A high-precision data acquisition method and system for beidou satellite navigation positioning

Abstract

The application discloses a high-precision Beidou satellite navigation positioning data collection method and system, and particularly relates to the technical field of satellite navigation positioning processing, and is used for solving the problem of increased positioning error caused by receiver hardware delay drift in the prior art, and is achieved by the following steps: synchronously collecting Beidou three-frequency observation values to generate an inter-frequency differential pseudo-range sequence; using carrier phase integral displacement to inverse a first dynamic reference value, and using coherent demodulation to extract a third-order intermodulation component to generate a second dynamic reference value; using mutual information entropy analysis to select effective reference values to calculate a residual error; calculating a deviation according to a carrier phase change rate fitting theoretical value; when time domain correlation continues to exceed a threshold value, marking a hardware drift error; and finally dynamically compensating ionospheric residual errors to generate corrected observation values, so that the hardware drift error and the ionospheric residual error are effectively separated, and the precision and reliability of long-period observation in a dynamic environment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of satellite navigation positioning processing, more particularly, the present application relates to a high-precision Beidou satellite navigation positioning data acquisition method and system. BACKGROUND

[0002] In the high-precision positioning application of the Beidou satellite navigation system, using three-frequency signals (B1I, B2a, B3I) for ionospheric delay correction can improve the positioning accuracy. The prior art eliminates the first-order error of the ionosphere by using an ionosphere-free combination model, which relies on the stability of the linear combination of the multi-frequency observation value and the receiver hardware delay. The group delay characteristics of the receiver radio frequency channel are usually regarded as fixed parameters and are compensated before leaving the factory. Such a method can meet the centimeter-level positioning requirements in static or short-time observation and has become a general solution in the field of high-precision positioning.

[0003] However, in the actual data acquisition process, the channel group delay of the receiver at different frequency bands produces non-uniform drift due to environmental factors, and the inter-frequency difference is coupled with the ionospheric correction model, resulting in residual pseudorange-related system errors in ionosphere-free combination observations, and the error is significantly amplified as the signal frequency interval increases. Since the prior art regards the channel delay as a static constant, it cannot separate the hardware drift and ionospheric residual error from the data source, which degrades the reliability of high-precision positioning in dynamic scenarios, especially affecting the accuracy convergence of long-period continuous observation. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, the present application provides a high-precision Beidou satellite navigation positioning data acquisition method and system to solve the problems raised in the background art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0006] A high-precision Beidou satellite navigation positioning data acquisition method, comprising:

[0007] S1, synchronously acquiring the original pseudorange observation value and the carrier phase observation value of the Beidou three-frequency signal, and performing inter-frequency difference processing on the original pseudorange observation value to generate an inter-frequency difference pseudorange sequence;

[0008] S2, based on the inter-frequency difference pseudorange sequence, the first dynamic reference value is obtained by integrating the carrier phase observation value, and the second dynamic reference value is obtained by coherent demodulation of the third-order intermodulation component phase difference based on the two preset frequency points in the Beidou three-frequency signal;

[0009] S3, since the hardware drift causes strong correlation between the two reference values, the inter-frequency difference residual value is calculated based on the mutual information entropy analysis to select the effective reference value;

[0010] S4, fitting the pseudo-range change theoretical value according to the short-term change rate of the carrier phase observation value, calculating the deviation of the actual change amount of the original pseudo-range observation value from the theoretical value;

[0011] S5, when the time domain correlation of the deviation and the inter-frequency difference residual calculation value continues to exceed the preset correlation threshold, marking the inter-frequency difference residual calculation value as a hardware drift dominant error;

[0012] S6, based on the hardware drift dominant error, dynamically compensating the ionospheric residual calculation value of the original pseudo-range observation value, generating a corrected pseudo-range observation value and outputting positioning solution data.

[0013] Further, the original pseudo-range observation value and the carrier phase observation value of the Beidou three-frequency signal are synchronously collected, and the original pseudo-range observation value is processed by inter-frequency difference to generate an inter-frequency difference pseudo-range sequence, including:

[0014] Synchronously capturing and tracking the pseudo-range code phase and the carrier phase of each frequency point in the Beidou three-frequency signal;

[0015] The pseudo-range observation data measured at each epoch is stored as the original pseudo-range observation value;

[0016] The carrier phase observation data measured at each epoch is stored as the carrier phase observation value;

[0017] Selecting the original pseudo-range observation values of any two different frequency points in the Beidou three-frequency signal;

[0018] Performing a per-epoch subtraction operation on the original pseudo-range observation values of the selected two different frequency points;

[0019] Generating an inter-frequency single-difference observation value sequence as the inter-frequency difference pseudo-range sequence.

[0020] Further, when selecting the original pseudo-range observation values of any two different frequency points in the Beidou three-frequency signal, the original pseudo-range observation values of the two frequency points with the largest frequency interval are preferentially selected.

[0021] Further, based on the inter-frequency difference pseudo-range sequence, a first dynamic reference value is inversely calculated through carrier phase observation value integral displacement, and a second dynamic reference value is generated by coherent demodulation to extract a third-order intermodulation component phase difference based on the two preset frequency points in the Beidou three-frequency signal, including:

[0022] Calculating the carrier phase change amount between adjacent epochs based on the carrier phase observation value;

[0023] Multiplying the carrier phase change amount by the carrier wavelength of the corresponding frequency point to obtain a carrier integral displacement amount;

[0024] Performing a sliding average filtering process on the carrier integral displacement amount to generate a first dynamic reference value;

[0025] The third-order intermodulation component signal is obtained by multiplying two preset frequency signals in the Beidou three-frequency signal;

[0026] The third-order intermodulation component signal is tracked by a phase-locked loop to obtain an instantaneous phase value;

[0027] The instantaneous phase value is converted into a phase difference sequence and processed by a low-pass filter to generate a second dynamic reference value;

[0028] When selecting the two preset frequency signals in the Beidou three-frequency signal, the signals of the two frequency points with the largest frequency interval are selected.

[0029] Further, in view of the strong correlation between the two reference values caused by hardware drift, an effective reference value is selected based on mutual information entropy analysis to calculate the frequency difference residual error value, including:

[0030] The time series data of the first dynamic reference value and the second dynamic reference value are obtained;

[0031] The joint probability distribution of the first dynamic reference value and the second dynamic reference value is calculated using a sliding window;

[0032] The mutual information entropy value between the first dynamic reference value and the second dynamic reference value is calculated based on the joint probability distribution;

[0033] The size relationship between the mutual information entropy value and the preset entropy threshold value is compared, and the dynamic reference value with a larger mutual information entropy value is selected as the effective reference value;

[0034] The frequency difference pseudo-range sequence is subtracted from the effective reference value point by point to generate a frequency difference residual error value sequence.

[0035] Further, the short-term change rate of the carrier phase observation value is used to fit the pseudo-range change theoretical value, and the deviation between the actual change amount of the original pseudo-range observation value and the theoretical value is calculated, including:

[0036] The carrier phase observation value of consecutive epochs in a preset short-term time window is extracted;

[0037] The change amount of the carrier phase observation value between adjacent epochs is calculated and converted into a distance change amount;

[0038] A polynomial fitting method is used to curve fit the distance change amount sequence to obtain a pseudo-range change theoretical value sequence;

[0039] The original pseudo-range observation value in the same time window is extracted, and the actual change amount between adjacent epochs is calculated;

[0040] The actual change amount of the original pseudo-range observation value is subtracted from the pseudo-range change theoretical value at the corresponding time point point by point to generate a deviation sequence as the calculation result.

