CN114624749A - Error evaluation method, terminal and storage medium of satellite navigation system deviation product - Google Patents
Error evaluation method, terminal and storage medium of satellite navigation system deviation product Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/35—Constructional details or hardware or software details of the signal processing chain
- G01S19/37—Hardware or software details of the signal processing chain
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/23—Testing, monitoring, correcting or calibrating of receiver elements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/396—Determining accuracy or reliability of position or pseudorange measurements
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Abstract
The application relates to an error evaluation method, a terminal and a storage medium of a satellite navigation system deviation product, wherein the error evaluation method comprises the following steps: acquiring an error evaluation range of a satellite navigation system deviation product; acquiring deviation product data of each satellite in the system according to the error evaluation range; determining a reference satellite according to the deviation product data of each satellite; and evaluating the error of the non-reference star deviation product in the system according to the deviation product data of each satellite and the reference star. According to the error evaluation method, the terminal and the storage medium of the satellite navigation system deviation product, the satellite with the minimum deviation change rate among epochs is selected as the reference satellite according to the deviation product data of each satellite, the data are preprocessed by adopting a median robust method in the error evaluation process of the deviation product of a non-reference satellite, and the reliability and the accuracy of the error evaluation of the deviation product are improved by eliminating the mean deviation on a time sequence and the systematic deviation of the reference satellite.
Description
Technical Field
The application belongs to the field of satellite navigation, and particularly relates to an error evaluation method, a terminal and a storage medium for a satellite navigation system deviation product.
Background
The gnss (global Navigation Satellite system) instrument deviation mainly refers to hardware delay existing in a Satellite end or a receiver end, and the partial error has the characteristics of systematicness, short-term stability, different deviation sizes at different frequency points and the like, and usually, the influence of the deviation on Navigation positioning is eliminated by adopting additional external products or weakened in a differential mode. However, the difference processing model has the disadvantages of low utilization rate of the observed values, enhanced correlation between the observed values, and the like, so the GNSS bias is usually processed by the first method. The GNSS bias product mainly includes: pseudo range end hardware delay (code bias) and phase end hardware delay (phase bias); the code deviation and the phase deviation are used as key products for realizing high-precision navigation positioning, and the precision of the code deviation and the phase deviation directly influences the positioning result of a user.
In the existing method, an inter-satellite single difference method is mostly adopted for solving errors of code deviation or phase deviation, namely, a certain reference satellite is selected, and single differences are made between other satellites in the same system and the reference satellite, which is called a primary difference result; then, performing secondary difference on primary difference results of different analysis center products to obtain a secondary difference sequence of the hardware deviation; and calculating the standard deviation of the quadratic deviation sequence as a characterization precision of the product. However, such methods introduce errors in the reference star, which results in the calculated precision not being able to accurately describe the precision of the product. In addition, when the reference star has self jump or gross error, the error of the reference star can be introduced through a quadratic difference method and is mapped into a quadratic difference sequence, and the accuracy of error results of various deviation products is seriously damaged. In addition, the standard deviation result of the deviation product represents the statistical result of a secondary difference sequence, the mean deviation on the time sequence is not considered, and the real error on the time sequence cannot be obtained.
Disclosure of Invention
In view of the above technical problems, the present application provides an error evaluation method, a terminal and a storage medium for a satellite navigation system bias product, so as to improve the reliability and accuracy of error evaluation of the bias product.
The application provides an error evaluation method of a satellite navigation system deviation product, which comprises the following steps: acquiring an error evaluation range of a satellite navigation system deviation product; acquiring deviation product data of each satellite in the system according to the error evaluation range; determining a reference star according to the deviation product data of each satellite; and evaluating the error of the deviation product of the non-reference star in the system according to the deviation product data of each satellite and the reference star.
In one embodiment, the error evaluation range includes a system type, a deviation product type, an evaluation time and an evaluation mode; the step of obtaining the deviation product data of each satellite in the system according to the error evaluation range comprises the following steps: if the evaluation mode is real-time evaluation, acquiring real-time product data of the deviation products of all satellites in the system at the evaluation moment; and if the evaluation mode is post evaluation, acquiring historical product data of the deviation products of each satellite in the system in the evaluation period and third-party post product data.
