A Beidou and long wave fusion timing method and related device

Abstract

The present application belongs to a timing method. Aiming at the current timing method integrating Beidou satellite navigation system and long-wave navigation technology, there are technical problems such as satellite rejection or failure, poor accuracy and large jitter of long-wave timing results. A Beidou and long-wave fusion timing method and related devices are provided to obtain a long-wave time difference data set and a Beidou time difference data set. First, outlier processing is performed on the long-wave time difference data and the Beidou time difference data respectively, and the long-wave time difference data is corrected to reduce the fluctuation of the long-wave time difference data, and the outliers are removed to ensure the accuracy of subsequent timing. Then, the extended Kalman filter algorithm is used to fuse the long-wave time difference data and the Beidou time difference data at each moment in turn, and finally the timing is completed according to the fusion result set. By integrating the advantages of the Beidou satellite navigation system and the long-wave navigation technology, the present application adopts the extended Kalman filter algorithm to significantly improve the system fusion timing accuracy and timing stability.

Description

Technical Field

[0001] The present application relates to a timing method, and specifically to a Beidou and longwave fusion timing method and related devices. Background Art

[0002] With the rapid development of science and technology and the continuous expansion of application fields, the requirements for the accuracy and stability of time synchronization are also constantly increasing. Traditional single timing methods, such as timing methods based on ground base stations or single satellite systems, have been unable to meet the growing demand due to problems such as limited coverage and susceptibility to interference. Especially under extreme conditions such as complex electromagnetic environments and long-distance transmission, the limitations of traditional timing methods are more obvious, and more advanced and reliable time synchronization technologies are urgently needed to support the operation and development of modern society.

[0003] It is in this context that the integration of the Beidou satellite navigation system and long-wave navigation technology came into being. The Beidou satellite navigation system provides strong support for time synchronization with its high-precision timing capability and global coverage. Long-wave navigation technology, with its unique low-frequency long-wave signal characteristics, demonstrates excellent anti-interference ability and long-distance transmission capability in complex electromagnetic environments. The integration of the two not only achieves complementary advantages, but also provides new solutions and development directions for high-precision time synchronization.

[0004] However, in complex scenarios, the Beidou satellite navigation system has problems with satellite denial or failure; the long-wave signal propagation delay in long-wave navigation technology is affected by many factors such as complex changes in electrical characteristics, and has obvious time-varying characteristics, resulting in long-wave timing results with poor accuracy and large jitter. Summary of the invention

[0005] The present application aims to provide a Beidou and long-wave fusion timing method and related devices to address the technical problems of satellite denial or failure, as well as poor accuracy and large jitter of long-wave timing results in the current timing method integrating the Beidou satellite navigation system and long-wave navigation technology.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions:

[0007] In the first aspect, the present application proposes a Beidou and longwave fusion timing method and a timing method, comprising:

[0008] Acquire a long-wave time difference data set and a Beidou time difference data set; each long-wave time difference data in the long-wave time difference data set is a time difference value between the 1PPS signals output by the long-wave timing receiver and the rubidium atomic clock at each moment, and each Beidou time difference data in the Beidou time difference data set is a time difference value between the 1PPS signals output by the Beidou timing receiver and the rubidium atomic clock at each moment;

[0009] The outliers of long-wave time difference data and Beidou time difference data are processed respectively, and the long-wave time difference data is corrected to reduce the fluctuation of the long-wave time difference data;

[0010] The extended Kalman filter algorithm is used to fuse the long-wave time difference data and the Beidou time difference data at each moment in turn, and a fusion result set containing the fusion results at each moment is obtained;

[0011] The timing is completed according to the fusion result set.

[0012] Furthermore, after obtaining the fusion result set including the fusion result at each moment, the method further includes:

[0013] The fusion result at each moment in the fusion result set is subjected to windowing and smoothing processing.

[0014] Furthermore, the method for performing windowing and smoothing processing includes:

[0015]

[0016] in, express The fusion result after time windowing and smoothing, Indicates the window size, express The fusion result of the moment and its previous two moments.

[0017] Furthermore, the method for performing outlier processing is to use a Hampel filter to perform outlier processing.

[0018] Furthermore, the method for correcting the long-wave time difference data is as follows:

[0019]

[0020] in, After correction processing Long wave time difference data at the moment, After outlier processing Long wave time difference data at the moment, n represents the number of periodic terms, i Indicates the serial number of the long wave time difference data period item, Indicates i The amplitude of the cycle, Indicates i The frequency of the cycle, Indicates i The phase of a cycle.

[0021] Furthermore, the method of using the extended Kalman filter algorithm to sequentially fuse the longwave time difference data and the Beidou time difference data at each moment includes sequentially performing the following steps at each moment:

[0022] (1) According to the state transfer function in the extended Kalman filter algorithm, calculate k Prediction status of Beidou time difference data at the moment and k Prediction status of long wave time difference data ;

[0023] (2) Calculate separately k Prediction covariance matrix of Beidou time difference data and k Prediction covariance matrix of long wave time difference data ;

[0024] (3) Obtain k Observation value of Beidou time difference data at the moment and k Observation value of long wave time difference data ;

[0025] (4) Combined with the predicted status and predicted status , calculate the observed value The difference between the corresponding predicted value , and the observed values The difference between the corresponding predicted value ;

[0026] (5) Calculate separately k Kalman gain of long-wave time difference data and k Kalman gain of Beidou time difference data ;

[0027] (6) Combined with the predicted status and predicted status ,difference and Difference , and the Kalman gain and Kalman gain , respectively calculated k Update status estimation of Beidou time difference data at the moment and k Update state estimation of long wave time difference data ;

[0028] (7) Combined with the prediction covariance matrix and the prediction covariance matrix , and the Kalman gain and Kalman gain , respectively calculated k Covariance matrix of Beidou time difference data after updating and k The covariance matrix of the updated long wave time difference data ;

[0029] (8) According to k -1 and the observed residuals of the Beidou time difference data before that, and k The observation residuals of the long wave time difference data at time -1 and before are calculated separately k Observation residual variance of Beidou time difference data and k The observed residual variance of long wave time difference data ;

[0030] (9) Combined with the observed residual variance and the observed residual variance , respectively calculated k Measurement noise covariance of Beidou time difference data and k The noise covariance of long wave time difference data ;

[0031] (10) Combined with updated state estimation and update the state estimate , and the covariance matrix and the covariance matrix , calculated k Always integrate the results.

