Multi-type time-frequency comparison data fusion method and system

Abstract

The invention belongs to the technical field of time frequency, and particularly discloses a multi-type time-frequency comparison data fusion method and system, which is characterized in that a plurality of types of time-frequency comparison time difference data measured or simulated between stations are used as observed quantity, the phase deviation and frequency deviation of the fused comparison data are used as parameters to be estimated, and a Kalman filtering algorithm is adopted to obtain a multi-type time-frequency comparison data fusion model. The method comprises the following steps of: performing time updating and measurement updating on a state quantity to be estimated by using available comparison time difference observed quantity data at the current moment so as to estimate and obtain phase deviation and frequency deviation parameters at the current moment, and substituting the phase deviation and frequency deviation parameters into a clock error data formula so as to calculate and obtain a fused inter-station comparison time difference value. The fusion result is a multi-type time-frequency comparison data fusion result at the current moment.

Description

Technical Field

[0001] The present invention belongs to the field of time-frequency technology, and in particular relates to a method and system for fusing multi-type time-frequency comparison data. Background Art

[0002] At present, the commonly used high-precision time and frequency comparison methods mainly include optical fiber, satellite two-way and precise single-point positioning GNSS-PPP time and frequency comparison. The optical fiber time and frequency comparison method uses optical fiber links to transmit optical frequency comb signals to compare time and frequency. It is characterized by high accuracy, good stability, and strong anti-electromagnetic interference ability. However, this method is very dependent on optical fiber infrastructure, and long-distance transmission may cause signal damage and other problems. The satellite two-way time and frequency comparison method forwards timing modulation information through geosynchronous communication satellites. Two ground stations send and receive signals to the satellite at the same time, and use the symmetry of the two-way link to eliminate path delays, thereby accurately comparing time and frequency. This comparison method has high accuracy and can achieve long-distance time and frequency comparison, but the system is relatively complex, requires multi-site collaborative work, and is greatly affected by satellite communication conditions and equipment performance. GNSS-PPP is based on the global satellite navigation system and adopts precision single point (PPP) technology. The server calculates the error and the user obtains the correction to obtain high-precision time and frequency comparison results. This method has high accuracy and is fully autonomous and controllable. However, the accuracy in areas with poor signals will be affected, and precise ephemeris / star clock products are required to solve the time difference between the two places, so the real-time performance is relatively poor.

[0003] For distributed timekeeping nodes, multiple types of comparison methods are usually used between two nodes, including the above-mentioned optical fiber, satellite bidirectional and GNSS-PPP, etc. for time-frequency comparison. If a link is interrupted during the process, it will cause problems such as large deviation in time difference data switching. Therefore, it is urgent to propose a multi-type time-frequency comparison data fusion method to solve the above technical problems. Summary of the invention

[0004] The present invention provides a multi-type time-frequency comparison data fusion method and system, which are used to solve the problem of large deviation in switching of time difference data caused by interruption of any type of link used between distributed time-keeping nodes.

[0005] In a first aspect, a method for fusing multi-type time-frequency comparison data is provided, the method comprising:

[0006] Query the availability of multi-type comparison links between two distributed timekeeping nodes, and only merge the available multi-type comparison link data;

[0007] Determine whether the available multi-type comparison links have measured data;

[0008] If the determination result is that the available multi-type comparison link has measured data, preprocessing the measured data to obtain preprocessed data;

[0009] If the determination result is that there is no measured data for the available multi-type comparison link, digital features of the comparison data are extracted for the available multi-type comparison link, and the multi-type comparison link error is simulated based on the extracted digital feature data to obtain a link error simulation sequence;

[0010] Simulating the frequency source noise sequence to obtain a frequency source synthetic noise simulation sequence, and superimposing the link error simulation sequence on the frequency source synthetic noise simulation sequence to generate a time-frequency comparison data simulation result;

[0011] The obtained preprocessed data or time-frequency comparison data simulation results are used as observations, and the Kalman filter algorithm is used to perform state estimation on the clock difference parameters of the comparison fusion results, thereby obtaining high-precision time difference comparison fusion results.

[0012] In a second aspect, a multi-type time-frequency comparison data fusion system is provided, the system comprising:

[0013] A query module is used to query the availability of multi-type comparison links between two distributed timekeeping nodes, and only merge the available multi-type comparison link data;

[0014] A judgment module, used to judge whether there is actual measured data in the available multi-type comparison links;

[0015] A data preprocessing module, configured to perform data preprocessing on the measured data to obtain preprocessed data if the result of the determination is that the available multi-type comparison link has measured data;

[0016] A link error simulation sequence acquisition module is used to extract digital features of comparison data for the available multi-type comparison links if the result of the determination is that there is no measured data for the available multi-type comparison links, and simulate the multi-type comparison link errors based on the extracted digital feature data to obtain a link error simulation sequence;

[0017] A simulation result acquisition module is used to simulate the frequency source noise sequence to obtain a frequency source synthetic noise simulation sequence, and superimpose the link error simulation sequence on the frequency source synthetic noise simulation sequence to generate a time-frequency comparison data simulation result;

[0018] The data fusion module is used to use the obtained pre-processed data or time-frequency comparison data simulation results as observation quantities, and use the Kalman filter algorithm to perform state estimation on the clock difference parameters of the comparison fusion results, so as to obtain high-precision time difference comparison fusion results.

