Error assessment methods, terminals, and storage media for satellite navigation system deviation products

Abstract

This application relates to an error assessment method, terminal, and storage medium for deviation products of a satellite navigation system. The error assessment method includes: obtaining the error assessment range of the satellite navigation system deviation products; obtaining deviation product data for each satellite in the system based on the error assessment range; determining a reference satellite based on the deviation product data of each satellite; and assessing the error of deviation products of non-reference satellites in the system based on the deviation product data of each satellite and the reference satellite. The error assessment method, terminal, and storage medium for satellite navigation system deviation products provided in this application select the satellite with the smallest rate of change of deviation between epochs as the reference satellite based on the deviation product data of each satellite. Furthermore, in the error assessment process of deviation products of non-reference satellites, the median robustness method is used to preprocess the data. By eliminating mean bias in the time series and systematic bias of the reference satellite, the reliability and accuracy of the deviation product error assessment are improved.

Description

Technical Field

[0001] This application belongs to the field of satellite navigation, and in particular relates to an error assessment method, terminal and storage medium for a satellite navigation system deviation product. Background Technology

[0002] Instrument bias in Global Navigation Satellite System (GNSS) mainly refers to hardware delays present at the satellite or receiver end. This error exhibits systematicity, short-term stability, and varying magnitudes across different frequencies. It is typically eliminated by adding external products or mitigated through differential processing. However, differential processing models suffer from low observation utilization and increased correlation between observations; therefore, the first method (code bias) is usually used to process GNSS biases. GNSS bias products mainly include two categories: pseudorange-end hardware delay (code bias) and phase-end hardware delay (phase bias). Code bias and phase bias are key products for achieving high-precision navigation and positioning, and their accuracy directly affects the user's positioning results.

[0003] Existing methods for calculating code or phase deviation errors often employ the inter-satellite single-difference method. This involves selecting a reference satellite and performing a single difference with other satellites in the same system, resulting in a first-order difference. Then, a second-order difference is performed between the first-order difference results from different analysis centers to obtain a second-order difference sequence for the hardware deviation. The standard deviation of this second-order difference sequence is then used as a representation of the product's accuracy. However, this method introduces errors from the reference satellite, causing the calculated accuracy to fail to accurately describe the product's precision. Furthermore, when the reference satellite exhibits jumps or gross errors, the second-order difference method introduces these errors, which are mapped into the second-order difference sequence, severely compromising the accuracy of error results for various deviation products. Moreover, the standard deviation of the deviation product represents a statistical result of a second-order difference sequence, failing to consider the mean deviation over time and thus failing to obtain the true error over the time series. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides an error assessment method, terminal, and storage medium for satellite navigation system deviation products, thereby improving the reliability and accuracy of deviation product error assessment.

[0005] This application provides an error assessment method for deviation products of a satellite navigation system, comprising: obtaining an error assessment range for the deviation products of the satellite navigation system; obtaining deviation product data of each satellite in the system based on the error assessment range; determining a reference satellite based on the deviation product data of each satellite; and assessing the error of deviation products of non-reference satellites in the system based on the deviation product data of each satellite and the reference satellite.

[0006] In one embodiment, the error assessment range includes system type, deviation product type, assessment time, and assessment method; the step of obtaining deviation product data of each satellite in the system according to the error assessment range includes: if the assessment method is real-time assessment, then obtaining real-time product data of deviation products of each satellite in the system at the assessment time; if the assessment method is post-assessment, then obtaining historical product data of deviation products of each satellite in the system during the assessment period and third-party post-assessment product data.

[0007] In one embodiment, the step of determining the reference satellite based on the deviation product data of each satellite includes: when the evaluation method is the real-time evaluation, acquiring the change in the real-time product data of each satellite over a preset time period, and selecting the satellite with the smallest change as the reference satellite; when the evaluation method is the post-evaluation, acquiring the standard deviation of the historical product data of each satellite, and selecting the satellite with the smallest standard deviation as the reference satellite.

[0008] In one embodiment, the step of evaluating the error of the deviation products of non-reference satellites in the system based on the deviation product data of each satellite and the reference satellite includes: when the evaluation method is real-time evaluation, subtracting the real-time product data of the reference satellite from the real-time product data of each non-reference satellite to obtain the first difference real-time result of the deviation products of each non-reference satellite; obtaining the real-time average of the first difference based on the real-time first difference results of each non-reference satellite; and subtracting the real-time average of the first difference from the real-time first difference results of each non-reference satellite to obtain the real-time error of the deviation products of each non-reference satellite.