[0032] Furthermore, the calculation k Prediction status of Beidou time difference data at the moment and k Prediction status of long wave time difference data When , the corresponding predicted state is calculated using the following formula:

[0033]

[0034] in, express k The predicted state at the moment, represents the state transfer function in the extended Kalman filter algorithm, express k -1 The predicted state at time instant;

[0035] calculate k Prediction covariance matrix of Beidou time difference data and kPrediction covariance matrix of long wave time difference data The formula is:

[0036]

[0037]

[0038] in, express k The covariance matrix of the Beidou time difference data after updating at -1 time, represents the noise covariance matrix during the Beidou time difference data collection process, express k The covariance matrix of the long wave time difference data after updating at -1 time, represents the noise covariance matrix during the long wave time difference data acquisition process;

[0039] Get k Observation value of Beidou time difference data at the moment and k Observation value of long wave time difference data The formula is:

[0040]

[0041] in, represents the observation matrix, express k The real status of Beidou time difference data at all times, express k The observation noise of Beidou time difference data at the moment, express k The real status of long wave time difference data at all times, express k Observation noise of Beidou time difference data;

[0042] Calculate observations The difference between the corresponding predicted value , and the observed values The difference between the corresponding predicted value The formula is:

[0043]

[0044] calculate k Kalman gain of long-wave time difference data and k Kalman gain of Beidou time difference data The formula is:

[0045]

[0046] in, express k The predicted covariance matrix of Beidou time difference data at the moment, express k The prediction covariance matrix of long wave time difference data at time, express k The observation noise covariance of Beidou time difference data at time -1, express k The observation noise covariance of the long-wave time difference data at time -1;

[0047] calculate k Update status estimation of Beidou time difference data at the moment and k Update state estimation of long wave time difference data The formula is:

[0048]

[0049] calculate k Covariance matrix of Beidou time difference data after updating and k The covariance matrix of the updated long wave time difference data The formula is:

[0050]

[0051] calculate k Observation residual variance of Beidou time difference data and k The observed residual variance of long wave time difference data The formula is:

[0052]

[0053] in, express The observation residual of Beidou time difference data at the moment, express The observation residual of long-wave time difference data at time, ;

[0054] calculate k Measurement noise covariance of Beidou time difference data and k The noise covariance of long wave time difference data The formula is:

[0055]

[0056] in, represents the observation noise covariance matrix of Beidou time difference data, represents the observation noise covariance matrix of the longwave time difference data;

[0057] Calculated k The formula for the moment fusion result is:

[0058]

[0059] in, express k The estimated value of the fusion result at the moment, express k The estimated value of the Beidou time difference data at the moment. express k The state estimate of the long wave time difference data at time, Represents the weight of the Beidou time difference data set, Represents the weight of the longwave time difference data set.

[0060] In the second aspect, the present application proposes a Beidou and longwave fusion timing system, comprising:

[0061] A data acquisition module is used to obtain a long-wave time difference data set and a Beidou time difference data set; each long-wave time difference data in the long-wave time difference data set is a time difference value between the 1PPS signals respectively output by the long-wave timing receiver and the rubidium atomic clock at each moment, and each Beidou time difference data in the Beidou time difference data set is a time difference value between the 1PPS signals respectively output by the Beidou timing receiver and the rubidium atomic clock at each moment;

[0062] The data processing module is used to process the outliers of the long-wave time difference data and the Beidou time difference data respectively, and to correct the long-wave time difference data to reduce the fluctuation of the long-wave time difference data;

[0063] A fusion module is used to use an extended Kalman filter algorithm to fuse the long-wave time difference data and the Beidou time difference data at each moment in turn, and obtain a fusion result set including the fusion result at each moment;

[0064] The timing module is used to complete the timing according to the fusion result set.

[0065] In the third aspect, the present application proposes an electronic device, comprising: a memory, one or more processors; the memory is coupled to the processor; wherein the memory stores computer program code, the computer program code includes computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the steps of the above-mentioned Beidou and longwave fusion timing method.

[0066] In a fourth aspect, the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the above-mentioned Beidou and longwave fusion timing method.

[0067] Compared with the prior art, this application has the following beneficial effects:

[0068] The present application proposes a Beidou and long-wave fusion timing method, obtains a long-wave time difference data set and a Beidou time difference data set, first performs outlier processing on the long-wave time difference data and the Beidou time difference data respectively, and corrects the long-wave time difference data to reduce the fluctuation of the long-wave time difference data, and removes outliers to ensure the accuracy of subsequent timing, and then uses the extended Kalman filter algorithm to fuse the long-wave time difference data and the Beidou time difference data at each moment in turn, and finally completes the timing according to the fusion result set. The present application integrates the advantages of the Beidou satellite navigation system and the long-wave navigation technology, and adopts the extended Kalman filter algorithm to significantly improve the system fusion timing accuracy and timing stability. At the same time, in view of the problems of poor long-wave timing accuracy and large data fluctuations, it is proposed to correct the long-wave time difference data, realize more efficient data processing and error correction, and the obtained fusion result is far better than the result of using a single timing system. Since the signals from Beidou and long-wave are received and processed at the same time, in an environment where Beidou signals are limited, long-wave can be used as a supplement to ensure the continuity of timing, thereby improving the stability of the system, and the timing result is better than the result of single long-wave operation. Similarly, when the long-wave signal is invalid, it can still provide higher timing accuracy and stability than a single Beidou. Therefore, the fusion timing method of this application is applicable to a variety of complex environments, can be extended to the timing fusion of different time sources, and can also be extended to multiple application fields such as transportation, power communication, etc., providing a reliable solution for high-precision timing needs.