[0019] The embodiment of the present invention provides a method and system for fusing multiple types of time-frequency comparison data. Based on multiple types of time-frequency comparison and time difference data measured or simulated between stations, the method and system complete the fusion and sharing of multiple combinations of multiple types of time-frequency comparison data between distributed stations. The Kalman filtering algorithm is used to generate stable and reliable time-frequency comparison fusion data between stations, providing important data support for the joint timekeeping between nodes.

[0020] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0022] Figure 1 A schematic diagram of an implementation flow of a multi-type time-frequency comparison data fusion method provided according to an embodiment of the present invention;

[0023] Figure 2 A flow chart of inter-station measured comparison data preprocessing provided according to an embodiment of the present invention;

[0024] Figure 3 A schematic diagram of a simulation process of inter-station time-frequency comparison data provided according to an embodiment of the present invention;

[0025] Figure 4 A schematic diagram of a clock error parameter estimation process provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0028] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0029] In order to achieve high-precision time-frequency comparison between distributed time-keeping nodes, an embodiment of the present invention provides a multi-type time-frequency comparison data fusion method and system.

[0030] Figure 1 FIG. 1 is a schematic diagram of a multi-type time-frequency comparison data fusion method according to an embodiment of the present invention. Figure 1 , the method comprising:

[0031] Query the availability of multi-type comparison links between two distributed timekeeping nodes, and only merge the available multi-type comparison link data;

[0032] Determine whether the available multi-type comparison links have measured data;

[0033] If the determination result is that the available multi-type comparison link has measured data, preprocessing the measured data to obtain preprocessed data;

[0034] If the determination result is that there is no measured data for the available multi-type comparison link, digital features of the comparison data are extracted for the available multi-type comparison link, and the multi-type comparison link error is simulated based on the extracted digital feature data to obtain a link error simulation sequence;

[0035] Simulating the frequency source noise sequence to obtain a frequency source synthetic noise simulation sequence, and superimposing the link error simulation sequence on the frequency source synthetic noise simulation sequence to generate a time-frequency comparison data simulation result;

[0036] The obtained preprocessed data or time-frequency comparison data simulation results are used as observations, and the Kalman filter algorithm is used to perform state estimation on the clock difference parameters of the comparison fusion results, thereby obtaining high-precision time difference comparison fusion results.

[0037] In a specific implementation manner, the querying of the availability of the multi-type comparison links between the two distributed timekeeping nodes only merges the available multi-type comparison link data, specifically including:

[0038] Before fusing multiple types of time difference comparison data, it is necessary to query the availability of multiple types of comparison links between two distributed timing nodes. When any type of time-frequency comparison link between the nodes is interrupted, the time difference comparison data of the link with the interrupted type will not be included in the fusion calculation, and the remaining available types of comparison links between the nodes will be used for subsequent data fusion.

[0039] In a specific implementation, if the determination result is that the available multi-type comparison link has measured data, data preprocessing is performed on the measured data to obtain preprocessed data, specifically including:

[0040] If the result of the determination is that there is measured data for the available multi-type comparison link, the 3σ criterion is used to preprocess the multi-type comparison measured data, traverse all comparison types and all observation times, and remove gross errors. If the inter-node comparison time difference σt corresponding to any observation time satisfies the following conditions:

[0041]

[0042] In the formula, σt is the time difference between nodes corresponding to any observation time; is the average value of the measured comparison data between nodes; σ is the standard deviation of the measured comparison data between nodes;

[0043] The comparison time difference value corresponding to the current observation time is determined to be a gross error and must be eliminated. Otherwise, the data is retained and participates in the subsequent data fusion process.

[0044] In a specific implementation, if the determination result is that there is no measured data for the available multi-type comparison link, digital features of the comparison data are extracted for the available multi-type comparison link, and the multi-type comparison link error is simulated based on the extracted digital feature data to obtain a link error simulation sequence, specifically including:

[0045] If any type of comparison link is not damaged but there is no measured data for fusion, the digital features of different types of time-frequency comparison data are analyzed and extracted according to the technical characteristics of different types of time-frequency comparison between distributed stations. The main error terms of different types of time-frequency comparison links are simulated according to the analysis results of the digital features of the time-frequency comparison data. A random error sequence is generated by combining the errors of the main error terms to obtain a link error simulation sequence.