In one embodiment, the step of determining a reference satellite according to the deviation product data of each satellite includes: when the evaluation mode is the real-time evaluation, respectively acquiring the variation of the real-time product data of each satellite in preset time, and selecting the satellite with the minimum variation as the reference satellite; and when the evaluation mode is the post evaluation, respectively acquiring the standard deviation of the historical product data of each satellite, and selecting the satellite with the minimum standard deviation as the reference satellite.
In one embodiment, the step of estimating the error of the non-reference star bias product in the system based on the bias product data of each satellite and the reference star comprises: when the evaluation mode is the real-time evaluation, subtracting the real-time product data of the reference star on the basis of the real-time product data of each non-reference star to obtain a primary difference real-time result of the deviation product of each non-reference star; acquiring a primary difference real-time mean value according to the primary difference real-time result of each non-reference star; and subtracting the real-time mean value of the primary difference on the basis of the real-time result of the primary difference of each non-reference satellite to obtain the real-time error of the deviation product of each non-reference satellite.
In an embodiment, the step of obtaining a first difference real-time average value according to the first difference real-time result of each non-reference star includes: preprocessing a primary difference real-time result sequence formed by the primary difference real-time results of the non-reference stars by adopting a median robust method; and acquiring a primary difference real-time mean value according to the primary difference real-time results included in the preprocessed primary difference real-time result sequence.
In one embodiment, the step of estimating an error of the bias product of each non-reference satellite based on the bias product data of each satellite and the reference satellite includes: when the evaluation mode is the post-evaluation, acquiring a post-event result of the deviation product of each satellite by epochs according to historical product data and post-product data of each satellite in the evaluation period; subtracting the first difference post-event result of the reference star on the basis of the first difference post-event result of each non-reference star, and acquiring the second difference post-event result of each non-reference star by each epoch; acquiring a secondary post-event mean value by epochs according to the secondary post-event results of the non-reference stars; and subtracting the secondary post-event mean value on the basis of the secondary post-event result of each non-reference satellite to obtain the error of the deviation product of each non-reference satellite in each epoch.
In one embodiment, the step of estimating an error of the bias product of each non-reference satellite based on the bias product data of each satellite and the reference satellite includes: preprocessing the secondary difference post-event results of the non-reference stars: acquiring a secondary post-event result sequence of each epoch according to the secondary post-event result of each non-reference star in each epoch; preprocessing the secondary difference result sequence of each epoch by adopting a median robust method; acquiring systematic deviation values of the reference satellite by each epoch according to the secondary difference after-event results included in the preprocessed secondary difference after-event result sequence of each epoch; and subtracting the systematic deviation value of the reference star on the basis of the secondary post-errant result of each non-reference star, and acquiring the preprocessed secondary post-errant result of each non-reference star by each epoch.
In an embodiment, the step of obtaining a second-order postmortem mean value epoch by epoch according to the second-order postmortem result of each non-reference star includes: acquiring a secondary post-errant result sequence of each non-reference star according to the secondary post-errant result of each non-reference star in each epoch; preprocessing the secondary difference post-event result sequence of each non-reference star by adopting a median error-proofing method; and acquiring a secondary post-event difference mean value by epochs according to the secondary post-event difference result included in the preprocessed secondary post-event difference result sequence of each non-reference star.
The present application further provides a terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above error assessment method when executing the computer program.
The present application also provides a storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the above-mentioned error assessment method.
According to the error evaluation method, the terminal and the storage medium of the satellite navigation system deviation product, the satellite with the minimum deviation change rate among epochs is selected as the reference satellite according to the deviation product data of each satellite, the data are preprocessed by adopting a median robust method in the error evaluation process of the deviation product of a non-reference satellite, and the reliability and the accuracy of the error evaluation of the deviation product are improved by eliminating the mean deviation on a time sequence and the systematic deviation of the reference satellite.
Drawings
Fig. 1 is a schematic flowchart of an error evaluation method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a terminal according to a second embodiment of the present application.
Detailed Description
The technical solution of the present application is further described in detail with reference to the drawings and specific embodiments of the specification. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, "and/or" includes any and all combinations of one or more of the associated listed items.