[0069] This application also proposes a Beidou and longwave fusion timing system, an electronic device and a computer storage medium, which have all the advantages of the above-mentioned Beidou and longwave fusion timing method. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0071] Figure 1 This is a schematic diagram of the first process of the Beidou and longwave fusion timing method of this application;

[0072] Figure 2This is a second flow chart of the Beidou and longwave fusion timing method of this application;

[0073] Figure 3 A schematic diagram of the BeiDou and longwave fusion timing system;

[0074] Figure 4 This is a comparison chart of four-day long wave time difference data before and after correction in the embodiment of the present application;

[0075] Figure 5 This is a stability comparison chart of four-day long wave time difference data before and after correction in the embodiment of the present application;

[0076] Figure 6 In the embodiment of the present application, when both Beidou and long wave are valid, a comparison chart of the fusion results of four-day long wave time difference data before and after correction and Beidou time difference data;

[0077] Figure 7 This is a stability comparison chart of the fusion result of the four-day long-wave time difference data before and after correction and the Beidou time difference data when both Beidou and long-wave are valid in the embodiment of the present application;

[0078] Figure 8 In the embodiment of the present application, the first two days are valid, and the Beidou time difference data becomes valid and the long-wave time difference data is invalid in the last two days. The comparison chart of the fusion results of the four-day long-wave time difference data before and after correction and the Beidou time difference data;

[0079] Fig. 9 In the embodiment of the present application, the first two days are valid, and the Beidou time difference data becomes valid and the long-wave time difference data is invalid in the last two days. The stability comparison chart of the fusion result of the four-day long-wave time difference data before and after correction and the Beidou time difference data is shown;

[0080] Fig.10 In the embodiment of the present application, the first two days are valid, and the long-wave time difference data becomes valid and the Beidou time difference data is invalid in the last two days. The comparison chart of the fusion results of the four-day long-wave time difference data before and after correction and the Beidou time difference data;

[0081] Fig.11 In the embodiment of the present application, the first two days are valid, and the long-wave time difference data becomes valid and the Beidou time difference data is invalid in the last two days. The stability comparison chart of the fusion result of the four-day long-wave time difference data before and after correction and the Beidou time difference data is shown;

[0082] Fig.12 This is a schematic diagram of the Beidou and longwave fusion timing system of this application. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0084] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0085] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0086] In the description of the embodiments of the present application, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the invented product is usually placed when used. It is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0087] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0088] In the description of the embodiments of the present application, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0089] The Beidou satellite navigation system has problems of satellite denial or failure in complex scenarios, such as urban canyons, long or large tunnels, three-dimensional transportation, underground parking lots, etc. This is mainly because in these environments, Beidou satellite signals are interfered by shielding, attenuation and multipath problems, and cannot provide accurate and reliable positioning services, and it is difficult to form a seamless positioning closed loop indoors and outdoors. Specifically, the signal strength of Beidou satellite signals in complex terrains such as urban canyons and tunnels will be significantly weakened or even completely disappear due to the obstruction and reflection of buildings. In addition, the multipath effect will also cause signal distortion and affect positioning accuracy. These problems are particularly prominent in areas such as urban transportation and underground space utilization, which poses challenges to the application of the Beidou satellite navigation system. Long-wave signals, such as the 100kHz long-wave ground wave signals used by land-based navigation and timing systems such as Loran C, have a long wavelength, strong diffraction propagation ability along the ground, and relatively stable propagation loss and transmission characteristics. Therefore, they are particularly suitable for long-distance, high-precision radio navigation and timing systems. However, the propagation delay of long-wave signals is affected by many factors such as the complex changes in electrical characteristics, and has obvious time-varying characteristics, resulting in the shortcomings of poor accuracy and large jitter in long-wave timing results. Specifically, the propagation delay of long-wave signals is related to factors such as the propagation distance, topography, earth conductivity, and atmospheric refractive index on the propagation path. The earth's electrical parameters are mainly affected by geological types and ground cover. These factors show obvious regional changes in space, and in time, they are affected by factors such as air temperature and humidity, precipitation, and stratum temperature and humidity, showing seasonal changes and diurnal changes. In addition, in different geographical locations, the propagation delay may also be affected by factors such as climate and season. These real-time changing factors make it difficult to accurately predict the long-wave propagation delay, thereby affecting the accuracy of the long-wave timing system. The measured data show that the precise propagation delay measured at the test point is not a constant value, and there will be some fluctuations within a day. The fluctuation range after filtering may exceed a certain limit (such as 60 nanoseconds). This is the result of real-time changes in factors such as meteorological conditions and electrical parameters of the propagation medium on the propagation path.

[0090] Therefore, how to effectively utilize the advantages of Beidou and long wave to make up for the shortcomings of a single timing system in a complex environment, thereby improving the accuracy and reliability of the overall timing of the system, is a difficulty that the Beidou and long wave fusion timing technology needs to solve.

[0091] Based on the above situation, the present application proposes a Beidou and longwave fusion timing method and related devices. The present application is described in detail below in conjunction with the embodiments and drawings.

[0092] like Figure 1 As shown, it is a first flow chart of the Beidou and longwave fusion timing method of the present application, which may include:

[0093] S101, obtaining a long-wave time difference data set and a Beidou time difference data set; each long-wave time difference data in the long-wave time difference data set is the time difference value between the 1PPS signals respectively output by the long-wave timing receiver and the rubidium atomic clock at each moment, and each Beidou time difference data in the Beidou time difference data set is the time difference value between the 1PPS signals respectively output by the Beidou timing receiver and the rubidium atomic clock at each moment.

[0094] In actual applications, the long-wave timing receiver receives long-wave signals and compares them with the 1PPS (One Pulse Per Second) signal generated by the internal or external rubidium atomic clock, and records the time difference data at each moment, which is recorded as the long-wave time difference data. The long-wave time difference data reflects the propagation delay of the long-wave signal and the time synchronization difference between the long-wave receiver and the rubidium atomic clock. The long-wave time difference data set contains the long-wave time difference data at multiple moments for subsequent analysis and processing. Similarly, the Beidou timing receiver receives the signal of the Beidou satellite navigation system and compares it with the 1PPS signal generated by the same rubidium atomic clock, and records the time difference data at each moment, which is recorded as the Beidou time difference data. The Beidou time difference data reflects the propagation delay of the Beidou signal, the processing delay of the Beidou receiver, and the time synchronization difference between the Beidou receiver and the rubidium atomic clock. The Beidou time difference data set also contains the Beidou time difference data at multiple moments for subsequent analysis and processing.