[0041] Further, when the time-domain correlation of the bias and the inter-frequency differential residual calculation value continues to exceed the preset correlation threshold, the inter-frequency differential residual calculation value is marked as a hardware drift dominant error, including:

[0042] Obtain a bias sequence and an inter-frequency differential residual calculation value sequence;

[0043] Calculate the correlation coefficient of the bias sequence and the inter-frequency differential residual calculation value sequence using a sliding window;

[0044] Determine whether the correlation coefficient exceeds a preset correlation threshold;

[0045] When the correlation coefficient continues to exceed the preset correlation threshold for a preset number of consecutive sliding windows, mark the inter-frequency differential residual calculation value in the current sliding window as a hardware drift dominant error;

[0046] Wherein, the length of the sliding window is set to a preset length of consecutive sampling epochs.

[0047] Further, based on the ionospheric residual calculation value of the hardware drift dominant error, the original pseudorange observation value is dynamically compensated, the corrected pseudorange observation value is generated, and positioning solution data is output, including:

[0048] Obtain the hardware drift dominant error value of the current epoch;

[0049] Calculate the ionospheric residual calculation value of the current epoch;

[0050] Weighted fusion processing is performed on the ionospheric residual calculation value and the hardware drift dominant error value;

[0051] According to the weighted fusion result, a compensation amount corresponding to the current epoch is generated;

[0052] The compensation amount is applied to the original pseudorange observation value to obtain the corrected pseudorange observation value;

[0053] The corrected pseudorange observation value is input into the positioning solution process to generate positioning data.

[0054] Further, the weight coefficient of the hardware drift dominant error value in the weighted fusion processing is dynamically adjusted according to its time-domain stability.

[0055] On the other hand, the application provides a high-precision Beidou satellite navigation and positioning data acquisition system, comprising:

[0056] A signal acquisition module for synchronously acquiring original pseudorange observation values and carrier phase observation values of Beidou three-frequency signals, and performing inter-frequency differential processing on the original pseudorange observation values to generate an inter-frequency differential pseudorange sequence;

[0057] The benchmark generation module is configured to generate a first dynamic benchmark value by carrier phase observation integral displacement inversion based on the inter-frequency differential pseudo-range sequence, and generate a second dynamic benchmark value by coherent demodulation to extract a third-order intermodulation component phase difference based on two preset frequency points in the Beidou three-frequency signal;

[0058] The residual calculation module is configured to select an effective benchmark value to calculate an inter-frequency differential residual calculation value based on mutual information entropy analysis in view of strong correlation between the two benchmark values caused by hardware drift;

[0059] The bias analysis module is configured to fit a pseudo-range change theoretical value according to a short-term change rate of the carrier phase observation value, and calculate a bias between an actual change amount of the original pseudo-range observation value and the theoretical value;

[0060] The error marking module is configured to mark the inter-frequency differential residual calculation value as a hardware drift dominant error when a time domain correlation between the bias and the inter-frequency differential residual calculation value lasts more than a preset correlation threshold.

[0061] The data correction module is configured to dynamically compensate an ionospheric residual calculation value of the original pseudo-range observation value based on the hardware drift dominant error, generate a corrected pseudo-range observation value, and output positioning solution data.

[0062] Compared with the prior art, the present application has the following beneficial effects:

[0063] The present application effectively separates the hardware group delay drift and the real ionospheric residual by constructing two dynamic benchmark values and optimizing them by mutual information entropy analysis; the first benchmark value inverted by carrier phase integral displacement provides a high-precision geometric change reference, and the second benchmark value extracted by the third-order intermodulation component reflects the difference in the nonlinear characteristics of the hardware channel, thus capturing the dynamic characteristics of the inter-frequency bias from the signal source, and breaking through the limitation of regarding the hardware delay as a fixed parameter in the traditional method.

[0064] The error marking mechanism based on the time domain correlation criterion realizes accurate identification and dynamic compensation of the hardware drift error, and can reliably distinguish between the hardware dominant error and the observation noise by comparing the sustained correlation between the pseudo-range change bias and the inter-frequency residual, so as to realize targeted suppression of the residual system error in the ionosphere-free combination model, and significantly improve the precision convergence and reliability of the long-period observation data in the dynamic environment. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 The flowchart of the data acquisition method for high-precision Beidou satellite navigation and positioning of the present application;

[0066] Figure 2 The structural schematic diagram of the data acquisition system for high-precision Beidou satellite navigation and positioning of the present application. DETAILED DESCRIPTION

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] Example 1: Figure 1 This invention provides a high-precision BeiDou satellite navigation and positioning data acquisition method, comprising:

[0069] S1. Synchronously acquire the original pseudorange observation values ​​and carrier phase observation values ​​of the Beidou three-frequency signals, and perform inter-frequency differential processing on the original pseudorange observation values ​​to generate an inter-frequency differential pseudorange sequence.

[0070] S2. Based on the inter-frequency differential pseudorange sequence, the first dynamic reference value is obtained by integrating the displacement of the carrier phase observation value, and the second dynamic reference value is generated by extracting the phase difference of the third-order intermodulation components based on two preset frequency points in the Beidou three-frequency signal through coherent demodulation.

[0071] S3. Given that hardware drift leads to a strong correlation between the two reference values, the effective reference value is selected based on mutual information entropy analysis to calculate the inter-frequency difference residual value.

[0072] S4. Fit the theoretical value of pseudorange change based on the short-term rate of change of carrier phase observations, and calculate the deviation between the actual change of the original pseudorange observations and the theoretical value.

[0073] S5. When the time-domain correlation between the deviation and the inter-frequency differential residual calculation value continues to exceed the preset correlation threshold, the inter-frequency differential residual calculation value is marked as hardware drift-dominated error.

[0074] S6. Based on the hardware drift-dominant error, dynamically compensate the ionospheric residuals of the original pseudorange observations, generate the corrected pseudorange observations, and output the positioning solution data.

[0075] S1. Synchronously acquire the raw pseudorange observations and carrier phase observations of the BeiDou tri-frequency signals, and perform inter-frequency differential processing on the raw pseudorange observations to generate an inter-frequency differential pseudorange sequence. Specific implementation includes:

[0076] In the implementation of this step, firstly, the B1I, B2a and B3I signals broadcast by the Beidou satellite navigation system are synchronously collected. Through the cooperation of the radio frequency front end and the baseband signal processing part of the receiver, the code phase and carrier phase of each frequency point signal are simultaneously captured and tracked. The code phase tracking is realized by a delay-locked loop, which adjusts the phase of the local pseudo-random code to keep it aligned with the phase of the pseudo-random code in the received signal, thereby obtaining the pseudo-range observation data. The carrier phase tracking is realized by a phase-locked loop, which adjusts the frequency and phase of the local oscillator to keep it locked with the carrier frequency and phase in the received signal, thereby obtaining the carrier phase observation data. The three frequency point signal processing channels use a common time reference source, such as a clock signal provided by the same temperature-compensated crystal oscillator or oven-controlled crystal oscillator, to ensure that the sampling times of all channels are strictly synchronized.