Fig. 1 is a schematic flowchart of an error evaluation method according to an embodiment of the present application. As shown in fig. 1, the error evaluation method of the present application may include the steps of:
step S101: acquiring an error evaluation range of a satellite navigation system deviation product;
optionally, the error evaluation range includes a system type, a deviation product type, an evaluation time, and an evaluation mode. Wherein, the system types comprise the Global Positioning System (GPS) of the United states, the Beidou satellite navigation System (BDS) of China, the Galileo positioning system (Galileo) of the European Union, the global satellite navigation system (GLONASS) of Russia and the like; the deviation product type comprises code deviation and phase deviation, wherein the phase deviation comprises wide lane phase deviation and narrow lane phase deviation; the evaluation time comprises an evaluation moment and an evaluation time period; the evaluation mode comprises real-time evaluation and post evaluation.
Step S102: acquiring deviation product data of each satellite in the system according to the error evaluation range;
in one embodiment, step S102 includes:
if the evaluation mode is real-time evaluation, acquiring real-time product data of the deviation products of all satellites in the system at the evaluation time;
and if the evaluation mode is post evaluation, acquiring historical product data of the deviation products of each satellite in the system in the evaluation period and third-party post product data.
The real-time product data is deviation product data provided by a deviation product service provider in real time; the historical product data is historical data of the deviated product stored by the local server of the deviated product service provider; the third-party after-production data is after-production data provided by third-party organizations (such as international satellite navigation service organizations like IGS, NASA and the like), and is generally generated 20-30 days later than the historical product data, so the data is called after-production data. It is worth mentioning that the narrow lane phase deviation product data is fused with the satellite clock deviation product data because the narrow lane phase deviation is coupled with the satellite clock deviation.
Step S103: determining a reference satellite according to the deviation product data of each satellite;
in one embodiment, step S103 includes:
when the evaluation mode is real-time evaluation, the variation of the real-time product data of each satellite in preset time is respectively obtained, and the satellite with the minimum variation is selected as a reference satellite;
and when the evaluation mode is post evaluation, respectively acquiring the standard deviation of the historical product data of each satellite, and selecting the satellite with the minimum standard deviation as a reference satellite.
Step S104: and evaluating the error of the non-reference star deviation product in the system according to the deviation product data of each satellite and the reference star.
In one embodiment, step S104 includes:
when the evaluation mode is real-time evaluation, subtracting the real-time product data of the reference star on the basis of the real-time product data of each non-reference star to obtain a primary difference real-time result of the deviation product of each non-reference star;
acquiring a primary difference real-time mean value according to a primary difference real-time result of each non-reference star;
and subtracting the real-time mean value of the primary difference on the basis of the real-time result of the primary difference of each non-reference satellite to obtain the real-time error of the deviation product of each non-reference satellite.
In one embodiment, obtaining a first difference real-time average value according to a first difference real-time result of each non-reference star includes:
preprocessing a primary difference real-time result sequence formed by primary difference real-time results of the non-reference stars by adopting a median robust method;
and acquiring a primary difference real-time mean value according to the primary difference real-time result included in the preprocessed primary difference real-time result sequence.
Illustratively, the number of the non-reference stars in the system is N, the first order difference real-time result sequence includes N first order difference real-time results, a outliers in the N first order difference real-time results are removed according to a median robust method, the preprocessed first order difference real-time result sequence includes N-a first order difference real-time results, and an average value of the N-a first order difference real-time results is calculated to obtain a first order difference real-time average value. It is worth mentioning that the removed a outliers are not involved in the calculation of the difference real-time mean value once, but are still involved in the calculation of the real-time error of the deviation product.
In one embodiment, step S104 includes:
when the evaluation mode is post evaluation, acquiring a one-time post-event result of the deviation product of each satellite by epochs according to historical product data and post product data of each satellite in the evaluation period;
subtracting the first post-erratic result of the reference star on the basis of the first post-erratic result of each non-reference star, and acquiring the second post-erratic result of each non-reference star by each epoch;
acquiring a secondary post-event mean value by epochs according to the secondary post-event results of the non-reference stars;
and subtracting the secondary post-errant mean value on the basis of the secondary post-errant result of each non-reference satellite to obtain the error of the deviation product of each non-reference satellite in each epoch.
Optionally, the result after one difference is the difference between the historical product data and the after product data at the same time; the second-order difference post-event mean value is the mean value of the second-order difference post-event results of the non-reference stars.