[0095] S102, performing outlier processing on the long-wave time difference data and the Beidou time difference data respectively, and performing correction processing on the long-wave time difference data to reduce the fluctuation of the long-wave time difference data.

[0096] In practical applications, outliers may be caused by corresponding receiver failures, signal interference, or transmission errors. Detecting and eliminating these outliers through statistical methods or machine learning algorithms can ensure the accuracy and reliability of the data. The propagation delay of long-wave signals is affected by many factors and has time-varying characteristics, which leads to fluctuations in long-wave time difference data. By modeling and analyzing the changing laws of long-wave propagation delays, the long-wave time difference data can be corrected to reduce its fluctuations and improve timing accuracy.

[0097] S103, using the extended Kalman filter algorithm to fuse the long wave time difference data and the Beidou time difference data at each moment in turn, to obtain a fusion result set including the fusion result at each moment.

[0098] The extended Kalman filter algorithm is a nonlinear filtering algorithm that can handle system state estimation problems with nonlinear characteristics. The long-wave time difference data and the Beidou time difference data can be used as observations and fused through the extended Kalman filter algorithm. The long-wave time difference data and the Beidou time difference data at each moment are fused in turn, and then the system state estimation value is updated according to the fusion result to obtain a fusion result set containing the fusion result at each moment.

[0099] S104, completing the timing according to the fusion result set.

[0100] Timing refers to determining the accurate time difference value at the current moment based on the fusion result. Each fusion result in the fusion result set corresponds to an accurate time difference estimate at a moment. By fusing long-wave time difference data and Beidou time difference data, the advantages of both can be fully utilized to improve timing accuracy. Long-wave signals have stable propagation characteristics, while Beidou signals have high-precision positioning capabilities. The fusion of the two can obtain more accurate and reliable timing results.

[0101] This application is based on the extended Kalman filter algorithm. By integrating Beidou and long wave, it can effectively make up for the shortcomings and risks of a single timing system in a complex environment, and effectively improve the overall timing performance and reliability of the timing system. At the same time, correcting the long wave time difference data before fusion can further improve the accuracy and stability of the fusion, which has significant beneficial effects and application value.

[0102] like Figure 2 As shown, it is a second flow chart of the Beidou and longwave fusion timing method of the present application, which may include:

[0103] S201, time difference data collection.

[0104] As a system for realizing the Beidou and longwave fusion timing method of the present application, Figure 3 The figure shows a schematic diagram of the Beidou and longwave fusion timing system. Figure 3In the system, the long-wave transmitter is responsible for transmitting long-wave signals, which can be transmitted stably over a long distance and are suitable for broadcasting time synchronization signals. Beidou satellites are the core components of the Beidou satellite navigation system. They orbit the earth and provide global positioning, navigation and timing services. Beidou satellites transmit accurate time information to ground users by transmitting radio signals. The long-wave antenna is used to receive signals transmitted by the long-wave transmitter, and the Beidou antenna is used to receive signals transmitted by Beidou satellites. The long-wave timing receiver is specifically used to receive and process the time synchronization signals transmitted by the long-wave transmitter and extract the time information therein. The Beidou timing receiver is specifically used to receive and process the time synchronization signals transmitted by the Beidou satellite and extract the time information therein. The rubidium atomic clock is a high-precision time standard device that uses the physical properties of rubidium atoms to generate stable time signals. The rubidium atomic clock can be used as a local time reference to compare and calibrate with the received external time signal. The multi-channel time interval counter module is used to measure the time difference between the Beidou timing receiver and the 1PPS signal output by the rubidium atomic clock, as well as the time difference between the long-wave timing receiver and the 1PPS signal output by the rubidium atomic clock, and transmit these time difference data to the fusion module. After the signal connection is normal, the fusion module will start collecting time difference data.

[0105] S202, time difference data preprocessing.

[0106] Since the multi-channel time interval counter module has many uncontrollable factors in the process of measuring time difference data for a long time, it may cause abnormal values ​​such as gross errors in the data, thus causing large measurement errors. At the same time, compared with the high-precision and long-term stable timing signal of Beidou, the long wave does have the problem of slightly lower accuracy and relatively large signal fluctuations. If the timing results of these two systems are directly fused, additional noise and interference may be introduced, resulting in a decrease in overall accuracy, which cannot meet the application scenarios with strict requirements for high-precision time synchronization.

[0107] Therefore, the preprocessing of time difference data includes two parts: outlier processing and long-wave time difference data correction. The following methods can be used:

[0108] (1) Outlier processing: This embodiment uses a Hampel filter to pre-process outliers.

[0109] The Hampel filter is a filter based on the median and median absolute deviation (MAD), which is designed to identify and remove outliers in time series data. It is particularly suitable for time series data. The core of the Hampel filter lies in the calculation of the median and the solution of MAD. The median represents the middle value of the data, while MAD measures the degree of dispersion between the data point and the median.

[0110] ① Median: For the Dataset with data points , the median calculation formula is as follows:

[0111]

[0112] in, Representation dataset Middle data points, Representation dataset Middle data points, Representation dataset Middle data points.

[0113] ②Median Absolute Deviation (MAD): MAD is the difference between each data point and the data set The median of the absolute differences between the medians of :

[0114]

[0115] in, Indicates data points, represents the value of the median absolute deviation, Representation dataset The median value of .

[0116] ③Hampel filter outlier judgment criteria: If the data point The outliers are identified as values ​​whose absolute deviation is divided by exceeds the threshold), then replace it with the dataset otherwise, keep the original value of the data point.

[0117]

[0118] It should be noted that the threshold in the above formula is usually The threshold is used to determine whether a data point is an outlier. In this embodiment, the threshold can be uniformly taken as Three times.

[0119] The Beidou time difference data of the collected Beidou timing receiver and rubidium atomic clock output 1PPS signal respectively , as well as the time difference data of the long-wave timing receiver and the 1PPS signal output by the rubidium atomic clock Perform outlier preprocessing and obtain and .

[0120] (2) Correction of long-wave time difference data: Through statistical analysis of a large amount of long-wave time difference data, it is found that the long-wave time difference data has obvious periodic characteristics over time. Therefore, this application corrects the long-wave time difference data by constructing a long-wave time difference correction model.