[0077] At each sampling epoch, the measured pseudo-range observation data is stored as the original pseudo-range observation value. The original pseudo-range observation value contains geometric distance, satellite clock error, receiver clock error, ionospheric delay, tropospheric delay, and receiver hardware delay error items. At the same time, the measured carrier phase observation data is stored as the carrier phase observation value. The carrier phase observation value also contains geometric distance, clock error, and atmospheric delay error, but its measurement accuracy is much higher than that of the pseudo-range observation value, and the observation noise level is usually 2 to 3 orders of magnitude lower. When storing, the original pseudo-range observation value and the carrier phase observation value for each epoch and each frequency point are marked with the same high-precision timestamp to ensure that they are strictly aligned in time. This time alignment is crucial for the subsequent step of evaluating the pseudo-range change based on the high-precision carrier phase rate.

[0078] After completing data collection and storage, inter-frequency difference processing is performed. From the stored original pseudo-range observation values, the original pseudo-range observation values of any two different frequency points in the Beidou three-frequency signal are selected for subsequent operation. When selecting, the combination of the two frequency points with the largest frequency interval is preferred, for example, the frequency interval between the B1I frequency point (center frequency 1561.098 MHz) and the B3I frequency point (center frequency 1268.520 MHz) is about 292.578 MHz, which is larger than the interval of 353.958 MHz between B1I and B2a (center frequency 1207.140 MHz), and also larger than the interval of 61.38 MHz between B2a and B3I. The combination with the largest frequency interval is preferred because the hardware group delay difference experienced by different frequency point signals in the receiver radio frequency front end is related to the frequency interval, and the larger the frequency interval, the more significant the impact of the group delay difference on the observation value, which can be highlighted through difference processing, which is beneficial to error separation and extraction in the subsequent steps.

[0079] The selected original pseudo-range observation values of two different frequencies are subtracted by epoch. Specifically, for each same epoch, the original pseudo-range observation value of the first frequency is subtracted by the original pseudo-range observation value of the second frequency to obtain a frequency interval single-difference observation value. Repeat the subtraction operation for all epochs to generate a time-varying frequency interval single-difference observation value sequence. The sequence is the frequency interval difference pseudo-range sequence. The main purpose of the difference operation is to eliminate the common error terms in the observation values of the two frequencies, such as geometric distance, receiver clock error, satellite clock error, and tropospheric delay (which is independent of frequency), thereby significantly weakening the influence of these common errors. After difference, the sequence mainly contains the ionospheric delay difference related to frequency, observation noise, and most importantly, the bias and its variation introduced by the inconsistency of the receiver hardware channel group delay. This frequency interval difference pseudo-range sequence will be the basis data for subsequent steps to invert the hardware-related errors.

[0080] The radio frequency front end refers to the hardware part in the receiver responsible for receiving radio frequency signals, low-noise amplification, down-conversion, and analog-to-digital conversion. The signal processing unit refers to the hardware part in the receiver responsible for baseband signal processing, implementing signal capture, tracking, and observation value extraction, such as a digital signal processor or a field programmable gate array. The alignment of timestamps is achieved by generating a global interrupt signal for each sampling time, and all channels simultaneously latch the current observation value data at the interrupt trigger. The frequency interval calculation is based on the frequency specification of the Beidou satellite navigation system public service signal published by the International Telecommunication Union. The epoch-by-epoch subtraction operation is an algebraic subtraction operation directly on two observation values at the same epoch timestamp. The frequency interval single-difference observation value sequence refers to a series of frequency interval single-difference observation values arranged in time order.

[0081] S2, based on the frequency interval difference pseudo-range sequence, the first dynamic reference value is inverted by the carrier phase observation value integral displacement, and the second dynamic reference value is generated by coherent demodulation to extract the third-order intermodulation component phase difference based on the two preset frequency points in the Beidou three-frequency signal, the specific implementation includes:

[0082] In the implementation of this step, first, the first dynamic reference value is calculated based on the frequency interval difference pseudo-range sequence generated in the previous step and the stored carrier phase observation value. The continuous epoch data of the current frequency point is extracted from the stored carrier phase observation value, and the carrier phase change between adjacent epochs is calculated. The specific calculation method is to subtract the carrier phase observation value of the previous epoch from the carrier phase observation value of the next epoch to obtain the phase difference value in cycles. Since the carrier phase observation value is a continuous phase value obtained by phase-locked loop tracking, its change reflects the small change of the distance between the receiver and the satellite. The time interval of adjacent epochs is determined by the sampling rate of the receiver, for example, when the sampling rate is 1 Hz, the epoch interval is 1 second.

[0083] The calculated carrier phase change is multiplied by the carrier wavelength of the corresponding frequency point to obtain the carrier integral displacement. The carrier wavelength is determined according to the frequency specification of the Beidou satellite navigation system signal, for example, the carrier wavelength of the B1I frequency point is about 0.192 meters, the carrier wavelength of the B2a frequency point is about 0.248 meters, and the carrier wavelength of the B3I frequency point is about 0.236 meters. The multiplication operation converts the phase change from cycles to meters to obtain the distance change between the receiver and the satellite between adjacent epochs. This distance change contains the true geometric distance change and measurement noise, but due to the high precision characteristics of the carrier phase observation value, the noise level is much lower than that of the pseudo-range observation value.

[0084] The obtained carrier integral displacement is subjected to a sliding average filtering process to generate a first dynamic reference value. The window length of the sliding average filtering is adjusted according to the dynamic characteristics of the receiver and the observation environment, for example, in a static or low-speed dynamic environment, the window length can be set to 10 to 30 epochs; in a high-speed dynamic environment, the window length is correspondingly reduced. The sliding average processing takes the arithmetic mean of the carrier integral displacement of a plurality of consecutive epochs to effectively suppress random noise and highlight the true distance change trend. The sequence obtained after filtering is the first dynamic reference value, which provides a high-precision, low-noise distance change reference.

[0085] At the same time, a second dynamic reference value is generated based on the preset two frequency points in the Beidou three-frequency signal by coherent demodulation to extract the third-order intermodulation component phase difference. The signals of the two frequency points with the largest frequency interval in the Beidou three-frequency signal are selected for subsequent processing, for example, the baseband signals of the B1I and B3I two frequency points are selected. The selected signals of the two frequency points are subjected to a multiplication operation, which is implemented in a digital signal processor, and the corresponding digital baseband signal samples of the two frequency points are multiplied. The signal generated after multiplication contains a third-order intermodulation component signal, and the frequency of this component signal is the combined frequency of the two original signal frequencies.

[0086] The obtained third-order intermodulation component signal is subjected to phase-locked loop tracking to obtain its instantaneous phase value. The phase-locked loop is composed of a phase detector, a loop filter and a voltage-controlled oscillator, and the output phase of the voltage-controlled oscillator is tracked by feedback control to track the phase of the input signal. The loop bandwidth of the phase-locked loop is set according to the frequency characteristics of the third-order intermodulation component signal and the expected dynamic range, for example, set to 2 to 5 Hz. After stable tracking of the phase-locked loop, the output phase value is the instantaneous phase value of the third-order intermodulation component signal.

[0087] The acquired instantaneous phase values are converted into a phase difference sequence by calculating the difference between the instantaneous phase values at adjacent epochs. Since the instantaneous phase values can contain integer ambiguity, the phase difference calculation uses a continuous phase difference method to avoid the influence of integer jumps. The obtained phase difference sequence is processed by a low-pass filter to generate a second dynamic reference value. The low-pass filter is implemented by a digital filter, such as a Butterworth low-pass filter with a cutoff frequency of 0.1 to 0.5 Hz, which is used to suppress high-frequency noise and retain low-frequency phase changes caused by hardware characteristics. The filtered phase difference sequence reflects the phase changes caused by the nonlinear characteristics of the receiver hardware, serving as the second dynamic reference value.