In one embodiment, step S104 includes:
preprocessing the secondary difference post-result of each non-reference satellite:
acquiring a secondary post-errant result sequence of each epoch according to the secondary post-errant result of each non-reference star in each epoch;
preprocessing the secondary post-event result sequence of each epoch by adopting a median robust method;
acquiring systematic deviation values of the reference stars from epoch to epoch according to the secondary difference after-event results included in the secondary difference after-event result sequence after the preprocessing of each epoch;
and subtracting the systematic deviation value of the reference star on the basis of the secondary post-errant result of each non-reference star, and acquiring the preprocessed secondary post-errant result of each non-reference star by each epoch.
Optionally, the systematic deviation value of the reference star is calculated by the following formula:
wherein, bias is a systematic deviation value of a reference star, and Nsat is the total number of non-reference stars in the system;
obtaining an equivalent weight corresponding to the jth non-reference star through initial weight iterative computation;
and the result is the second post-errand result of the ith non-reference star.
In one embodiment, obtaining the second-order postmortem mean value epoch by epoch according to the second-order postmortem result of each non-reference star includes:
acquiring a secondary post-errant result sequence of each non-reference star according to the secondary post-errant result of each non-reference star in each epoch;
preprocessing the secondary difference result sequence of each non-reference star by adopting a median error-proofing method;
and acquiring a secondary post-errant mean value by epochs according to secondary post-errant results included in the preprocessed secondary post-errant result sequence of each non-reference star.
The secondary post-erratic result sequence of each epoch and the secondary post-erratic result sequence of each non-reference star respectively represent sequences counted from the epoch dimension and the non-reference star dimension, for example, K epochs and N non-reference stars can be counted from the epoch dimension to obtain K secondary post-erratic result sequences, and each secondary post-erratic result sequence comprises N secondary post-erratic results; n secondary post-errant result sequences can be obtained through statistics from the dimension of the non-reference star, and each secondary post-errant result sequence comprises K secondary post-errant results; acquiring epoch by epoch means that acquisition operation is executed once at each moment, and if K epochs are acquired, K acquisition operations are executed; in addition, the process of preprocessing the secondary post-event result sequence of each epoch and the secondary post-event result sequence of each non-reference star by using the median robust method refers to the preprocessing process of the primary difference real-time result sequence, and is not described herein again.
According to the error evaluation method provided by the embodiment of the application, the satellite with the minimum deviation change rate among epochs is selected as the reference satellite according to the deviation product data of each satellite, the data is preprocessed by adopting a median robust method in the error evaluation process of the deviation product of the non-reference satellite, and the reliability and the accuracy of the error evaluation of the deviation product are improved by eliminating the mean deviation on a time sequence and the systematic deviation of the reference satellite.
Fig. 2 is a schematic structural diagram of a terminal according to a second embodiment of the present application. The terminal of the application includes: a processor 110, a memory 111 and a computer program 112 stored in said memory 111 and executable on said processor 110. The processor 110, when executing the computer program 112, implements the steps in the various error assessment method embodiments described above, such as the steps S101 to S104 shown in fig. 1.
The terminal may include, but is not limited to, a processor 110, a memory 111. Those skilled in the art will appreciate that fig. 2 is only an example of a terminal and is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or different components, e.g., the terminal may also include input-output devices, network access devices, buses, etc.
The Processor 110 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 111 may be an internal storage unit of the terminal, such as a hard disk or a memory of the terminal. The memory 111 may also be an external storage device of the terminal, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the terminal. Further, the memory 111 may also include both an internal storage unit and an external storage device of the terminal. The memory 111 is used for storing the computer program and other programs and data required by the terminal. The memory 111 may also be used to temporarily store data that has been output or is to be output.
The present application also provides a storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the error assessment method as described above.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
As used herein, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, including not only those elements listed, but also other elements not expressly listed.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An error evaluation method for a satellite navigation system bias product, comprising:
acquiring an error evaluation range of a satellite navigation system deviation product;
acquiring deviation product data of each satellite in the system according to the error evaluation range;
determining a reference satellite according to the deviation product data of each satellite;
and evaluating the error of the non-reference star deviation product in the system according to the deviation product data of each satellite and the reference star.