[0046] In a specific implementation, the errors on the distributed inter-station time-frequency comparison link are independent of each other, and the variance of their sum is the sum of the variances of the errors;

[0047] The simulating of the frequency source noise sequence to obtain a frequency source synthetic noise simulation sequence, and superimposing the link error simulation sequence on the frequency source synthetic noise simulation sequence to generate a time-frequency comparison data simulation result specifically includes:

[0048] When generating inter-station time-frequency comparison simulation data, the variance of each error is summed, and the sum of the variances is σ 2 , which in turn produces a variance of σ 2 A random sequence, that is, a link error simulation sequence; the random sequence is superimposed on a frequency source synthetic noise simulation sequence with a variance of 1 to generate a distributed inter-station time-frequency comparison data simulation result.

[0049] In a specific implementation, the obtained pre-processed data or time-frequency comparison data simulation results are used as observation quantities, and the Kalman filter algorithm is used to perform state estimation on the clock difference parameters of the comparison fusion results, thereby obtaining a high-precision time difference comparison fusion result, specifically including:

[0050] The clock error parameter is determined as the phase deviation a from the following formula representing the clock error data: 0 and frequency deviation a 1 :

[0051]

[0052] Where x(t) is the clock error data; a 0 is the phase deviation; a 1 is the frequency deviation; a 2 is the frequency drift coefficient; x r (t) is a random item;

[0053] The phase deviation a of the fused comparison data 0 and frequency deviation a 1 As the parameter to be estimated, take the state quantity X = [a 0 ,a 1 ] T ; Using the preprocessed data or the simulation results of the time-frequency comparison data obtained at the current moment as the observed quantity, the state quantity to be estimated is measured and updated according to the following parameter estimation model to complete the estimation of the parameter to be estimated;

[0054] The parameter estimation model is as follows:

[0055]

[0056] Where, X k The state vector, that is, X = [a 0 ,a 1 ] T , represents the phase deviation and frequency deviation of the fusion result at the kth second; Φk,k-1 is the state transfer matrix from the k-1th second to the kth second. For a discrete time system, if the sampling interval is Δt, the calculation formula of the state transfer matrix is: Γ k-1 W k-1 is the process noise, set to zero-mean Gaussian white noise, and its covariance matrix is ​​Q; Z k The measurement vector is the measured comparison data of the kth second, which is composed of multiple types of time-frequency comparison data available at the current moment; H k is the measurement matrix, which maps the state vector to the measurement space and is the unit matrix; V k To measure the noise, it is set to zero-mean Gaussian white noise, and its covariance matrix is ​​R;

[0057] State quantity X k Valuation The solution can be found by the following steps:

[0058] (1) State prediction

[0059]

[0060] (2) Prediction variance matrix

[0061]

[0062] Where P k,k-1 is the error covariance matrix, and P is updated through the measurement update step. k-1 Update to P k ;

[0063] (3) Filter gain

[0064]

[0065] In the formula, K k is the Kalman gain matrix, which is used to determine the weight of the state estimation update. Specifically, the weight value of each comparison data is in R k Given in the matrix;

[0066] (4) Estimated variance matrix

[0067] P k =(IK k ·H k ) k，k-1 ;

[0068] Where I is the unit matrix;

[0069] (5) State Estimation

[0070]

[0071] Based on the available multi-type time-frequency comparison data at any observation time, that is, the obtained pre-processed data or time-frequency comparison data simulation results, after the time update and measurement update process, the phase deviation a at the current time k can be estimated 0 and frequency deviation a 1 , substituting into the formula of clock difference data, the fused inter-station comparison time difference value can be calculated, which is the fusion result of multi-type time-frequency comparison data at the current moment.

[0072] In order to better understand the present invention, the implementation process of the present invention is described below in conjunction with specific implementations.

[0073] The specific steps of the multi-type time-frequency comparison data fusion method provided by the embodiment of the present invention include:

[0074] Step 1: Query the link availability between nodes

[0075] Before fusing multiple types of time difference comparison data, it is necessary to query the availability of the comparison links between nodes. When a certain time-frequency comparison link between stations is interrupted or encounters a fault, the time difference comparison data of this link will not be included in the fusion calculation. Other available comparison links between stations will be used for fusion to generate a fused comparison data sequence.

[0076] Step 2: Preprocessing of inter-station measured comparison data

[0077] Multi-type time comparison data between stations are the data source for comparison data fusion. If there is no measured data, simulated data is obtained through distributed inter-station time-frequency comparison data simulation technology. If there is measured data, it is preprocessed according to the 3σ criterion and then used for comparison data fusion.

[0078] When using the 3σ criterion to preprocess multi-type comparison measured data, it is necessary to traverse all comparison types and all observation times to eliminate gross errors. If the inter-station comparison time difference σt corresponding to a certain observation time satisfies the following conditions:

[0079]

[0080] In the formula, σt is the time difference between nodes corresponding to any observation time; is the average value of the measured comparison data between nodes; σ is the standard deviation of the measured comparison data between nodes;

[0081] The comparison time difference value corresponding to the current observation time is determined to be a gross error and must be eliminated. Otherwise, the data is retained and participates in the subsequent data fusion process.