[0088] The third-order intermodulation component refers to the combined frequency component generated when two or more frequency signals pass through a nonlinear system, and its order is determined by the sum of the frequency coefficients. Coherent demodulation refers to a demodulation method that uses a local reference signal to multiply the received signal. Phase-locked loop tracking refers to the precise tracking and estimation of the phase of a signal through a phase-locked loop. The instantaneous phase value refers to the phase angle value of a signal at a certain time. The phase difference sequence refers to the phase difference values at adjacent times arranged in chronological order. Low-pass filtering refers to a filtering operation that allows low-frequency signals to pass while suppressing high-frequency signals. The selection of the largest frequency interval is based on the difference in performance of hardware nonlinear characteristics at different frequencies. The larger the frequency interval, the more significant the nonlinear effect, which is beneficial for extracting hardware-related error characteristics.

[0089] S3, in view of the strong correlation between the two reference values caused by hardware drift, the effective reference value is selected based on mutual information entropy analysis to calculate the inter-frequency difference residual error value, which includes the following specific implementations:

[0090] In the implementation of this step, first, the time series data of the first dynamic reference value and the second dynamic reference value generated in the previous step are obtained (the dynamic reference value includes the first dynamic reference value and the second dynamic reference value). The two time series data have the same sampling time and the same data length, and each epoch time corresponds to a first dynamic reference value data point and a second dynamic reference value data point. The time series data are stored in the memory of the receiver and organized in the form of arrays, each array element containing a timestamp and the corresponding reference value data. During the data acquisition process, it is necessary to ensure that the two reference value sequences are time-aligned, i.e., the data points with the same timestamp represent observations at the same time.

[0091] The joint probability distribution of the first dynamic reference value and the second dynamic reference value is calculated using a sliding window method. The sliding window length is set to be no less than sixty sampling epochs, for example, it can be set to sixty epochs, one hundred epochs, or other integer values greater than or equal to sixty epochs. The sliding window moves along the time series step by step, one epoch at a time, and the statistical characteristics of the data in the current window are calculated at each window position. The calculation of the joint probability distribution is achieved by counting the joint occurrence frequency of the two reference value data falling in each value interval. Specifically, the value range of the first dynamic reference value is divided into several equally spaced intervals, and the number of intervals is determined according to the data distribution characteristics and the calculation accuracy requirements, for example, it is divided into 10 to 20 intervals. The value range of the second dynamic reference value is also divided into several equally spaced intervals, and the number of intervals can be the same as or different from that of the first reference value. Then the number of data points that fall in the corresponding interval combination of the two reference values at the same time in each window is counted, and finally the joint probability estimate value is obtained by dividing the total number of data points in the window. The boundary values of the interval division are dynamically determined according to the maximum and minimum values of the data in the window, ensuring that all data points can be included in the appropriate interval.

[0092] The mutual information entropy value between the first dynamic reference value and the second dynamic reference value is calculated based on the calculated joint probability distribution. The calculation of the mutual information entropy value is achieved by iterating and summing all possible value interval combinations, and the sum term is the joint probability multiplied by the logarithmic value of the ratio of the joint probability to the product of the marginal probabilities. The marginal probability is obtained by summing the joint probability distribution matrix by row and by column to obtain the marginal probability distribution of the first dynamic reference value and the second dynamic reference value, respectively. The natural logarithm or the logarithm with base 2 is required in the calculation process, but regardless of which logarithm base is used, it needs to be consistent throughout the calculation process. The size of the mutual information entropy value reflects the degree of statistical correlation between the two dynamic reference values, and the larger the entropy value, the stronger the correlation between the two reference values, and the smaller the entropy value, the weaker the correlation. The unit of the calculation result is bit or nat, depending on the selected logarithm base.

[0093] The calculated mutual information entropy value is compared with a preset entropy threshold. The preset entropy threshold is set in advance according to the hardware characteristics of the receiver and the observation environment, for example, it can be obtained by statistical analysis of a large amount of experimental data. The statistical analysis method includes collecting baseline value data of different types of receivers in different observation environments, calculating the mutual information entropy value of each group, and then determining a suitable threshold level according to the statistical distribution characteristics. When setting the threshold, the type of receiver, the characteristics of the observation environment, and the required detection sensitivity need to be considered, for example, a smaller threshold can be used in high-precision positioning applications to improve detection sensitivity, and a larger threshold can be used in general applications to improve anti-interference ability. When the mutual information entropy value is greater than the preset entropy threshold, it indicates that there is a strong correlation between the two dynamic baseline values, and this correlation is mainly caused by the receiver hardware drift error.

[0094] The dynamic baseline value with a larger mutual information entropy value is selected as the effective baseline value. The selection criterion is based on the comparison of the mutual information entropy values, and the baseline value with a larger mutual information entropy value indicates that it has a stronger correlation with the other baseline value, and this strong correlation reflects the significant influence of the hardware drift error, so it is more suitable as a baseline for error compensation. The effective baseline value will be used for subsequent residual calculation, which represents the main characteristics of the hardware drift error. In the selection process, if the mutual information entropy values of the two baseline values differ very little, for example, the difference is less than a preset tolerance value, then one of them can be selected as the effective baseline value, or other auxiliary judgment criteria can be used for selection.

[0095] The frequency difference pseudo-range sequence is subtracted from the selected effective baseline value point by point. The frequency difference pseudo-range sequence comes from the data sequence generated in the first step, and the effective baseline value comes from the selection result of the current step. The point-by-point subtraction operation is performed at each epoch, and the frequency difference pseudo-range sequence data value at the current epoch is subtracted from the effective baseline value data value at the same epoch. The result sequence obtained by the subtraction operation is the frequency difference residual calculation value sequence, which mainly contains random error components such as observation noise and multipath error, while the system error components such as hardware drift error have been compensated and eliminated by the effective baseline value. Before the subtraction operation, it is necessary to ensure that the two sequences are time-aligned, that is, the data points at each epoch correspond one-to-one.

[0096] The setting of the sliding window length takes into account the time-dependent characteristics of the hardware drift error, which usually has a long time constant, and thus requires a long enough observation time to accurately estimate its statistical characteristics. The window length of no less than sixty sampling epochs ensures that a long enough time period is covered at a typical sampling rate, so that the joint probability distribution can be reliably estimated. The dynamic reference values include a first dynamic reference value and a second dynamic reference value, which are generated by different physical mechanisms but are both affected by the hardware drift error, and thus exhibit statistical correlation. The mutual information entropy analysis can accurately quantify the strength of this correlation, providing a basis for selecting the most effective reference value. The entire calculation process is implemented using numerical calculation methods, and all operations are completed in a digital signal processor or a general-purpose processor, ensuring the implementation and reliability of the calculation.

[0097] The joint probability distribution refers to the probability distribution of two random variables taking each value at the same time, which is estimated by statistical methods. Mutual information entropy is an index used in information theory to measure the degree of mutual dependence between two random variables, which is calculated based on the probability distribution. The preset entropy threshold is a pre-set numerical threshold used to determine whether the correlation strength is significant, which is determined through experimental data analysis. The effective reference value is the reference value sequence selected for error compensation, which is selected based on the mutual information entropy analysis result. The point-by-point subtraction operation refers to the algebraic subtraction operation of the values at the same time in two sequences. The inter-frequency difference residual calculation value sequence refers to the difference value sequence obtained after the reference value compensation, which can be used for subsequent error analysis and processing.