[0009] In one embodiment, the step of obtaining the real-time mean of the first difference based on the real-time results of the first difference of each non-reference star includes: preprocessing the real-time result sequence of the first difference formed by the real-time results of the first difference of each non-reference star using the median robust difference method; and obtaining the real-time mean of the first difference based on the real-time results of the first difference included in the preprocessed real-time result sequence of the first difference.

[0010] In one embodiment, the step of evaluating the error of the deviation products of each non-reference satellite based on the deviation product data of each satellite and the reference satellite includes: when the evaluation method is the ex-post evaluation, obtaining the first-order deviation ex-post result of the deviation products of each satellite epoch by epoch based on the historical product data and ex-post product data of each satellite in the evaluation period; subtracting the first-order deviation ex-post result of the reference satellite from the first-order deviation ex-post result of each non-reference satellite to obtain the second-order deviation ex-post result of each non-reference satellite epoch by epoch; obtaining the second-order deviation ex-post mean epoch by epoch based on the second-order deviation ex-post result of each non-reference satellite; and subtracting the second-order deviation ex-post mean epoch from the second-order deviation ex-post result of each non-reference satellite to obtain the error of the deviation products of each non-reference satellite at each epoch.

[0011] In one embodiment, the step of evaluating the error of the bias products of each non-reference satellite based on the bias product data of each satellite and the reference satellite includes: preprocessing the quadratic difference post-hoc results of each non-reference satellite; obtaining a sequence of quadratic difference post-hoc results for each epoch based on the quadratic difference post-hoc results of each non-reference satellite at each epoch; preprocessing the sequence of quadratic difference post-hoc results for each epoch using the median robustness method; obtaining the systematic bias value of the reference satellite epoch-by-epoch based on the quadratic difference post-hoc results included in the preprocessed quadratic difference post-hoc results sequence for each epoch; and subtracting the systematic bias value of the reference satellite from the quadratic difference post-hoc results of each non-reference satellite to obtain the preprocessed quadratic difference post-hoc results of each non-reference satellite epoch-by-epoch.

[0012] In one embodiment, the step of obtaining the post-quadratic mean of the quadratic differences on an epoch-by-epoch basis based on the post-quadratic results of the quadratic differences of the non-reference stars includes: obtaining a sequence of post-quadratic results of the quadratic differences of the non-reference stars at each epoch based on the post-quadratic results of the quadratic differences of the non-reference stars at each epoch; preprocessing the sequence of post-quadratic results of the quadratic differences of the non-reference stars using the median robust difference method; and obtaining the post-quadratic mean of the quadratic differences on an epoch-by-epoch basis based on the post-quadratic results of the quadratic differences included in the preprocessed sequence of post-quadratic results of the quadratic differences of the non-reference stars.

[0013] This application also provides a terminal, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described error evaluation method.

[0014] This application also provides a storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described error assessment method.

[0015] This application provides an error assessment method, terminal, and storage medium for deviation products of a satellite navigation system. Based on the deviation product data of each satellite, the satellite with the smallest deviation change rate between epochs is selected as the reference satellite. In the error assessment process of deviation products of non-reference satellites, the median robustness method is used to preprocess the data. By eliminating the mean deviation in the time series and the systematic deviation of the reference satellite, the reliability and accuracy of the error assessment of deviation products are improved. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the error assessment method provided in Embodiment 1 of this application;

[0017] Figure 2 This is a schematic diagram of the terminal provided in Embodiment 2 of this application. Detailed Implementation

[0018] The technical solutions of this application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this application. The word "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0019] Figure 1 This is a flowchart illustrating the error assessment method provided in Embodiment 1 of this application. Figure 1 As shown, the error assessment method of this application may include the following steps:

[0020] Step S101: Obtain the error assessment range of the satellite navigation system deviation product;

[0021] Optionally, the scope of error assessment includes system type, deviation product type, assessment time, and assessment method. System types include the US Global Positioning System (GPS), China's BeiDou Navigation Satellite System (BDS), the EU's Galileo positioning system, Russia's GLONASS, etc.; deviation product types include code deviation and phase deviation, with phase deviation including wide-lane phase deviation and narrow-lane phase deviation; assessment time includes assessment moment and assessment period; and assessment methods include real-time assessment and post-assessment.

