy inference rule is used to generate a satellite channel ambiguity vector; Cluster positioning satellites based on BeiDou observation data to generate satellite clusters; Combining the satellite channel ambiguity vector and the carrier state prediction value, a parallel sub-filter based on the Gauss-Markov model is set for each cluster of satellites. The parallel sub-filter is used to perform collaborative correction on the satellites in the cluster, and the Gauss-Markov model parameters in the parallel sub-filter are dynamically adjusted. The collaborative correction of satellites in the cluster is achieved by setting the same-cluster covariance correlation term in the process noise covariance matrix of the parallel sub-filter. The corrected data set is output through the parallel sub-filter. The correction data set includes an error correction amount and an uncertainty variance; Based on the BeiDou observation data and the corrected data set, a confidence coupling method is used to generate corrected observation values ​​and a composite noise covariance matrix; The carrier state prediction value, the corrected observation value and the composite noise covariance matrix are input into the navigation solver to generate the real-time carrier state and extract the time data.

2. The real-time high-precision timing method for integrating BeiDou and INS according to claim 1, characterized in that: The method comprises the following steps: obtaining Beidou observation data, IMU sensor data and the real-time carrier state at the previous moment, and calculating a carrier state prediction value; generating a pseudorange residual and a code-subtraction carrier based on the Beidou observation data and the carrier state prediction value, and the specific implementation process comprises: the Beidou observation data comprises the measured pseudorange, carrier phase and signal-to-noise ratio of the Beidou satellite; the IMU sensor data is an inertial measurement value, specifically comprising an angular increment output by a three-axis gyroscope fixed to the carrier and a velocity increment output by a three-axis accelerometer; extracting an IMU sensor error estimate and real-time carrier attitude information from the real-time carrier state at the previous moment, using the IMU sensor error estimate to perform compensation calculation on the angular increment and velocity increment to generate inertial measurement information; generating a carrier state prediction value based on the real-time carrier attitude information and the inertial measurement information using a strapdown inertial navigation update algorithm; calculating a theoretical pseudorange based on the carrier state prediction value and the satellite position, and then performing a subtraction with the measured pseudorange to generate a pseudorange residual; and generating a code-subtraction carrier based on the measured pseudorange and carrier phase observation value.

3. The real-time high-precision timing method for integrating BeiDou and INS according to claim 1, characterized in that: The satellite channel ambiguity vector is generated based on the pseudorange residual and the code-subtracted carrier using fuzzy inference rules. The specific implementation process includes: using a dynamic membership function based on carrier state changes to map the pseudorange residual and the code-subtracted carrier into fuzzy membership; inputting the fuzzy membership into a fuzzy inference rule library, applying fuzzy inference rules in the form of IF-THEN, and then synthesizing all activated fuzzy inference rules to quantitatively generate the satellite channel ambiguity vector.

4. The real-time high-precision timing method for integrating BeiDou and INS according to claim 3 is characterized in that: The dynamic membership function based on carrier state changes is specifically as follows: fuzzifying the pseudorange residual to establish a triangular membership function with a zero value as a central peak, and dynamically adjusting the positions of the boundary points of the triangular membership function according to the carrier velocity in the inertial measurement value; wherein, when the carrier velocity increases, the distance between the boundary point and the central peak is reduced; fuzzifying the code-subtracted carrier to establish a trapezoidal membership function covering the zero value area, wherein the trapezoidal membership function includes a fully affiliated platform area and an outward-extending transition slope area; and dynamically adjusting the position of the outer boundary points of the platform area according to the carrier angular velocity and acceleration in the inertial measurement value; wherein, when the modulus of the carrier angular velocity and acceleration increases, the outer boundary points are moved toward the zero value direction.

5. The real-time high-precision timing method for integrating BeiDou and INS according to claim 1, characterized in that: The method of clustering positioning satellites based on Beidou observation data to generate satellite clusters specifically includes: calculating the azimuth and altitude of each satellite at the current moment based on the satellite ephemeris and carrier state prediction value; constructing a multidimensional feature time series vector for each satellite based on the azimuth, altitude, signal-to-noise ratio and pseudorange residual of each satellite in the past sampling period; and clustering the satellites using a spatial clustering algorithm based on the multidimensional feature time series vector to form satellite clusters.

6. The real-time high-precision timing method for integrating BeiDou and INS according to claim 1, characterized in that: Dynamically adjust the Gauss-Markov model parameters in the parallel sub-filters. The specific process is as follows: extract speed information from the carrier state prediction value to construct a correlation time parameter that characterizes the speed of change of the multipath error; analyze the satellite channel ambiguity vector to obtain the confidence of different quality levels of the satellite channel, and generate a basic variance parameter based on the inherent noise variance; and use the correlation time parameter and the basic variance parameter together as the core parameters of the Gauss-Markov model.

7. The real-time high-precision timing method for integrating BeiDou and INS according to claim 1, characterized in that: Based on the Beidou observation data and the corrected data set, a confidence coupling method is used to generate corrected observation values ​​and a composite noise covariance matrix. The specific implementation process includes: based on the Beidou observation data, subtracting the multipath error correction amount to obtain the corrected observation value; generating a confidence penalty factor based on the carrier state prediction value to reflect the change amplitude of the satellite signal measurement environment; calculating the inherent noise variance of the original Beidou observation value, and coupling it with the uncertainty variance and confidence penalty factor in the corrected data set to generate a composite noise covariance matrix.

8. The real-time high-precision timing method for integrating BeiDou and INS according to claim 1, characterized in that: The carrier state prediction value, the corrected observation value and the composite noise covariance matrix are input into the navigation solver to generate the real-time carrier state and extract the time data. The specific implementation process includes: based on the corrected observation value, the carrier state prediction value and the composite noise covariance matrix, the carrier state prediction value is updated and corrected by the navigation solver to generate the real-time carrier state; and the receiver clock error and clock drift are extracted from the real-time carrier state to form the time data.

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