[0098] S4, fit the pseudo-range change theoretical value according to the short-term change rate of the carrier phase observation value, calculate the deviation of the actual change amount of the original pseudo-range observation value from the theoretical value, which includes:

[0099] In the implementation of this step, first, the time series data of the first dynamic reference value and the second dynamic reference value generated in the previous step are obtained. These two time series data have the same sampling time and the same data length, and each epoch time corresponds to a first dynamic reference value data point and a second dynamic reference value data point. The time series data is stored in the memory of the receiver and organized in the form of an array, each array element containing a timestamp and the corresponding reference value data. During data acquisition, it is necessary to ensure that the two reference value sequences are time-aligned, i.e. the data points with the same timestamp represent observations at the same time.

[0100] The joint probability distribution of the first dynamic reference value and the second dynamic reference value is calculated using a sliding window method. The sliding window length is set to be no less than sixty sampling epochs, for example, it can be set to sixty epochs, one hundred epochs, or other integer values greater than or equal to sixty epochs. The sliding window moves along the time series step by step, one epoch at a time, and the statistical characteristics of the data in the current window are calculated at each window position. The calculation of the joint probability distribution is achieved by counting the joint occurrence frequency of the two reference value data falling in each value interval. Specifically, the value range of the first dynamic reference value is divided into several equally spaced intervals, and the number of intervals is determined according to the data distribution characteristics and the calculation accuracy requirements, for example, it is divided into 10 to 20 intervals. The value range of the second dynamic reference value is also divided into several equally spaced intervals, and the number of intervals can be the same as or different from that of the first reference value. Then the number of data points that fall in the corresponding interval combination of the two reference values at the same time in each window is counted, and finally the joint probability estimate value is obtained by dividing the total number of data points in the window. The boundary values of the interval division are dynamically determined according to the maximum and minimum values of the data in the window, ensuring that all data points can be included in the appropriate interval.

[0101] The mutual information entropy value between the first dynamic reference value and the second dynamic reference value is calculated based on the calculated joint probability distribution. The calculation of the mutual information entropy value is achieved by iterating and summing all possible value interval combinations, and the sum term is the joint probability multiplied by the logarithmic value of the ratio of the joint probability to the product of the marginal probabilities. The marginal probability is obtained by summing the joint probability distribution matrix by row and by column to obtain the marginal probability distribution of the first dynamic reference value and the second dynamic reference value, respectively. The natural logarithm or the logarithm with base 2 is required in the calculation process, but regardless of which logarithm base is used, it needs to be consistent throughout the calculation process. The size of the mutual information entropy value reflects the degree of statistical correlation between the two dynamic reference values, and the larger the entropy value, the stronger the correlation between the two reference values, and the smaller the entropy value, the weaker the correlation. The unit of the calculation result is bit or nat, depending on the selected logarithm base.

[0102] The calculated mutual information entropy value is compared with a preset entropy threshold. The preset entropy threshold is set in advance according to the hardware characteristics of the receiver and the observation environment, for example, it can be obtained by statistical analysis of a large amount of experimental data. The statistical analysis method includes collecting baseline value data of different types of receivers in different observation environments, calculating the mutual information entropy value of each group, and then determining a suitable threshold level according to the statistical distribution characteristics. When setting the threshold, the type of receiver, the characteristics of the observation environment, and the required detection sensitivity need to be considered, for example, a smaller threshold can be used in high-precision positioning applications to improve detection sensitivity, and a larger threshold can be used in general applications to improve anti-interference ability. When the mutual information entropy value is greater than the preset entropy threshold, it indicates that there is a strong correlation between the two dynamic baseline values, and this correlation is mainly caused by the hardware drift error of the receiver.

[0103] The dynamic baseline value with a larger mutual information entropy value is selected as the effective baseline value. The selection criterion is based on the comparison of the mutual information entropy values, and the baseline value with a larger mutual information entropy value indicates that it has a stronger correlation with the other baseline value, and this strong correlation reflects the significant influence of the hardware drift error, so it is more suitable as a baseline for error compensation. The effective baseline value will be used for subsequent residual calculation, which represents the main characteristics of the hardware drift error. In the selection process, if the mutual information entropy values of the two baseline values differ very little, for example, the difference is less than a preset tolerance value, then one of them can be selected as the effective baseline value, or other auxiliary judgment criteria can be used for selection.

[0104] The frequency difference pseudo-range sequence is subtracted from the selected effective baseline value point by point. The frequency difference pseudo-range sequence comes from the data sequence generated in the first step, and the effective baseline value comes from the selection result of the current step. The point-by-point subtraction operation is performed at each epoch, and the frequency difference pseudo-range sequence data value of the current epoch is subtracted from the effective baseline value data value at the same epoch. The result sequence obtained by the subtraction operation is the frequency difference residual calculation value sequence, which mainly contains random error components such as observation noise and multipath error, while the system error components such as hardware drift error have been compensated and eliminated by the effective baseline value. Before the subtraction operation, it is necessary to ensure the time alignment of the two sequences, that is, the data points at each epoch correspond one by one.

[0105] The setting of the sliding window length takes into account the time-dependent characteristics of the hardware drift error, which usually has a long time constant, and thus requires a long enough observation time to accurately estimate its statistical characteristics. The window length of no less than sixty sampling epochs ensures that a long enough time period is covered at a typical sampling rate, so that the joint probability distribution can be reliably estimated. The dynamic reference values include a first dynamic reference value and a second dynamic reference value, which are generated by different physical mechanisms but are both affected by the hardware drift error, and thus exhibit statistical correlation. The mutual information entropy analysis can accurately quantify the strength of this correlation, providing a basis for selecting the most effective reference value. The entire calculation process is implemented using numerical calculation methods, and all operations are completed in a digital signal processor or a general-purpose processor, ensuring the implementation and reliability of the calculation.

[0106] The joint probability distribution refers to the probability distribution of two random variables taking values simultaneously, which is estimated by statistical methods. The mutual information entropy is an index used in information theory to measure the degree of mutual dependence between two random variables, which is calculated based on the probability distribution. The preset entropy threshold is a pre-set numerical threshold used to determine whether the correlation strength is significant, which is determined through experimental data analysis. The effective reference value is the reference value sequence selected for error compensation, which is selected based on the mutual information entropy analysis result. The point-by-point subtraction operation refers to the algebraic subtraction operation of the values at the same time in two sequences. The inter-frequency difference residual calculation value sequence refers to the difference sequence obtained after reference value compensation, which can be used for subsequent error analysis and processing.

[0107] S5, when the time-domain correlation between the bias and the inter-frequency difference residual calculation value continues to exceed the preset correlation threshold, the inter-frequency difference residual calculation value is marked as a hardware drift dominant error, and the specific implementation includes:

[0108] When this step is implemented, first, the bias sequence generated in the previous step and the inter-frequency difference residual calculation value sequence are obtained. The bias sequence comes from the calculation result of S4, and the inter-frequency difference residual calculation value sequence comes from the calculation result of S3. The two sequences have the same time length and sampling interval, and each epoch time corresponds to a bias data point and an inter-frequency difference residual calculation value data point. When data is acquired, it is necessary to ensure that the two sequences are time-aligned, i.e. the data points at the same index position represent observations at the same time. The sequence data is stored in the memory of the receiver in the form of an array, and each element of the array contains a timestamp and the corresponding observation value data, which facilitates subsequent sliding window processing. Data alignment is achieved by matching timestamps to ensure that the data points of the two sequences at the same time can be correctly corresponded.