[0121] Since the long-wave time difference data has an obvious periodic fluctuation component, in theory, the timing result of the long-wave timing receiver at a fixed point, i.e., the long-wave time difference data, should be a constant. Therefore, a long-wave timing mathematical model can be constructed based on the characteristics of the long-wave timing periodic fluctuation, as shown below:

[0122]

[0123] in, represents the sampling time, express Long wave time difference data after outlier processing at all times, is a constant term, representing the long-wave time difference data of a fixed point, represents the number of periodic terms, , , Respectively represent The amplitude, frequency and phase of each cycle, represents the fitted residuals.

[0124] Therefore, removing the periodic term Part, the long wave time difference data can be corrected, as shown below:

[0125]

[0126] in, express Long wave time difference data after correction at all times.

[0127] The specific solution process is as follows:

[0128] Specific cycle items , which can be obtained through FT (Fourier Transform) time-frequency analysis. The calculation formula can be expressed in matrix form as follows:

[0129]

[0130] in, Represents the measured value, i.e., the long-wave time difference data set , Indicates Long wave time difference data at the moment.

[0131] , Represents the parameter to be estimated. Indicates The coefficients of the periodic terms, Respectively represent The phase of a periodic term.

[0132] ,

[0133] represents the coefficient matrix, where to Respectively represent the 1st to the time, to Respectively represent the 1st to the The frequency of the periodic term.

[0134] To find the parameters to be estimated The matrix equation can be transformed into the following formula:

[0135]

[0136] The least squares method can be used to solve it.

[0137] Substitute the obtained parameters into the previous formula, that is, Deduct periodic items By fitting the data, we can get the stable corrected long-wave time difference data. .like Figure 4 As shown in the figure, it is a comparison chart of the four-day long wave time difference data before and after modification. Figure 5 The following table shows the stability comparison of the four-day long-wave time difference data before and after correction. Table 1 shows the stability comparison of the four-day long-wave time difference data before and after correction. Among them, the standard deviation of the long-wave time difference data before correction is 64.3812ns, the standard deviation of the long-wave time difference data after correction is 14.1148ns, and the standard deviation of the Beidou time difference data is 11.6843ns. Figure 4 , Figure 5 As can be seen from Table 1, by establishing a long-wave time difference correction model, the fluctuation of long-wave time difference data can be effectively reduced, and the accuracy and stability of the data have been improved, which is close to the timing accuracy of Beidou. The processing here lays the foundation for subsequent fusion. Through this step of preprocessing, a long-wave time difference data set can be obtained. , providing a basis for subsequent fusion with Beidou time difference data.

[0138]

[0139] S203, establishing a Beidou and longwave fusion timing algorithm model based on the extended Kalman filter algorithm.

[0140] According to the data preprocessing result of step S202, the fusion module can obtain the time length Two sets of time difference data: one is the Beidou time difference data set , the variance is ; One is the long wave time difference data set , the variance is The data sampling period is 1 second, so the data length is .

[0141] The specific steps to build the Beidou and longwave fusion timing algorithm model based on the extended Kalman filter algorithm are as follows:

[0142] Suppose you need to estimate a certain time The time difference value can be used to Defined as an estimate of the time difference (i.e., time synchronization error). The extended Kalman filter algorithm requires a state transfer equation to predict the state at the next moment, and an observation equation to update the state using actual observation data.

[0143] In EKF (Extended Kalman Filter), the state is first predicted based on the estimate of the previous state. Assume that the state transition equation is:

[0144]

[0145] in, yes The state of the moment, yes The state of the moment, is the state transfer function in the extended Kalman filter algorithm. Since the system state has only one time difference data, at this time , is process noise, which is usually assumed to have a mean of 0 and a covariance of Gaussian white noise.

[0146] The specific algorithm of this embodiment is as follows:

[0147] (1) State prediction

[0148] The state prediction formula is: ,in, yes The predicted state at the moment, yes The predicted state at time . Therefore, express The predicted status of Beidou time difference data at all times, express The predicted status of long wave time difference data at each moment.

[0149] (2) Covariance prediction

[0150] The covariance prediction formula is:

[0151]

[0152] in, is the state transition function for the state The Jacobian matrix of , since the state transfer function , so the state transfer matrix can be obtained That is the identity matrix .

[0153] is the predicted covariance matrix, since the state transfer matrix is the identity matrix ,therefore, . is the covariance matrix of the process noise, yes The forecast covariance matrix at time t.

[0154] Then we have:

[0155]

[0156]

[0157] in, express k The predicted covariance matrix of Beidou time difference data at the moment, express k The covariance matrix of the Beidou time difference data after updating at -1 time, represents the noise covariance matrix during the Beidou time difference data collection process, express k The covariance matrix of the long wave time difference data after updating at -1 time, Represents the noise covariance matrix during the longwave time difference data acquisition process. and It is determined by actual system modeling and can be set to zero if there is no external interference.

[0158] (3) Observation update

[0159] The observation equation is:

[0160]

[0161] in, express kThe observed value of Beidou time difference data at the moment, express k The observed value of long wave time difference data at time, Represents the observation matrix, which indicates how to change the state of the system Get the observed values. express k The observation noise of Beidou time difference data at the moment, express k Observation noise of Beidou time difference data at all times. and It is usually assumed to be Gaussian white noise with a mean of 0. Since the Beidou observation noise and the long-wave observation noise are independent of each other, , , is the variance of Beidou time difference data, is the variance of the longwave time difference data.

[0162] In this application, since the system state has only one time difference data, but there are two observations (long wave data and Beidou data are both directly observed), the observation matrix of the two sets of data is , can be regarded as the unit matrix. express k The real status of Beidou time difference data at all times, express k The true status of long wave time difference data at all times.

[0163] (4) Observation residuals

[0164] For Beidou time difference data collection and long wave time difference data set , respectively calculate the residuals:

[0165]

[0166] in, Represents the observed value The difference between the corresponding predicted value, Represents the observed value The difference between the two sets of observed data and the corresponding predicted values, that is, the deviation between the two sets of observed data and the model prediction.