[0082] For the detailed process of data preprocessing for distributed inter-station comparison, see Figure 2 ,include:

[0083] 1) Select a certain type of measured data for comparison, and judge whether the comparison time difference value at a certain observation time is a gross error according to the 3σ criterion. If the time difference value is judged to be a gross error, it will be eliminated, otherwise the corresponding time difference value will be retained;

[0084] 2) Determine whether there are other measured data for this type of comparison method. If yes, continue to repeat the gross error determination process. If no, complete the measured data preprocessing of this comparison type;

[0085] 3) Determine whether there are other types of comparison data that have not been preprocessed. If so, repeat the data preprocessing process of steps 1) and 2) above. If not, end the distributed inter-station measured comparison data preprocessing.

[0086] Step 3: Multi-type time-frequency comparison data simulation

[0087] If a certain type of comparison link is not damaged but there is no measured data for fusion, the digital characteristics of different types of time-frequency comparison data can be analyzed and extracted according to the technical characteristics of different types of time-frequency comparison between distributed stations. The main error terms of different types of time-frequency comparison links can be simulated according to the results of the digital characteristic analysis of the time-frequency comparison data. A random error sequence is generated by combining various errors, and then the time-frequency comparison data between distributed stations is simulated for use in the fusion of multiple types of time-frequency comparison data.

[0088] Since the errors on the distributed inter-station time-frequency comparison link are independent of each other, the variance of their sum is the sum of the variances of each error. Therefore, when simulating the inter-station time-frequency comparison data, the variances of each error are first summed, and the sum of the variances is σ2. Then, MATLAB software is used to generate a random sequence with a variance of σ2. This random sequence is superimposed on the frequency source synthetic noise simulation sequence (a random sequence with a variance of 1) to generate the simulation results of the distributed inter-station time-frequency comparison data.

[0089] The simulation process of distributed station time-frequency comparison data is as follows: Figure 3 shown.

[0090] After obtaining the simulation sequence of inter-station time-frequency comparison data, the distributed inter-station time-frequency comparison data simulation algorithm is verified by comparing the consistency of different types of time-frequency comparison simulation data with the measured data. The same method is used to calculate the statistical values ​​of the simulation data and the measured data of different types of comparison means, and the consistency of the simulation data and the measured data is judged by comparing the statistical values ​​of the two. There will inevitably be a certain error between the simulation results of the distributed inter-station multi-type time-frequency comparison data and the measured data. The main reasons are analyzed as follows:

[0091] 1) Environmental factors: Different environmental conditions may affect the transmission and reception of signals, such as weather, building obstructions, etc., resulting in errors in simulation data.

[0092] 2) Signal transmission delay. Wireless signal transmission takes a certain amount of time. The time delay affects the difference between the simulation data and the actual measurement data.

[0093] 3) Instrument accuracy. The different accuracy of the equipment and instruments involved in the time-frequency comparison may lead to differences between the measured data and the theoretical simulation results.

[0094] 4) Calibration problem: If the device is not calibrated correctly or the calibration is inaccurate, it will also affect the time comparison results, resulting in errors in the simulation data.

[0095] Step 4: Multi-type time-frequency comparison data fusion

[0096] 1) Systematic error calibration of multi-type time-frequency comparison data

[0097] Before the fusion of multi-type time difference comparison data, system difference calibration is required. System difference calibration of multi-type comparison data refers to the use of a calibrated system difference of a comparison type to calibrate the system difference of other uncalibrated multi-type comparison data. After the system difference calibration, multiple types of comparison methods monitor each other to monitor whether there are any abnormalities in the currently available multi-type comparison data. The monitoring and calibration methods are as follows:

[0098] Assuming that the optical fiber time-frequency comparison link between stations has been calibrated, the system difference of the optical fiber time-frequency comparison link is used as the standard, and the system difference calibration of other types of comparison links between stations is performed. After calibration, multiple comparison methods monitor each other. Assuming that at a certain moment, the system detects that the optical fiber time-frequency comparison data has a large jump, while other types of comparison data are normal, the system generates an alarm message and determines that the optical fiber time-frequency comparison link is abnormal. At this time, other calibrated comparison types are used to reversely calibrate the optical fiber time-frequency comparison link to avoid the influence of its abnormal comparison data on the fusion result, thereby improving the stability and reliability of the comparison data fusion result. Assuming that at a certain moment, the system detects that all time-frequency comparison data have a large jump, the system generates an alarm message and determines that the system is abnormal at that moment. All comparison data do not participate in the calculation, thereby avoiding the influence of abnormal comparison data on the fusion result, thereby improving the stability and reliability of the comparison data fusion result.

[0099] 2) Multi-type time-frequency comparison data fusion

[0100] The multi-type time-frequency comparison data fusion method is to use the clock error parameters (including phase deviation and frequency deviation) of the time-frequency comparison data fusion results as the parameters to be estimated, and use the currently available multi-type comparison data between stations as the observation quantity to estimate the parameters to be estimated, and then obtain the fused time difference comparison result, and use the multi-type time-frequency comparison simulation data between stations to obtain a reliable, stable and high-precision time difference comparison result.

[0101] For specific implementation, see Figure 4 .