[0022] Step S102: Based on the error assessment range, obtain the deviation product data of each satellite in the system;

[0023] In one embodiment, step S102 includes:

[0024] If the evaluation method is real-time evaluation, then the real-time product data of the deviation products of each satellite in the system at the evaluation time will be obtained.

[0025] If the evaluation method is ex post, then obtain the historical product data of the deviation products of each satellite in the system during the evaluation period, as well as the ex post product data from third parties.

[0026] Among them, real-time product data refers to deviation product data provided in real time by the deviation product service provider; historical product data refers to historical deviation product data stored on the local server of the deviation product service provider; and third-party post-event product data refers to post-event product data provided by third-party organizations (such as IGS, NASA, and other international satellite navigation service organizations). This data is generally generated 20-30 days later than historical product data, hence the name post-event product data. It is worth mentioning that, due to the coupling between narrow-lane phase deviation and satellite clock bias, satellite clock bias product data is incorporated into the narrow-lane phase deviation product data.

[0027] Step S103: Determine the reference satellite based on the deviation product data of each satellite;

[0028] In one embodiment, step S103 includes:

[0029] When the evaluation method is real-time evaluation, the change in real-time product data of each satellite over a preset time is obtained, and the satellite with the smallest change is selected as the reference satellite.

[0030] When the evaluation method is ex post, the standard deviation of the historical product data of each satellite is obtained, and the satellite with the smallest standard deviation is selected as the reference satellite.

[0031] Step S104: Based on the deviation product data of each satellite and the reference satellite, evaluate the error of the deviation products of non-reference satellites in the system.

[0032] In one embodiment, step S104 includes:

[0033] When the evaluation method is real-time evaluation, the real-time product data of the reference star is subtracted from the real-time product data of each non-reference star to obtain the first difference real-time result of the deviation product of each non-reference star.

[0034] Based on the real-time results of the first difference of each non-reference satellite, obtain the real-time mean of the first difference;

[0035] The real-time error of the deviation product of each non-reference satellite is obtained by subtracting the real-time mean of the first difference from the real-time results of each non-reference satellite.

[0036] In one embodiment, the real-time mean of the first difference is obtained based on the real-time results of the first difference of each non-reference satellite, including:

[0037] The median robust difference method is used to preprocess the real-time result sequence of the first difference from each non-reference satellite.

[0038] The real-time mean of the first difference is obtained from the first difference real-time results included in the preprocessed first difference real-time result sequence.

[0039] For example, if the number of non-reference satellites in the system is N, then the first-order difference real-time result sequence includes N first-order difference real-time results. After removing *a* outliers from these N first-order difference real-time results using the median robustness method, the preprocessed first-order difference real-time result sequence includes *Na* first-order difference real-time results. The average of these *Na* first-order difference real-time results is calculated to obtain the first-order difference real-time mean. It is worth noting that the removed *a* outliers only do not participate in the calculation of the first-order difference real-time mean; they still participate in the calculation of the real-time error of the deviation product.

[0040] In one embodiment, step S104 includes:

[0041] When the evaluation method is ex post, the first-order ex post result of the deviation product of each satellite is obtained epoch by epoch based on the historical product data and ex post product data of each satellite during the evaluation period.

[0042] Subtract the first-difference post-event result of the reference star from the first-difference post-event result of each non-reference star, and obtain the second-difference post-event result of each non-reference star on an epoch-by-epoch basis.

[0043] Based on the post-quadratic results of each non-reference star, the post-quadratic mean of the quadratic differences is obtained epoch by epoch.

[0044] The error of the deviation product of each non-reference star at each epoch is obtained by subtracting the post-quadrant mean of the quadratic difference from the post-quadrant results of each non-reference star.

[0045] Optionally, the post-difference result of the first difference is the difference between the historical product data and the post-difference product data at the same time; the post-difference mean of the second difference is the average of the post-difference results of each non-reference satellite.

[0046] In one embodiment, step S104 includes:

[0047] Preprocessing is performed on the post-difference results of each non-reference satellite:

[0048] Based on the quadratic difference post-hoc results of each non-reference star at each epoch, obtain the quadratic difference post-hoc result sequence for each epoch.