2. The error evaluation method of claim 1, wherein the error evaluation range includes a system type, a deviated product type, an evaluation time, and an evaluation manner;
the step of obtaining the deviation product data of each satellite in the system according to the error evaluation range comprises the following steps:
if the evaluation mode is real-time evaluation, acquiring real-time product data of the deviation products of all satellites in the system at the evaluation moment;
and if the evaluation mode is post evaluation, acquiring historical product data of the deviation products of each satellite in the system in the evaluation period and third-party post product data.
3. The error-assessment method according to claim 2, wherein said step of determining a reference star based on said offset product data for each satellite comprises:
when the evaluation mode is the real-time evaluation, respectively acquiring the variation of the real-time product data of each satellite in preset time, and selecting the satellite with the minimum variation as the reference satellite;
and when the evaluation mode is the post evaluation, respectively acquiring the standard deviation of the historical product data of each satellite, and selecting the satellite with the minimum standard deviation as the reference satellite.
4. The error estimation method of claim 2, wherein the step of estimating the error of the non-reference star bias product in the system based on the bias product data for each satellite and the reference star comprises:
when the evaluation mode is the real-time evaluation, subtracting the real-time product data of the reference star on the basis of the real-time product data of each non-reference star to obtain a primary difference real-time result of the deviation product of each non-reference star;
acquiring a primary difference real-time mean value according to the primary difference real-time result of each non-reference star;
and subtracting the real-time mean value of the primary difference from the real-time result of the primary difference of each non-reference satellite to obtain the real-time error of the deviation product of each non-reference satellite.
5. The error-assessment method of claim 4, wherein said step of obtaining a real-time mean of the primary differences based on the real-time results of the primary differences of the non-reference stars comprises:
preprocessing a primary difference real-time result sequence formed by the primary difference real-time results of the non-reference stars by adopting a median robust method;
and acquiring a primary difference real-time mean value according to the primary difference real-time result included in the preprocessed primary difference real-time result sequence.
6. The error estimation method of claim 2, wherein the step of estimating the error of the bias product of each non-reference star based on the bias product data of each satellite and the reference star comprises:
when the evaluation mode is the post-evaluation, acquiring a post-event result of the deviation product of each satellite by epochs according to historical product data and post-product data of each satellite in the evaluation period;
subtracting the first difference post-event result of the reference star on the basis of the first difference post-event result of each non-reference star, and acquiring the second difference post-event result of each non-reference star by each epoch;
acquiring a secondary post-event mean value by epochs according to the secondary post-event results of the non-reference stars;
and subtracting the secondary post-event mean value on the basis of the secondary post-event result of each non-reference satellite to obtain the error of the deviation product of each non-reference satellite in each epoch.
7. The error estimation method of claim 6, wherein the step of estimating the error of the bias product of each non-reference star based on the bias product data of each satellite and the reference star comprises:
preprocessing the secondary miscarriage results of the non-reference stars:
acquiring a secondary post-event result sequence of each epoch according to the secondary post-event result of each non-reference star in each epoch;
preprocessing the secondary difference result sequence of each epoch by adopting a median robust method;
acquiring systematic deviation values of the reference satellite by each epoch according to the secondary difference after-event results included in the preprocessed secondary difference after-event result sequence of each epoch;
and subtracting the systematic deviation value of the reference star on the basis of the secondary post-errant result of each non-reference star, and acquiring the preprocessed secondary post-errant result of each non-reference star by each epoch.
8. The error-assessment method according to any of claims 6 or 7, wherein said step of obtaining a quadratic difference mean, epoch by epoch, from quadratic difference post-hoc results of said non-reference stars comprises:
acquiring a secondary post-errant result sequence of each non-reference star according to the secondary post-errant result of each non-reference star in each epoch;
preprocessing the secondary post-event result sequence of each non-reference star by adopting a median robust method;
and acquiring a secondary post-event difference mean value by epochs according to the secondary post-event difference result included in the preprocessed secondary post-event difference result sequence of each non-reference star.
9. A terminal, characterized in that the terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the error assessment method according to any one of claims 1 to 8 when executing the computer program.
10. A storage medium storing a computer program, characterized in that the computer program realizes the steps of the error assessment method according to any one of claims 1 to 8 when executed by a processor.
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