[0102] The clock error parameter is determined as the phase deviation a from the following formula representing the clock error data: 0 and frequency deviation a 1 :

[0103]

[0104] Where x(t) is the clock error data; a 0 is the phase deviation; a 1 is the frequency deviation; a 2 is the frequency drift coefficient; x r (t) is a random item;

[0105] As the time of use increases, the clock will age to a certain extent, but the change is not significant in a short period of time. Therefore, when the measurement time is short, the impact of frequency drift (aging) can be ignored. When calculating the inter-station time-frequency ratio on the data fusion result, the impact of phase deviation and frequency deviation is mainly considered.

[0106] The phase deviation a of the fused comparison data 0 and frequency deviation a 1 As the parameter to be estimated, take the state quantity X = [a 0 ,a 1 ] T ; Using the preprocessed data or the simulation results of the time-frequency comparison data obtained at the current moment as the observed quantity, the state quantity to be estimated is measured and updated according to the following parameter estimation model to complete the estimation of the parameter to be estimated;

[0107] The parameter estimation model is as follows:

[0108]

[0109] Where, X k The state vector, that is, X = [a 0 ,a 1 ] T , represents the phase deviation and frequency deviation of the fusion result at the kth second; Φ k,k-1 is the state transfer matrix from the k-1th second to the kth second. For a discrete time system, if the sampling interval is Δt, the calculation formula of the state transfer matrix is: Γ k-1 W k-1 is the process noise, set to zero-mean Gaussian white noise, and its covariance matrix is ​​Q; Z k The measurement vector is the measured comparison data of the kth second, which is composed of multiple types of time-frequency comparison data available at the current moment; H kis the measurement matrix, which maps the state vector to the measurement space and is the unit matrix; V k To measure the noise, it is set to zero-mean Gaussian white noise, and its covariance matrix is ​​R;

[0110] State quantity X k Valuation The solution can be found by the following steps:

[0111] (1) State prediction

[0112]

[0113] (2) Prediction variance matrix

[0114]

[0115] Where P k,k-1 is the error covariance matrix, and P is updated through the measurement update step. k-1 Update to P k ;

[0116] (3) Filter gain

[0117]

[0118] In the formula, K k is the Kalman gain matrix, which is used to determine the weight of the state estimation update. Specifically, the weight value of each comparison data is in R k Given in the matrix;

[0119] (4) Estimated variance matrix

[0120] P k =(IK k ·H k ) k，k-1 ;

[0121] Where I is the unit matrix;

[0122] (5) State Estimation

[0123]

[0124] Based on the available multi-type time-frequency comparison data at any observation time, that is, the obtained pre-processed data or time-frequency comparison data simulation results, after the time update and measurement update process, the phase deviation a at the current time k can be estimated 0 and frequency deviation a 1 , substituting into the formula of clock difference data, the fused inter-station comparison time difference value can be calculated, which is the fusion result of multi-type time-frequency comparison data at the current moment.

[0125] 3) Verification of multi-type time-frequency comparison data fusion results

[0126] Finally, in order to prove the accuracy and reliability of the multi-type time-frequency comparison data fusion results, it is necessary to verify them. The performance of the distributed station multi-type time-frequency comparison data fusion algorithm is evaluated by comparing the STD statistical values ​​of the time comparison data fusion results of different combinations after fusion.

[0127] STD represents the standard deviation of the time-frequency comparison data, and the calculation formula is as follows:

[0128]

[0129] Where: x i Time comparison data corresponding to a certain observation moment; is the average value of all time comparison data during this observation period; N is the number of time comparison data corresponding to a certain comparison type.

[0130] If the STD statistical values ​​of various combinations of multi-type comparison fusion data are all ≤1 nanosecond (ns), it means that the multi-type comparison data fusion method is helpful to ensure the accuracy and reliability of the joint timing of multiple time-frequency comparison methods between stations.

[0131] A multi-type time-frequency comparison data fusion method provided in an embodiment of the present invention is based on various types of time-frequency comparison and time difference data measured or simulated between stations, completes the fusion and sharing of various combinations of multi-type time-frequency comparison data between distributed stations, and adopts the Kalman filtering algorithm to generate a stable and reliable inter-station time-frequency comparison fusion data, providing important data support for the joint timekeeping between each node.

[0132] Based on the same inventive concept, the present invention also provides a multi-type time-frequency comparison data fusion system, the system comprising:

[0133] A query module is used to query the availability of multi-type comparison links between two distributed timekeeping nodes, and only merge the available multi-type comparison link data;

[0134] A judgment module, used to judge whether there is actual measured data in the available multi-type comparison links;

[0135] A data preprocessing module, configured to perform data preprocessing on the measured data to obtain preprocessed data if the result of the determination is that the available multi-type comparison link has measured data;

[0136] A link error simulation sequence acquisition module is used to extract digital features of comparison data for the available multi-type comparison links if the result of the determination is that there is no measured data for the available multi-type comparison links, and simulate the multi-type comparison link errors based on the extracted digital feature data to obtain a link error simulation sequence;

[0137] A simulation result acquisition module is used to simulate the frequency source noise sequence to obtain a frequency source synthetic noise simulation sequence, and superimpose the link error simulation sequence on the frequency source synthetic noise simulation sequence to generate a time-frequency comparison data simulation result;

[0138] The data fusion module is used to use the obtained pre-processed data or time-frequency comparison data simulation results as observation quantities, and use the Kalman filter algorithm to perform state estimation on the clock difference parameters of the comparison fusion results, so as to obtain high-precision time difference comparison fusion results.