[0049] The median robustness method was used to preprocess the quadratic difference post-hoc result sequence for each epoch;

[0050] Based on the quadratic difference post-event results included in the preprocessed quadratic difference post-event result sequence at each epoch, the systematic deviation value of the reference star is obtained epoch by epoch.

[0051] The systematic bias value of the reference star is subtracted from the quadratic difference post-hoc results of each non-reference star, and the preprocessed quadratic difference post-hoc results of each non-reference star are obtained epoch by epoch.

[0052] Optionally, the systematic deviation value of the reference satellite is calculated using the following formula:

[0053]

[0054] Where, bias is the systematic deviation value of the reference star, and Nsat is the total number of non-reference stars in the system; The equivalent weight is the weight corresponding to the j-th non-reference star, which is obtained through iterative calculation of the initial weight. This is the post-hoc result of the quadratic difference of the j-th non-reference star at time i.

[0055] In one embodiment, based on the quadratic difference post-hoc results of each non-reference star, the post-hoc mean of the quadratic difference is obtained epoch-by-epoch, including:

[0056] Based on the quadratic difference post-hoc results of each non-reference star at each epoch, obtain the quadratic difference post-hoc result sequence of each non-reference star.

[0057] The median robustness method was used to preprocess the quadratic difference post-processing result sequences of each non-reference star;

[0058] Based on the quadratic difference post-event results included in the preprocessed quadratic difference post-event result sequence of each non-reference star, the quadratic difference post-event mean is obtained epoch by epoch.

[0059] Here, the quadratic difference post-event result sequence for each epoch and the quadratic difference post-event result sequence for each non-reference star represent the sequences statistically analyzed from the epoch dimension and the non-reference star dimension, respectively. For example, with K epochs and N non-reference stars, K quadratic difference post-event result sequences can be obtained from the epoch dimension, and each quadratic difference post-event result sequence includes N quadratic difference post-event results; N quadratic difference post-event result sequences can be obtained from the non-reference star dimension, and each quadratic difference post-event result sequence includes K quadratic difference post-event results. Epoch-by-epoch acquisition means that an acquisition operation is performed once at each time point. For example, with K epochs, K acquisition operations are performed. In addition, the preprocessing process of the quadratic difference post-event result sequence for each epoch and the quadratic difference post-event result sequence for each non-reference star using the median robust difference method is the same as the preprocessing process of the first-order difference real-time result sequence, and will not be repeated here.

[0060] The error assessment method provided in Embodiment 1 of this application selects the satellite with the smallest rate of change of deviation between epochs as the reference satellite based on the deviation product data of each satellite. In the error assessment process of deviation products of non-reference satellites, the median robustness method is used to preprocess the data. By eliminating the mean deviation in the time series and the systematic deviation of the reference satellite, the reliability and accuracy of the error assessment of deviation products are improved.

[0061] Figure 2 This is a schematic diagram of the terminal provided in Embodiment 2 of this application. The terminal of this application includes: a processor 110, a memory 111, and a computer program 112 stored in the memory 111 and executable on the processor 110. When the processor 110 executes the computer program 112, it implements the steps in the various error evaluation method embodiments described above, for example... Figure 1 Steps S101 to S104 are shown.

[0062] The terminal may include, but is not limited to, a processor 110 and a memory 111. Those skilled in the art will understand that... Figure 2 This is merely an example of a terminal and does not constitute a limitation on the terminal. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal may also include input / output devices, network access devices, buses, etc.

[0063] The processor 110 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0064] The memory 111 can be an internal storage unit of the terminal, such as a hard drive or RAM. The memory 111 can also be an external storage device of the terminal, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 111 can include both internal and external storage units. The memory 111 is used to store the computer program and other programs and data required by the terminal. The memory 111 can also be used to temporarily store data that has been output or will be output.

[0065] This application also provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the error evaluation method described above.

[0066] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0067] In this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, which includes not only the elements listed but also other elements not expressly listed.

[0068] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for error assessment of deviation products in a satellite navigation system, characterized in that, include: Obtain the error assessment range of the satellite navigation system deviation product; the error assessment range includes the assessment method. Based on the error assessment range, obtain the deviation product data of each satellite in the system; The reference satellite is determined based on the deviation product data of each satellite; Based on the deviation product data of each satellite and the reference satellite, evaluate the error of the deviation products of non-reference satellites in the system; The step of determining the reference satellite based on the deviation product data of each satellite includes: When the evaluation method is real-time evaluation, the change in real-time product data of each satellite over a preset time is obtained, and the satellite with the smallest change is selected as the reference satellite. When the evaluation method is a post-evaluation, the standard deviation of the historical product data of each satellite is obtained, and the satellite with the smallest standard deviation is selected as the reference satellite.