[0167] (5) Kalman gain

[0168] For Beidou time difference data collection and long wave time difference data set , calculate their Kalman gains respectively:

[0169]

[0170] in, express k The Kalman gain of long-wave time difference data at the moment, express k The Kalman gain of the Beidou time difference data at the moment determines the update weight of the current observation information on the state estimation. The Kalman gain reflects the relationship between the uncertainty of the predicted state and the observation noise. The larger the Kalman gain, the greater the correction effect of the observation information on the estimation. express k The predicted covariance matrix of Beidou time difference data at the moment, express k The prediction covariance matrix of the long-wave time difference data at each moment reflects the estimated uncertainty of the predicted time difference. It reflects the mapping relationship from observation value to state value. express k The observation noise covariance of Beidou time difference data at time -1, express k The observation noise covariance of the long-wave time difference data at time -1 reflects the uncertainty of each observation source.

[0171] (6) Update state estimation

[0172] The state estimation update formula is:

[0173]

[0174] in, express k The update status of Beidou time difference data is estimated at the moment. express k The updated state estimate of the longwave time difference data at time instant is a combination of the predicted time difference and the observed residuals of the two sets of data.

[0175] (7) Update the covariance matrix

[0176] The covariance matrix update formula is:

[0177]

[0178] in, express k The covariance matrix of the Beidou time difference data after updating, express k The covariance matrix after the long-wave time difference data is updated, that is, the uncertainty of the state estimation after combining the observed data. and Determines their respective contributions to the state estimation, and Ensure that the covariance matrix of the state estimate remains of appropriate dimensions.

[0179] (8) Observation residual variance

[0180] The variance estimation formula of the observed residual is:

[0181]

[0182] in, express k The observation residual variance of Beidou time difference data at the moment, express k The observation residual variance of the long-wave time difference data reflects the size of the observation residual and is used to evaluate the quality of the observation data. express The observation residual of Beidou time difference data at the moment, express The observation residual of long-wave time difference data at time, .

[0183] (9) Update noise covariance

[0184] The noise covariance update formula is:

[0185]

[0186] in, express k The noise covariance of Beidou time difference data at the moment, express k The measured noise covariance of the long-wave time difference data at the current moment represents the uncertainty of the observed data at the current moment.

[0187] (10) Time fusion result output

[0188] The fusion result at this moment will be a weighted average of the two data sources:

[0189]

[0190] in, express k The estimated value of the fusion result at the moment, express k The estimated value of the Beidou time difference data at the moment. express k State estimate of long wave time difference data at time. Represents the weight of the Beidou time difference data set, Represents the weight of the long wave time difference data set, which can be specifically expressed as:

[0191]

[0192]

[0193] For all moments, loop through (1) to (10) in step S203. The preliminary fusion result set of Beidou and longwave data can be obtained. .

[0194] S204, data smoothing processing.

[0195] In order to reduce random fluctuations in the data, the above fusion result set In order to reduce the influence of short-term and medium-term noise in the data, this embodiment adopts a smoothing method. After the fusion step is completed, the fusion result is further smoothed by adding windows to effectively improve the stability and accuracy of the data. The formula is as follows:

[0196]

[0197] in, express The fusion result after time windowing and smoothing, Indicates the window size, express The fusion result of the moment and the two moments before it. For a given window size In this embodiment, the window size is selected ,Pick .

[0198] This application realizes the effective fusion of Beidou time difference data and long-wave time difference data by extending the Kalman filter algorithm, and further improves the accuracy and stability of time difference estimation through smoothing processing.

[0199] Verify the above method of this application:

[0200] There are three states of Beidou and longwave fusion: Beidou and longwave are both valid, Beidou is valid and longwave is invalid, Beidou is invalid and longwave is valid. In this verification, 345,600 sets of data from four days are taken to show the fusion of the three states and verify the effectiveness of the fusion algorithm.

[0201] (1) Both Beidou and longwave are valid

[0202] like Figure 6 As shown in the figure, when both Beidou and longwave are valid, the four-day longwave time difference data before and after correction and the fusion result of Beidou time difference data are compared. Figure 7 As shown in the figure, when both Beidou and long wave are valid, the stability comparison of the fusion results of the four-day long wave time difference data before and after correction and Beidou time difference data. It should be noted that due to the limited space in the figure, Figures 6 to 11In the figure, the "Beidou time difference" refers to the Beidou time difference data, and the "long wave time difference" refers to the long wave time difference data.

[0203]

[0204]

[0205] Depend on Figure 6 , Figure 7 The statistical results in Table 2 and Table 3 show that the timing accuracy and stability after fusion are higher than the timing results of a single timing system. Therefore, the Beidou and longwave fusion timing algorithm model based on the extended Kalman filter algorithm can effectively improve the accuracy and stability of the system timing results.

[0206] (2) Beidou time difference data is valid and long wave time difference data is invalid

[0207] In the selected four days of data, the first two days are when both Beidou and long wave are valid, and the last two days are when long wave is invalid, that is, when long wave is interfered, only Beidou is working. That is, there is no long wave data in the last two days.

[0208] like Figure 8 As shown in the figure, the first two days are valid, and the Beidou time difference data becomes valid and the long wave time difference data is invalid in the last two days. The four-day long wave time difference data is corrected and fused with the Beidou time difference data. Fig. 9 As shown in the figure, when the first two days are valid and the Beidou time difference data becomes valid and the long-wave time difference data is invalid in the last two days, the stability comparison chart of the fusion results of the four-day long-wave time difference data before and after correction with the Beidou time difference data is shown.

[0209]

[0210]

[0211] Depend on Figure 8 , Fig. 9 From the statistical results of Table 4 and Table 5, it can be seen that although the long-wave signal was invalid in the last two days and only Beidou was working, the time difference accuracy and stability after fusion are still higher than the timing results of a single timing system. At the same time, if the long-wave time difference data is corrected, the time difference accuracy and stability after fusion will be further improved. Therefore, the time difference fusion proposed in this application is still effective when the long wave becomes invalid, and can continue to improve the timing accuracy, stability and reliability of the system.

[0212] (3) The longwave time difference data is valid and the Beidou time difference data is invalid.

[0213] Among the selected four days of data, the first two days are valid for both Beidou time difference data and long wave time difference data, and the last two days are invalid for Beidou time difference data, that is, when Beidou is interfered, only long wave is working. That is, there is no Beidou time difference data in the last two days.