[0139] In a specific implementation, the query module is specifically used to:

[0140] Before fusing multiple types of time difference comparison data, it is necessary to query the availability of multiple types of comparison links between two distributed timing nodes. When any type of time-frequency comparison link between the nodes is interrupted, the time difference comparison data of the link with the interrupted type will not be included in the fusion calculation, and the remaining available types of comparison links between the nodes will be used for subsequent data fusion.

[0141] In a specific implementation, the data preprocessing module is specifically used to:

[0142] If the result of the determination is that there is measured data for the available multi-type comparison link, the 3σ criterion is used to preprocess the multi-type comparison measured data, traverse all comparison types and all observation times, and remove gross errors. If the inter-node comparison time difference σt corresponding to any observation time satisfies the following conditions:

[0143]

[0144] In the formula, σt is the time difference between nodes corresponding to any observation time; is the average value of the measured comparison data between nodes; σ is the standard deviation of the measured comparison data between nodes;

[0145] The comparison time difference value corresponding to the current observation time is determined to be a gross error and must be eliminated. Otherwise, the data is retained and participates in the subsequent data fusion process.

[0146] In a specific implementation manner, the link error simulation sequence acquisition module is specifically used to:

[0147] If any type of comparison link is not damaged but there is no measured data for fusion, the digital features of different types of time-frequency comparison data are analyzed and extracted according to the technical characteristics of different types of time-frequency comparison between distributed stations. The main error terms of different types of time-frequency comparison links are simulated according to the results of the digital feature analysis of the time-frequency comparison data. A random error sequence is generated by combining the errors of the main error terms to obtain a link error simulation sequence.

[0148] In a specific implementation, the errors on the distributed inter-station time-frequency comparison link are independent of each other, and the variance of their sum is the sum of the variances of the errors;

[0149] The simulation result acquisition module is specifically used for:

[0150] When generating inter-station time-frequency comparison simulation data, the variance of each error is summed, and the sum of the variances is σ 2 , which in turn produces a variance of σ 2 A random sequence, that is, a link error simulation sequence; the random sequence is superimposed on a frequency source synthetic noise simulation sequence with a variance of 1 to generate a distributed inter-station time-frequency comparison data simulation result.

[0151] In a specific implementation, the data fusion module is specifically used to:

[0152] The clock error parameter is determined as the phase deviation a from the following formula representing the clock error data: 0 and frequency deviation a 1 :

[0153]

[0154] Where x(t) is the clock error data; a 0 is the phase deviation; a 1 is the frequency deviation; a 2 is the frequency drift coefficient; x r (t) is a random item;

[0155] The phase deviation a of the fused comparison data 0 and frequency deviation a 1 As the parameter to be estimated, take the state quantity X = [a 0 ,a 1 ] T ; Using the preprocessed data or the simulation results of the time-frequency comparison data obtained at the current moment as the observed quantity, the state quantity to be estimated is measured and updated according to the following parameter estimation model to complete the estimation of the parameter to be estimated;

[0156] The parameter estimation model is as follows:

[0157]

[0158] Where, X k The state vector, that is, X = [a 0 ,a 1 ] T , represents the phase deviation and frequency deviation of the fusion result at the kth second; Φ k,k-1is the state transfer matrix from the k-1th second to the kth second. For a discrete time system, if the sampling interval is Δt, the calculation formula of the state transfer matrix is: Γ k-1 W k-1 is the process noise, set to zero-mean Gaussian white noise, and its covariance matrix is ​​Q; Z k The measurement vector is the measured comparison data of the kth second, which is composed of multiple types of time-frequency comparison data available at the current moment; H k is the measurement matrix, which maps the state vector to the measurement space and is the unit matrix; V k To measure the noise, it is set to zero-mean Gaussian white noise, and its covariance matrix is ​​R;

[0159] State quantity X k Valuation The solution can be found by the following steps:

[0160] (1) State prediction

[0161]

[0162] (2) Prediction variance matrix

[0163]

[0164] Where P k,k-1 is the error covariance matrix, and P is updated through the measurement update step. k-1 Update to P k ;

[0165] (3) Filter gain

[0166]

[0167] In the formula, K k is the Kalman gain matrix, which is used to determine the weight of the state estimation update. Specifically, the weight value of each comparison data is in R k Given in the matrix;

[0168] (4) Estimated variance matrix

[0169] P k =(IK k ·H k ) k，k-1 ;

[0170] Where I is the unit matrix;

[0171] (5) State Estimation

[0172]

[0173] Based on the available multi-type time-frequency comparison data at any observation time, that is, the obtained pre-processed data or time-frequency comparison data simulation results, after the time update and measurement update process, the phase deviation a at the current time k can be estimated 0 and frequency deviation a 1 , substituting into the formula of clock difference data, the fused inter-station comparison time difference value can be calculated, which is the fusion result of multi-type time-frequency comparison data at the current moment.