[0109] The sliding window method is used to calculate the correlation coefficient of the bias sequence and the frequency difference residual calculation value sequence. The sliding window length is set to a preset length of consecutive sampling epochs, for example, it can be set to 20 epochs, 30 epochs or other appropriate length values. The specific length is determined according to the characteristics of the observation data and application requirements. The sliding window moves along the time sequence step by step, moving one epoch each time, and the correlation coefficient of the two sequence data in the current window is calculated at each window position. The Pearson correlation coefficient calculation method is used to calculate the correlation coefficient. First, the mean of the two sequence data in the window is calculated, then the difference between each data point and the mean is calculated, then the sum of the products of these differences is calculated, and finally the value is standardized by dividing by the number of data points minus one and taking the square root. During the calculation process, the stability of numerical calculation needs to be ensured to avoid division by zero error or numerical overflow and other problems.

[0110] It is judged whether the calculated correlation coefficient exceeds the preset correlation threshold. The preset correlation threshold is set in advance according to the hardware characteristics of the receiver and the observation environment, for example, it can be obtained by statistical analysis of a large amount of experimental data. The statistical analysis method includes collecting bias sequence and frequency difference residual calculation value sequence data of different types of receivers in different observation environments, calculating the correlation coefficient distribution of each group of data, and then determining the appropriate threshold level according to the statistical distribution characteristics. When setting the threshold value, the type of receiver, the characteristics of the observation environment and the required detection sensitivity need to be considered. For example, in high-precision positioning applications, a higher threshold value such as 0.7 or 0.8 can be used to improve the detection specificity, and in ordinary applications, a lower threshold value such as 0.6 or 0.5 can be used to improve the detection sensitivity. The threshold value is stored in the non-volatile memory of the receiver for subsequent judgment.

[0111] When the correlation coefficient continuously exceeds the preset correlation threshold for a preset number of consecutive sliding windows, the frequency difference residual calculation value in the current sliding window is marked as a hardware drift dominant error. The preset number of consecutive sliding windows is set according to the duration characteristics of the hardware drift error, for example, it can be set to 3 consecutive windows, 5 consecutive windows or other appropriate numbers. The judgment of continuous exceeding needs to record and track the correlation coefficient of each sliding window position, maintaining a queue or array that records the correlation coefficient state of the recent windows. When the correlation coefficients of multiple consecutive windows exceed the threshold value, it is considered that there is a significant hardware drift error. The marking operation is realized by setting a flag bit in the data storage structure, adding a state flag to each frequency difference residual calculation value data point, and marking the data point that meets the condition to a special value to indicate a hardware drift dominant error. The setting of the flag bit can be realized by modifying the definition of the data structure, for example, adding an error type flag field based on the original data field.

[0112] The setting of the sliding window length takes into account the time-dependent characteristics of the hardware drift error, which requires a long enough observation time to reliably estimate the correlation coefficient. The preset length of the continuous sampling epoch ensures that a suitable time period is covered at a typical sampling rate, so as to accurately evaluate the correlation between the two sequences. For example, at a sampling rate of 1 Hz, a window length of 20 epochs corresponds to an observation time of 20 seconds, and a window length of 30 epochs corresponds to an observation time of 30 seconds, which is long enough to effectively capture the slowly varying characteristics of the hardware drift error. The calculation of the correlation coefficient uses standard statistical calculation methods to ensure the accuracy and reliability of the calculation results. Attention should be paid to the details of numerical processing during the calculation process, such as using double-precision floating-point operations to improve the calculation accuracy and avoid the accumulation of rounding errors.

[0113] The setting of the threshold and the number of consecutive windows is based on statistical analysis of a large amount of experimental data, ensuring the accuracy and reliability of the detection results. The statistical analysis process includes collecting observation data under different scenarios, calculating the distribution characteristics of the correlation coefficient, analyzing the detection effect under different combinations of threshold and consecutive window number, and finally selecting a parameter combination that balances the false alarm rate and the missed detection rate. The entire judgment process is implemented using numerical comparison and logical judgment, and all operations are completed in a digital signal processor or a general-purpose processor. The processor needs to have sufficient computing power to process real-time data streams, ensuring that all calculation and judgment operations are completed within each epoch time.

[0114] The bias sequence refers to the difference between the actual change of the original pseudorange observation value and the theoretical value of the pseudorange change, which is the result of the fourth step. The inter-frequency difference residual calculation value sequence refers to the difference between the inter-frequency difference pseudorange sequence and the effective reference value, which is the result of the third step. The Pearson correlation coefficient is a statistical index used to measure the degree of linear correlation between two variables, with a value range of -1 to +1. The preset correlation threshold is a pre-set numerical threshold used to judge whether the correlation strength is significant. The hardware drift dominant error refers to the error component mainly caused by the change in the receiver hardware characteristics. The marking operation is implemented by setting a data flag bit, which facilitates special processing of the hardware drift dominant error in subsequent steps. The entire processing process ensures accurate identification and marking of the hardware drift error, providing a basis for subsequent error compensation. The data flag bit is set in binary encoding, for example, 0 represents normal error and 1 represents hardware drift dominant error. This encoding method is simple and efficient, and facilitates fast error type identification by subsequent processing programs.

[0115] S6, based on the hardware drift dominant error, dynamically compensating the ionospheric residual calculation value of the original pseudorange observation value, generating the corrected pseudorange observation value and outputting the positioning calculation data, including:

[0116] When this step is implemented, first, the hardware drift dominant error value of the current epoch is obtained. The hardware drift dominant error value comes from the marking result of the fifth step, which is the data point extracted from the inter-frequency difference residual calculation value sequence and has been marked as a hardware drift dominant error. The acquisition process is realized by querying the flag bit in the data storage structure. When the flag bit indicates that the current data point is a hardware drift dominant error, the corresponding value is extracted as the hardware drift dominant error value of the current epoch. For epochs that have not been marked as hardware drift dominant errors, the hardware drift dominant error value can be set to zero or other default values. The data storage structure is organized in the form of an array, each element contains a timestamp, observation data and error type flag, which facilitates fast query and access.

[0117] The ionospheric residual calculation value of the current epoch is calculated. The ionospheric residual calculation value is calculated by combining the observation values of the Beidou three-frequency signal, and the non-geometric distance combination method is used. The calculation method is to linearly combine the carrier phase observation values of two different frequency points according to a certain coefficient, eliminate common error terms such as geometric distance and clock error, and retain the ionospheric delay difference term. The carrier frequency values of each frequency point are needed in the calculation process, which are determined according to the signal specification of the Beidou satellite navigation system, for example, the B1I frequency point is 1561.098MHz, the B2a frequency point is 1207.140MHz, and the B3I frequency point is 1268.520MHz. The combination coefficient is calculated based on the frequency ratio, for example, when B1I and B2a frequency points are used, the coefficient is calculated as f1² / (f1²-f2²) and -f2² / (f1²-f2²); f1 and f2 represent the carrier frequency values of the two different Beidou navigation signal frequency points selected when calculating the ionospheric residual. The calculated ionospheric residual calculation value reflects the ionospheric delay change of the current epoch, and its dimension is length unit, usually expressed in meters.