[0214] like Fig.10 As shown in the figure, the first two days are valid, and the long-wave time difference data becomes valid and the Beidou time difference data is invalid in the last two days. The four-day long-wave time difference data before and after correction and the Beidou time difference data fusion result comparison chart. Fig.11 As shown in the figure, when the first two days are valid and the long-wave time difference data becomes valid and the Beidou time difference data is invalid in the last two days, the stability comparison chart of the fusion results of the four-day long-wave time difference data before and after correction with the Beidou time difference data is shown.

[0215]

[0216]

[0217] Depend on Fig.10 , Fig.11 It can be seen from Table 6 and Table 7 that although the Beidou signal is invalid in the last two days and only long-wave is working, the time difference accuracy and stability after fusion are still good. If direct fusion is higher than a single timing system in the short term, it is better than a single long-wave system in the long term, but worse than Beidou. If the long-wave timing result is corrected before fusion, the short-term and long-term stability are higher than a single timing system. Therefore, the fusion method and long-wave correction algorithm of this application are both effective.

[0218] The above three experimental results show that the long-wave time difference data correction algorithm proposed in this application and the Beidou and long-wave fusion timing algorithm model based on the EKF algorithm can still maintain good performance in a variety of complex environments. Compared with a single timing system, the fusion timing method of this application has significantly improved the timing accuracy, and can better cope with the influence of adverse factors such as signal shielding and multipath effects. At the same time, if the long-wave time difference data is corrected before fusion, the accuracy and stability of the fusion can be further improved. These results fully verify the effectiveness and practicality of the algorithm. Therefore, this application provides a new solution for the development of high-precision time synchronization technology and has broad application prospects.

[0219] This application builds an efficient fusion platform that can collect and pre-process time difference data in real time, and realizes high-precision Beidou and long-wave timing fusion by deeply analyzing the state relationship between the Beidou system and the long-wave signal. An adaptive adjustment method is used to optimize the system noise covariance, and a fusion timing algorithm model is established on the basis of the extended Kalman filter algorithm. At the same time, in view of the problems of poor long-wave timing accuracy and large data fluctuations, a long-wave time difference data correction algorithm is proposed to achieve more efficient data processing and error correction. This method is particularly suitable for complex electromagnetic environments or areas with limited satellite signals, and provides an effective solution for high-precision timing needs. This application has the ability to adjust parameters and data pre-processing in real time in complex environments to ensure high-performance fusion timing capabilities under various conditions. In addition, seamless integration with existing infrastructure is considered, which can provide support for users in navigation, communication and other key applications. Through continuous monitoring and feedback, it also has the ability to self-optimize, can adapt to different operational requirements, and further enhance flexibility and reliability.

[0220] like Fig.12 As shown, it is a schematic diagram of the Beidou and longwave fusion timing system of the present application, which may include:

[0221] A data acquisition module is used to obtain a long-wave time difference data set and a Beidou time difference data set; each long-wave time difference data in the long-wave time difference data set is a time difference value between a 1PPS signal output by a long-wave timing receiver and a rubidium atomic clock at each moment, and each Beidou time difference data in the Beidou time difference data set is a time difference value between a 1PPS signal output by a Beidou timing receiver and a rubidium atomic clock at each moment;

[0222] The data processing module is used to process the outliers of the long-wave time difference data and the Beidou time difference data respectively, and to correct the long-wave time difference data to reduce the fluctuation of the long-wave time difference data;

[0223] A fusion module is used to use an extended Kalman filter algorithm to fuse the long-wave time difference data and the Beidou time difference data at each moment in turn, and obtain a fusion result set including the fusion result at each moment;

[0224] The timing module is used to complete the timing according to the fusion result set.

[0225] It should be noted that in the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the system embodiments described above are merely schematic. For example, the division of each module is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another device, or some features can be ignored or not executed. The module described as a separate component may or may not be physically separated. The component displayed as a module may be a physical unit or multiple physical units, that is, it may be located in one place, or it may be distributed in multiple different places. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0226] In addition, each module in each embodiment of the present invention may be integrated into a processing unit, each module may exist physically separately, or two or more modules may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0227] An embodiment of the present application also provides an electronic device, which may include one or more processors, a memory, and a communication interface.

[0228] The memory, the communication interface and the processor are coupled, for example, the memory, the communication interface and the processor may be coupled together via a bus.

[0229] The communication interface is used to transmit data with other devices. The memory stores computer program code. The computer program code includes computer instructions. When the computer instructions are executed by the processor, the electronic device executes the steps of the above-mentioned Beidou and longwave fusion timing method.

[0230] Wherein, the processor can be a processor or a controller, for example, a central processing unit (CPU), a general processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the present disclosure. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of DSP and microprocessors, and the like. The processor can be used to support electronic devices to execute the method steps provided in the above embodiments.

[0231] The bus may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The above bus may be divided into an address bus, a data bus, a control bus, etc.

[0232] An embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned Beidou and longwave fusion timing method are implemented.

[0233] The computer-readable storage medium involved in the present application includes random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the technical field.

[0234] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A Beidou and longwave fusion timing method, characterized in that: include: Obtain longwave time difference data set and Beidou time difference data set; Each long-wave time difference data in the long-wave time difference data set is the time difference value between the 1PPS signal output by the long-wave timing receiver and the rubidium atomic clock at each moment, and each Beidou time difference data in the Beidou time difference data set is the time difference value between the 1PPS signal output by the Beidou timing receiver and the rubidium atomic clock at each moment; The outliers of long-wave time difference data and Beidou time difference data are processed respectively, and the long-wave time difference data is corrected to reduce the fluctuation of the long-wave time difference data; The extended Kalman filter algorithm is used to fuse the long-wave time difference data and the Beidou time difference data at each moment in turn, and a fusion result set containing the fusion results at each moment is obtained; The timing is completed according to the fusion result set.

2. The Beidou and longwave fusion timing method according to claim 1 is characterized in that: After obtaining the fusion result set including the fusion result at each moment, the method further includes: The fusion result at each moment in the fusion result set is subjected to windowing and smoothing processing.

3. The Beidou and longwave fusion timing method according to claim 2 is characterized in that: The method for performing windowing and smoothing processing comprises: in, express The fusion result after windowing and smoothing at each moment, Indicates the window size, express The fusion result of the moment and its previous two moments.