[0174] A multi-type time-frequency comparison data fusion system provided by an embodiment of the present invention is based on various types of time-frequency comparison and time difference data measured or simulated between stations, completes the fusion and sharing of various combinations of multi-type time-frequency comparison data between distributed stations, and adopts the Kalman filtering algorithm to generate stable and reliable time-frequency comparison fusion data between stations, providing important data support for the joint timekeeping between each node.

[0175] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

Claims

1. A multi-type time-frequency comparison data fusion method, characterized in that: The method comprises: Query the availability of multi-type comparison links between two distributed timekeeping nodes, and only merge the available multi-type comparison link data; Determine whether the available multi-type comparison links have measured data; If the determination result is that the available multi-type comparison link has measured data, preprocessing the measured data to obtain preprocessed data; If the determination result is that there is no measured data for the available multi-type comparison link, digital features of the comparison data are extracted for the available multi-type comparison link, and the multi-type comparison link error is simulated based on the extracted digital feature data to obtain a link error simulation sequence; Simulating the frequency source noise sequence to obtain a frequency source synthetic noise simulation sequence, and superimposing the link error simulation sequence on the frequency source synthetic noise simulation sequence to generate a time-frequency comparison data simulation result; The obtained preprocessed data or time-frequency comparison data simulation results are used as observations, and the Kalman filter algorithm is used to perform state estimation on the clock difference parameters of the comparison fusion results, thereby obtaining high-precision time difference comparison fusion results.

2. The method according to claim 1, characterized in that The querying of the availability of the multi-type comparison links between the two distributed timekeeping nodes only merges the available multi-type comparison link data, specifically including: Before fusing multiple types of time difference comparison data, it is necessary to query the availability of multiple types of comparison links between two distributed timing nodes. When any type of time-frequency comparison link between the nodes is interrupted, the time difference comparison data of the link with the interrupted type will not be included in the fusion calculation, and the remaining available types of comparison links between the nodes will be used for subsequent data fusion.

3. The method according to claim 2, characterized in that If the determination result is that the available multi-type comparison link has measured data, data preprocessing is performed on the measured data to obtain preprocessed data, specifically including: If the result of the determination is that there is measured data for the available multi-type comparison link, the 3σ criterion is used to preprocess the multi-type comparison measured data, traverse all comparison types and all observation times, and remove gross errors. If the inter-node comparison time difference σt corresponding to any observation time satisfies the following conditions: In the formula, σt is the time difference between nodes corresponding to any observation time; is the average value of the measured comparison data between nodes; σ is the standard deviation of the measured comparison data between nodes; The comparison time difference value corresponding to the current observation time is determined to be a gross error and must be eliminated. Otherwise, the data is retained and participates in the subsequent data fusion process.

4. The method according to claim 2, characterized in that: If the determination result is that there is no measured data for the available multi-type comparison link, then extracting digital features of the comparison data for the available multi-type comparison link, and simulating the multi-type comparison link error based on the extracted digital feature data to obtain a link error simulation sequence, specifically including: If any type of comparison link is not damaged but there is no measured data for fusion, the digital features of different types of time-frequency comparison data are analyzed and extracted according to the technical characteristics of different types of time-frequency comparison between distributed stations. The main error terms of different types of time-frequency comparison links are simulated according to the results of the digital feature analysis of the time-frequency comparison data. A random error sequence is generated by combining the errors of the main error terms to obtain a link error simulation sequence.

5. The method according to claim 4, characterized in that The errors on the time-frequency comparison link between distributed stations are independent of each other, and the variance of their sum is the sum of the variances of each item; The simulating of the frequency source noise sequence to obtain a frequency source synthetic noise simulation sequence, and superimposing the link error simulation sequence on the frequency source synthetic noise simulation sequence to generate a time-frequency comparison data simulation result specifically includes: When generating inter-station time-frequency comparison simulation data, the variances of various errors are summed, and the sum of the variances is σ 2 , which in turn produces a variance of σ 2 A random sequence, that is, a link error simulation sequence; the random sequence is superimposed on a frequency source synthetic noise simulation sequence with a variance of 1 to generate a distributed inter-station time-frequency comparison data simulation result.