[0118] The ionospheric residual calculation value and the hardware drift dominant error value are weighted and fused. The weighted fusion processing adopts the method of weighted average, and the two input values are weighted and summed according to their weight coefficients. The determination of the weight coefficient is based on the reliability and accuracy characteristics of each value. The weight coefficient of the hardware drift dominant error value is dynamically adjusted according to its time domain stability, and the weight coefficient of the ionospheric residual calculation value is determined according to its observation accuracy and signal-to-noise ratio. The evaluation of time domain stability is realized by calculating the statistical characteristics of the hardware drift dominant error value in the last several epochs, such as calculating the standard deviation or coefficient of variation. The higher the stability, the larger the weight coefficient. The weight coefficient is limited to the range of 0 to 1, and the sum of the two weight coefficients is 1, which ensures the rationality of the weighted fusion result.

[0119] The compensation quantity is generated according to the weighted fusion result corresponding to the current epoch. The generation of the compensation quantity is realized by appropriately scaling and adjusting the weighted fusion result, and the scaling coefficient is determined according to the observation environment and the receiver state. The adjustment process includes clipping processing, which ensures that the compensation quantity does not exceed a reasonable range, for example, limiting the compensation quantity to between -0.5 meters and +0.5 meters, to avoid excessive compensation leading to distortion of the observation value. The sign and size of the compensation quantity need to match the error characteristics in the original pseudorange observation value, ensuring that the compensation operation can effectively suppress the error influence. The calculation of the compensation quantity also considers the time correlation of the error, and uses recursive filtering to smooth the changes in the compensation quantity, avoiding sharp jumps.

[0120] The compensation quantity is applied to the original pseudorange observation value to obtain the modified pseudorange observation value. The application mode is to perform algebraic subtraction operation between the compensation quantity and the original pseudorange observation value, that is, to subtract the compensation quantity from the original pseudorange observation value. Before operation, it is necessary to ensure that the compensation quantity and the original pseudorange observation value have the same dimension and unit, which are usually in meters. The modification operation is carried out independently at each epoch, and the modified pseudorange observation value retains the geometric distance information and clock error information in the original observation value, while significantly suppressing the influence of hardware drift error and ionospheric residual error. The modified observation value is stored in a special data buffer for subsequent positioning calculation.

[0121] The modified pseudorange observation value is input into the positioning calculation process to generate positioning data. The positioning calculation process uses the least squares method or Kalman filtering algorithm to calculate the three-dimensional coordinates and clock error of the receiver using the modified pseudorange observation values of multiple satellites. Auxiliary information such as satellite ephemeris data, ionospheric model parameters, and tropospheric model parameters is needed in the calculation process. Satellite ephemeris data is extracted from navigation messages to provide satellite position and clock error information. The ionospheric model uses the Klobuchar model or other improved models, and the tropospheric model uses the Saastamoinen model or the Hopfield model. The generated positioning data includes latitude and longitude coordinates, height coordinates, clock error estimates, and corresponding precision indicators such as horizontal precision factor and vertical precision factor.

[0122] The weight coefficient of the hardware drift dominant error value in the weighted fusion processing is dynamically adjusted according to the time domain stability thereof. The evaluation of the time domain stability is realized by calculating the statistical characteristics of the hardware drift dominant error value in a sliding window, for example, calculating the standard deviation, variance or coefficient of variation. The length of the sliding window is determined according to the observed environment and dynamic characteristics, for example, can be set to 20 epochs or 30 epochs. The weight coefficient is positively correlated with the stability index, and the higher the stability is, the greater the weight coefficient is. The specific relationship can be realized by a linear function, for example, weight coefficient = stability index / maximum stability index, or a lookup table method is used to directly determine the weight coefficient according to the stability index range. The dynamic adjustment of the weight coefficient ensures the adaptability of the compensation strategy, and can automatically adjust the compensation strength according to the change of the observation condition.

[0123] The calculation of the ionospheric residual error value adopts a geometry-free distance combination model, which eliminates the influence of the geometric distance and clock error through linear combination of observation values of different frequencies. The acquisition of the hardware drift dominant error value is based on the error identification and marking results of the previous steps. The weighted fusion processing combines error estimation values from two different sources, fully utilizes their respective advantages to improve the compensation accuracy. The generation of the compensation amount considers the influence of the observation environment and the receiver state, ensuring the adaptability and reliability of the compensation operation. The modified pseudo-range observation value is significantly improved in observation quality through the compensation operation, providing reliable observation data input for high-precision positioning. The positioning solution adopts a standard navigation positioning algorithm, ensuring the accuracy and reliability of the positioning result. The entire processing process realizes a complete chain from error estimation to observation value correction to positioning solution, ensuring the improvement of the final positioning accuracy.

[0124] The time domain stability refers to the degree of stability of a certain quantity changing over time, which is quantitatively evaluated by statistical indicators. Weighted fusion refers to the process of summing multiple input values by weight coefficients. The compensation amount refers to the adjustment value used to correct the observation value. The positioning solution refers to the process of calculating the receiver position and clock error using observation values of multiple satellites. The dynamic adjustment mechanism of the weight coefficient ensures the optimality of the compensation strategy, which can automatically adjust the compensation strength according to the change of the observation condition, improving the robustness and reliability of the system. The entire processing process is realized in a digital signal processor or general-purpose processor, ensuring real-time performance and computational efficiency. The error compensation operation significantly improves the accuracy of the pseudo-range observation value, providing a reliable data basis for high-precision positioning applications.

[0125] Embodiment 2: Figure 2 A structural diagram of a high-precision Beidou satellite navigation positioning data acquisition system is given, and the high-precision Beidou satellite navigation positioning data acquisition system comprises:

[0126] The signal acquisition module is configured to synchronously acquire original pseudo-range observation values and carrier phase observation values of the Beidou three-frequency signal, and perform inter-frequency difference processing on the original pseudo-range observation values to generate an inter-frequency difference pseudo-range sequence.

[0127] The reference generation module is configured to generate a first dynamic reference value by integral displacement inversion of the carrier phase observation values based on the inter-frequency difference pseudo-range sequence, and generate a second dynamic reference value by coherent demodulation of a third-order intermodulation component phase difference based on two preset frequency points in the Beidou three-frequency signal.

[0128] The residual calculation module is configured to select an effective reference value to calculate an inter-frequency difference residual calculation value based on mutual information entropy analysis in view of the strong correlation between the two reference values caused by the hardware drift.

[0129] The bias analysis module is configured to fit a pseudo-range change theoretical value according to a short-term change rate of the carrier phase observation values, and calculate a bias between an actual change amount of the original pseudo-range observation values and the theoretical value.

[0130] The error marking module is configured to mark the inter-frequency difference residual calculation value as a hardware drift dominant error when a time domain correlation between the bias and the inter-frequency difference residual calculation value lasts for more than a preset correlation threshold.

[0131] The data correction module is configured to dynamically compensate an ionospheric residual calculation value of the original pseudo-range observation values based on the hardware drift dominant error, generate a corrected pseudo-range observation value, and output positioning solution data.

[0132] The calculations involved in the embodiments are all dimensionless numerical calculations, and preset parameters and threshold values in the calculations are set by a person skilled in the art according to actual conditions.

[0133] It should be noted that the application can be deployed in a device itself to realize embedded application, or run on a PC terminal or other terminal with a user interface, so as to meet various hardware environments and use requirements.

[0134] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through wireless or wired transmission. The wired transmission includes optical fiber, twisted pair, coaxial cable, etc. The wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0135] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and module can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0136] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0137] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, which can be located in one place or distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0138] In addition, each functional module in the various embodiments of the present application can be integrated in one processing module, or each module can exist physically independently, or two or more modules can be integrated in one module.