2. The error assessment method as described in claim 1, characterized in that, The error assessment scope includes system type, deviation product type, and assessment time; The step of obtaining the deviation product data of each satellite in the system based on the error assessment range includes: If the evaluation method is real-time evaluation, then the real-time product data of the deviation products of each satellite in the system at the evaluation time are obtained; If the evaluation method is a post-evaluation, then the historical product data of the deviation products of each satellite in the system during the evaluation period and the post-evaluation product data of a third party are obtained.

3. The error assessment method as described in claim 2, characterized in that, The step of evaluating the error of the non-reference satellite bias products in the system based on the bias product data of each satellite and the reference satellite includes: When the evaluation method is the real-time evaluation, the real-time product data of the reference star is subtracted from the real-time product data of each non-reference star to obtain the first difference real-time result of the deviation product of each non-reference star. Based on the real-time results of the first difference of each non-reference star, obtain the real-time average of the first difference; The real-time error of the deviation product of each non-reference satellite is obtained by subtracting the real-time mean of the first difference from the real-time result of the first difference of each non-reference satellite.

4. The error assessment method as described in claim 3, characterized in that, The step of obtaining the real-time mean of the first difference based on the real-time results of the first difference of each non-reference star includes: The median robust difference method is used to preprocess the sequence of real-time first difference results formed by the real-time first difference results of each non-reference star; The real-time mean of the first difference is obtained from the first difference real-time results included in the preprocessed first difference real-time result sequence.

5. The error assessment method as described in claim 2, characterized in that, The step of evaluating the error of the deviation products of each non-reference satellite based on the deviation product data of each satellite and the reference satellite includes: When the evaluation method is the post-evaluation, the first-order post-evaluation result of the deviation product of each satellite is obtained epoch by epoch based on the historical product data and post-evaluation product data of each satellite during the evaluation period. Subtract the first-difference post-event result of the reference star from the first-difference post-event result of each non-reference star, and obtain the second-difference post-event result of each non-reference star epoch by epoch. Based on the quadratic difference post-hoc results of each non-reference star, the quadratic difference post-hoc mean is obtained epoch by epoch. The error of the deviation product of each non-reference star at each epoch is obtained by subtracting the post-quadratic mean of the quadratic difference from the post-quadratic results of each non-reference star.

6. The error assessment method as described in claim 5, characterized in that, The step of evaluating the error of the deviation products of each non-reference satellite based on the deviation product data of each satellite and the reference satellite includes: The post-hoc results of the quadratic differences of the aforementioned non-reference stars are preprocessed as follows: Based on the quadratic difference post-hoc results of each non-reference star at each epoch, obtain the sequence of quadratic difference post-hoc results of each non-reference star at each epoch. The median robustness method is used to preprocess the quadratic difference post-hoc result sequences of each non-reference star at each epoch, so as to obtain the preprocessed quadratic difference post-hoc results of each non-reference star at each epoch. Based on the post-hoc results of the quadratic difference, the systematic deviation value of the reference star is obtained epoch by epoch; Based on the quadratic difference post-hoc results of each non-reference star, the systematic deviation value of the reference star is subtracted, and the preprocessed quadratic difference post-hoc results of each non-reference star are obtained epoch by epoch.

7. The error assessment method as described in any one of claims 5 or 6, characterized in that, The step of obtaining the post-quadrant mean of the quadratic differences on an epoch-by-epoch basis based on the post-quadrant results of each non-reference star includes: Based on the quadratic difference post-hoc results of each non-reference star at each epoch, obtain the quadratic difference post-hoc result sequence of each non-reference star; The median robustness method was used to preprocess the quadratic difference post-processing result sequences of each non-reference star; Based on the quadratic difference post-event results included in the preprocessed quadratic difference post-event result sequence of each non-reference star, the quadratic difference post-event mean is obtained epoch by epoch.

8. A terminal, characterized in that, The terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the error assessment method as described in any one of claims 1 to 7.

9. A storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the error evaluation method as described in any one of claims 1 to 7.

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