6. The method according to claim 3 or 5, characterized in that: The obtained pre-processed data or time-frequency comparison data simulation results are used as observation quantities, and the Kalman filter algorithm is used to perform state estimation on the clock difference parameters of the comparison fusion results, thereby obtaining a high-precision time difference comparison fusion result, specifically including: The clock error parameters are determined from the following formula for expressing the clock error data as phase deviation a0 and frequency deviation a1: Where x(t) is the clock error data; a0 is the phase deviation; a1 is the frequency deviation; a2 is the frequency drift coefficient; x r (t) is a random item; The phase deviation a0 and frequency deviation a1 of the fused comparison data are taken as the parameters to be estimated, and the state quantity X = [a0, a1] is taken. T ; Using the preprocessed data or the simulation results of the time-frequency comparison data obtained at the current moment as the observed quantity, the state quantity to be estimated is measured and updated according to the following parameter estimation model to complete the estimation of the parameter to be estimated; The parameter estimation model is as follows: Where, X k The state vector, that is, X = [a0, a1] T , represents the phase deviation and frequency deviation of the fusion result at the kth second; Φ k，k-1 is the state transfer matrix from the k-1th second to the kth second. For a discrete time system, if the sampling interval is Δt, the calculation formula of the state transfer matrix is: Γ k-1 W k-1 is the process noise, set to zero-mean Gaussian white noise, and its covariance matrix is ​​Q; Z k The measurement vector is the measured comparison data of the kth second, which is composed of multiple types of time-frequency comparison data available at the current moment; H k is the measurement matrix, which maps the state vector to the measurement space and is the unit matrix; V k To measure the noise, it is set to zero-mean Gaussian white noise, and its covariance matrix is ​​R; State quantity X k Valuation The solution can be found by the following steps: (1) State prediction (2) Prediction variance matrix Where P k，k-1 is the error covariance matrix, and P is updated through the measurement update step. k-1 Update to P k ; (3) Filter gain In the formula, K k is the Kalman gain matrix, which is used to determine the weight of the state estimation update. Specifically, the weight value of each comparison data is in R k Given in the matrix; (4) Estimated variance matrix Where I is the unit matrix; (5) State Estimation Based on the available multi-type time-frequency comparison data at any observation moment, that is, the obtained pre-processed data or time-frequency comparison data simulation results, after the time update and measurement update process, the phase deviation a0 and frequency deviation a1 at the current moment k can be estimated. Substituting into the formula of the clock difference data can calculate the fused inter-station comparison time difference value, which is the fusion result of the multi-type time-frequency comparison data at the current moment.

7. A multi-type time-frequency comparison data fusion system, characterized in that: The multi-type time-frequency comparison data fusion method according to any one of claims 1 to 6 is adopted, and the system comprises: A query module is used to query the availability of multi-type comparison links between two distributed timekeeping nodes, and only merge the available multi-type comparison link data; A judgment module, used to judge whether there is actual measured data in the available multi-type comparison links; A data preprocessing module, configured to perform data preprocessing on the measured data to obtain preprocessed data if the result of the determination is that the available multi-type comparison link has measured data; A link error simulation sequence acquisition module is used to extract digital features of comparison data for the available multi-type comparison links if the result of the determination is that there is no measured data for the available multi-type comparison links, and simulate the multi-type comparison link errors based on the extracted digital feature data to obtain a link error simulation sequence; A simulation result acquisition module is used to simulate the frequency source noise sequence to obtain a frequency source synthetic noise simulation sequence, and superimpose the link error simulation sequence on the frequency source synthetic noise simulation sequence to generate a time-frequency comparison data simulation result; The data fusion module is used to use the obtained pre-processed data or time-frequency comparison data simulation results as observation quantities, and use the Kalman filter algorithm to perform state estimation on the clock difference parameters of the comparison fusion results, so as to obtain high-precision time difference comparison fusion results.

8. The system according to claim 7, characterized in that The query module is specifically used for: Before fusing multiple types of time difference comparison data, it is necessary to query the availability of multiple types of comparison links between two distributed timing nodes. When any type of time-frequency comparison link between the nodes is interrupted, the time difference comparison data of the link with the interrupted type will not be included in the fusion calculation, and the remaining available types of comparison links between the nodes will be used for subsequent data fusion.

9. The system according to claim 8, characterized in that The data preprocessing module is specifically used for: If the result of the determination is that there is measured data for the available multi-type comparison link, the 3σ criterion is used to preprocess the multi-type comparison measured data, traverse all comparison types and all observation times, and remove gross errors. If the inter-node comparison time difference σt corresponding to any observation time satisfies the following conditions: In the formula, σt is the time difference between nodes corresponding to any observation time; is the average value of the measured comparison data between nodes; σ is the standard deviation of the measured comparison data between nodes; The comparison time difference value corresponding to the current observation time is determined to be a gross error and must be eliminated. Otherwise, the data is retained and participates in the subsequent data fusion process.

10. The system according to claim 8, characterized in that The link error simulation sequence acquisition module is specifically used for: If any type of comparison link is not damaged but there is no measured data for fusion, the digital features of different types of time-frequency comparison data are analyzed and extracted according to the technical characteristics of different types of time-frequency comparison between distributed stations. The main error terms of different types of time-frequency comparison links are simulated according to the results of the digital feature analysis of the time-frequency comparison data. A random error sequence is generated by combining the errors of the main error terms to obtain a link error simulation sequence.

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