[0139] If the functions are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0140] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0141] Finally: the above is merely preferred embodiments of the present application, and is not used to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A data acquisition method for high-precision BeiDou satellite navigation and positioning, characterized in that, include: S1. Synchronously acquire the raw pseudorange observations and carrier phase observations of the BeiDou three-frequency signals, and perform inter-frequency differential processing on the raw pseudorange observations to generate an inter-frequency differential pseudorange sequence, including: For each frequency point in the BeiDou three-frequency signal, its pseudorange code phase and carrier phase are synchronously captured and tracked; The pseudorange observation data obtained at each epoch time is stored as the original pseudorange observation value; The carrier phase observation data measured at each epoch time is stored as carrier phase observation values; Select any two original pseudorange observations from the three frequency points of the BeiDou signal; The original pseudorange observations of the two selected different frequency points are subtracted epoch by epoch. Generate a sequence of inter-frequency single-difference observations as an inter-frequency difference pseudorange sequence; S2. Based on the inter-frequency differential pseudorange sequence, the first dynamic reference value is retrieved by integrating the displacement of carrier phase observations, and the second dynamic reference value is generated by extracting the phase difference of the third-order intermodulation components from two preset frequency points in the BeiDou three-frequency signal through coherent demodulation, including: Calculate the carrier phase change between adjacent epochs based on carrier phase observations; Multiply the carrier phase change by the carrier wavelength at the corresponding frequency point to obtain the carrier integral displacement. The carrier integral displacement is filtered by moving average to generate the first dynamic reference value; The third-order intermodulation component signal is obtained by multiplying two preset frequency points from the BeiDou three-frequency signal. Phase-locked loop tracking is used to obtain the instantaneous phase value of the third-order intermodulation component signal; The instantaneous phase value is converted into a phase difference sequence and then processed by low-pass filtering to generate a second dynamic reference value; When selecting signals from two preset frequency points in the BeiDou three-frequency signals, the signals from the two frequency points with the largest frequency interval are selected. S3. Given the strong correlation between the two reference values ​​due to hardware drift, the inter-frequency difference residual is calculated based on mutual information entropy analysis by selecting an effective reference value, including: Obtain time series data of the first dynamic baseline value and the second dynamic baseline value; The joint probability distribution of the first dynamic benchmark value and the second dynamic benchmark value is calculated using a sliding window. The mutual information entropy value between the first dynamic benchmark value and the second dynamic benchmark value is calculated based on the joint probability distribution. Compare the mutual information entropy value with the preset entropy threshold, and select the dynamic benchmark value with the larger mutual information entropy value as the effective benchmark value; The inter-frequency difference pseudorange sequence is subtracted point by point from the effective reference value to generate the inter-frequency difference residual value sequence; S4. Fit the theoretical value of pseudorange variation based on the short-term rate of change of the carrier phase observations, and calculate the deviation between the actual change of the original pseudorange observations and the theoretical value, including: Extract carrier phase observations for consecutive epochs within a preset short time window; Calculate the change in carrier phase observations between adjacent epochs and convert it into a distance change. A polynomial fitting method was used to perform curve fitting on the distance change sequence to obtain the pseudo-distance change theoretical value sequence. Extract the original pseudorange observations within the same time window and calculate the actual changes between adjacent epochs; The actual change in the original pseudorange observation value is subtracted point by point from the theoretical value of the pseudorange change at the corresponding time, and the deviation sequence is generated as the calculation result. S5. When the time-domain correlation between the deviation and the inter-frequency differential residual calculation value continues to exceed the preset correlation threshold, the inter-frequency differential residual calculation value is marked as hardware drift-dominated error. S6. Based on the hardware drift-dominant error, dynamically compensate the ionospheric residuals of the original pseudorange observations, generate the corrected pseudorange observations, and output the positioning solution data.

2. The data acquisition method for high-precision BeiDou satellite navigation and positioning according to claim 1, characterized in that, When selecting the original pseudorange observations of any two different frequency points in the BeiDou three-frequency signal, the original pseudorange observations of the two frequency points with the largest frequency interval should be selected first.

3. The data acquisition method for high-precision BeiDou satellite navigation and positioning according to claim 1, characterized in that, When the temporal correlation between the deviation and the inter-frequency differential residual calculation value continuously exceeds a preset correlation threshold, the inter-frequency differential residual calculation value is marked as the hardware drift-dominated error, including: Obtain the deviation sequence and the inter-frequency difference residual calculation value sequence; The correlation coefficient between the deviation sequence and the inter-frequency difference residual sequence was calculated using a sliding window method. Determine whether the correlation coefficient exceeds a preset correlation threshold; When the correlation coefficient continuously exceeds the preset correlation threshold for a preset number of consecutive sliding windows, the calculated value of the inter-frequency difference residual within the current sliding window is marked as the hardware drift-dominated error. The sliding window length is set to a preset length of continuous sampling epochs.

4. The data acquisition method for high-precision BeiDou satellite navigation and positioning according to claim 3, characterized in that, Based on the ionospheric residual calculation values ​​of the original pseudorange observations dynamically compensated for the hardware drift-dominant error, corrected pseudorange observation values ​​are generated and the positioning solution data is output, including: Obtain the hardware drift dominant error value for the current epoch; Calculate the ionospheric residual value for the current epoch; The calculated ionospheric residual values ​​are weighted and fused with the hardware drift-dominant error values. The compensation amount corresponding to the current epoch is generated based on the weighted fusion result; Applying the compensation amount to the original pseudorange observations yields the corrected pseudorange observations. The corrected pseudorange observations are input into the positioning solution process to generate positioning data.

5. The data acquisition method for high-precision BeiDou satellite navigation and positioning according to claim 4, characterized in that, In the weighted fusion process, the weighting coefficients of the hardware drift-dominant error value are dynamically adjusted based on its temporal stability.

6. A high-precision BeiDou satellite navigation and positioning data acquisition system, used to implement the high-precision BeiDou satellite navigation and positioning data acquisition method according to any one of claims 1-5, characterized in that, include: The signal acquisition module is used to synchronously acquire the original pseudorange observation values ​​and carrier phase observation values ​​of the Beidou three-frequency signals, and to perform inter-frequency differential processing on the original pseudorange observation values ​​to generate an inter-frequency differential pseudorange sequence. The reference generation module is used to invert the first dynamic reference value by integrating the displacement of the carrier phase observation value based on the inter-frequency differential pseudorange sequence, and to generate the second dynamic reference value by extracting the phase difference of the third-order intermodulation components based on two preset frequency points in the Beidou three-frequency signal through coherent demodulation. The residual calculation module is used to calculate the inter-frequency difference residual value by selecting an effective reference value based on mutual information entropy analysis, given that the strong correlation between the two reference values ​​is caused by hardware drift. The deviation analysis module is used to fit the theoretical value of pseudorange variation based on the short-term rate of change of carrier phase observations, and to calculate the deviation between the actual change of the original pseudorange observations and the theoretical value. The error marking module is used to mark the inter-frequency difference residual calculation value as hardware drift-dominated error when the time-domain correlation between the deviation and the inter-frequency difference residual calculation value continuously exceeds a preset correlation threshold. The data correction module is used to dynamically compensate the ionospheric residual calculation value of the original pseudorange observation value based on the hardware drift-dominant error, generate the corrected pseudorange observation value, and output the positioning solution data.

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