4. The Beidou and longwave fusion timing method according to claim 1 is characterized in that: The method for processing outliers is to use a Hampel filter to process outliers.

5. The Beidou and longwave fusion timing method according to claim 1 is characterized in that: The method for correcting the long-wave time difference data is as follows: in, After correction processing Long wave time difference data at the moment, After outlier processing Long wave time difference data at the moment, n represents the number of periodic terms, i Indicates the serial number of the long wave time difference data period item, Indicates i The amplitude of the cycle, Indicates i The frequency of the cycle, Indicates i The phase of a cycle.

6. The Beidou and longwave fusion timing method according to claim 1, characterized in that: The method of adopting the extended Kalman filter algorithm to fuse the long wave time difference data and the Beidou time difference data at each moment in turn comprises performing the following steps at each moment in turn: (1) According to the state transfer function in the extended Kalman filter algorithm, calculate k Prediction status of Beidou time difference data at the moment and k Prediction status of long wave time difference data ; (2) Calculate separately k Prediction covariance matrix of Beidou time difference data and k Prediction covariance matrix of long wave time difference data ; (3) Obtain k Observation value of Beidou time difference data at the moment and k Observation value of long wave time difference data ; (4) Combined with the predicted status and predicted status , calculate the observed value The difference between the corresponding predicted value , and the observed values The difference between the corresponding predicted value ; (5) Calculate separately k Kalman gain of long-wave time difference data and k Kalman gain of Beidou time difference data ; (6) Combined with the predicted status and predicted status ,difference and Difference , and the Kalman gain and Kalman gain , respectively calculated k Update status estimation of Beidou time difference data at the moment and k Update state estimation of long wave time difference data ; (7) Combined with the prediction covariance matrix and the prediction covariance matrix , and the Kalman gain and Kalman gain , respectively calculated k Covariance matrix of Beidou time difference data after updating and k The covariance matrix of the updated long wave time difference data ; (8) According to k -1 and the observed residuals of the Beidou time difference data before that, and k The observation residuals of the long wave time difference data at time -1 and before are calculated separately k Observation residual variance of Beidou time difference data and k The observed residual variance of long wave time difference data ; (9) Combined with the observed residual variance and the observed residual variance , respectively calculated k Measurement noise covariance of Beidou time difference data and k The noise covariance of long wave time difference data ; (10) Combined with updated state estimation and update the state estimate , and the covariance matrix and the covariance matrix , calculated k Always integrate the results.

7. The Beidou and longwave fusion timing method according to claim 6 is characterized in that: calculate k Prediction status of Beidou time difference data at the moment and k Prediction status of long wave time difference data When , the corresponding predicted state is calculated using the following formula: in, express k The predicted state at the moment, represents the state transfer function in the extended Kalman filter algorithm, express k -1 The predicted state at time instant; calculate k Prediction covariance matrix of Beidou time difference data and k Prediction covariance matrix of long wave time difference data The formula is: in, express k The covariance matrix of the Beidou time difference data after updating at -1 time, represents the noise covariance matrix during the Beidou time difference data collection process, express k The covariance matrix of the long wave time difference data after updating at -1 time, represents the noise covariance matrix during the long wave time difference data acquisition process; Get k Observation value of Beidou time difference data at the moment and k Observation value of long wave time difference data The formula is: in, represents the observation matrix, express k The real status of Beidou time difference data at all times, express k The observation noise of Beidou time difference data at the moment, express k The real status of long wave time difference data at all times, express k Observation noise of Beidou time difference data; Calculate observations The difference between the corresponding predicted value , and the observed values The difference between the corresponding predicted value The formula is: calculate k Kalman gain of long-wave time difference data and k Kalman gain of Beidou time difference data The formula is: in, express k The predicted covariance matrix of Beidou time difference data at the moment, express k The prediction covariance matrix of long wave time difference data at time, express k The observation noise covariance of Beidou time difference data at time -1, express k The observation noise covariance of the long-wave time difference data at time -1; calculate k Update status estimation of Beidou time difference data at the moment and k Update state estimation of long wave time difference data The formula is: calculate k Covariance matrix of Beidou time difference data after updating and k The covariance matrix of the updated long wave time difference data The formula is: calculate k Observation residual variance of Beidou time difference data and k The observed residual variance of long wave time difference data The formula is: in, express The observation residual of Beidou time difference data at the moment, express The observation residual of long-wave time difference data at time, ; calculate k Measurement noise covariance of Beidou time difference data and k The noise covariance of long wave time difference data The formula is: in, represents the observation noise covariance matrix of Beidou time difference data, represents the observation noise covariance matrix of the longwave time difference data; Calculated k The formula for the moment fusion result is: in, express k The estimated value of the fusion result at the moment, express k The estimated value of the Beidou time difference data at the moment. express k The state estimate of the long wave time difference data at time, Represents the weight of the Beidou time difference data set, Represents the weight of the longwave time difference data set.

8. A Beidou and longwave fusion timing system, characterized in that: include: Data acquisition module, used to obtain long wave time difference data set and Beidou time difference data set; Each long-wave time difference data in the long-wave time difference data set is the time difference value between the 1PPS signal output by the long-wave timing receiver and the rubidium atomic clock at each moment, and each Beidou time difference data in the Beidou time difference data set is the time difference value between the 1PPS signal output by the Beidou timing receiver and the rubidium atomic clock at each moment; The data processing module is used to process the outliers of the long-wave time difference data and the Beidou time difference data respectively, and to correct the long-wave time difference data to reduce the fluctuation of the long-wave time difference data; A fusion module is used to use an extended Kalman filter algorithm to fuse the long-wave time difference data and the Beidou time difference data at each moment in turn, and obtain a fusion result set including the fusion result at each moment; The timing module is used to complete the timing according to the fusion result set.

9. An electronic device, characterized in that: include: A memory and one or more processors; the memory is coupled to the processor; wherein the memory stores computer program code, the computer program code includes computer instructions, and when the computer instructions are executed by the processor, the electronic device executes the steps of the Beidou and longwave fusion timing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the Beidou and longwave fusion timing method as described in any one of claims 1 to 7 are implemented.

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