CN116953734A - Receiver autonomous integrity monitoring method, equipment and medium based on weighted estimation - Google Patents
Receiver autonomous integrity monitoring method, equipment and medium based on weighted estimation Download PDFInfo
- Publication number
- CN116953734A CN116953734A CN202310941810.6A CN202310941810A CN116953734A CN 116953734 A CN116953734 A CN 116953734A CN 202310941810 A CN202310941810 A CN 202310941810A CN 116953734 A CN116953734 A CN 116953734A
- Authority
- CN
- China
- Prior art keywords
- robust estimation
- determining
- prediction model
- constructing
- estimation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000012544 monitoring process Methods 0.000 title claims abstract description 39
- 239000011159 matrix material Substances 0.000 claims abstract description 42
- 230000006870 function Effects 0.000 claims description 21
- 238000005259 measurement Methods 0.000 claims description 15
- 239000013598 vector Substances 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 6
- 238000001514 detection method Methods 0.000 abstract description 17
- 230000002159 abnormal effect Effects 0.000 abstract description 12
- 238000012360 testing method Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 230000002401 inhibitory effect Effects 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 239000005433 ionosphere Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000005436 troposphere Substances 0.000 description 1
Classifications
-
- 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
- G01S19/20—Integrity monitoring, fault detection or fault isolation of space segment
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Security & Cryptography (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention discloses a receiver autonomous integrity monitoring method, equipment and medium based on weighted estimation, which comprises the following specific steps: obtaining observation data, constructing a prediction model, and optimizing the prediction model based on a least square method; performing robust estimation by adopting the optimized prediction model, and determining an initial value of robust estimation; constructing a weighting matrix, and determining an robust estimation result based on an initial value of robust estimation; and monitoring satellite fault data according to the robust estimation result. The method has the advantages that the least square method is introduced to optimize the prediction model, an estimated value is obtained and is used as an estimated initial value of the prediction model, the influence of abnormal points on the result is reduced, a weighting matrix is constructed to determine an robust estimation result, the robustness is improved, the influence of the abnormal value on the fault detection rate is effectively restrained, the robust monitoring precision is improved, the problems that the estimated value is unreliable in the least square method and deviates too far from a true value are solved, and the reliability of the robust estimation result is improved.
Description
Technical Field
The invention relates to the technical field of satellite navigation receivers, in particular to a receiver autonomous integrity monitoring method, equipment and medium based on weighted estimation.
Background
Satellite navigation systems are an important basis for technological advancement and promotion of social development. At present, satellite navigation systems are already served worldwide and applied to various fields, and with the continuous increase of civil aviation traffic, satellite navigation integrity is receiving more and more attention, and for a certain moment, if a satellite fails, the satellite cannot be found in time, and a great positioning error may be caused. Positioning errors can place users in danger, and in order to monitor satellite faults in time, the prior art generally employs receiver autonomous integrity monitoring (Receiver autonomous integrity monitoring, RAIM) to detect and reject potentially faulty satellites to ensure reliability of the calculated position of the navigation system.
RAIM is an integrity monitoring method for monitoring the positioning result of a receiver by using redundant observation information, is an important component of integrity monitoring, and is widely applied to high-precision position service at present, so that higher requirements are put on the reliability of satellite navigation. Therefore, the research on the multi-coarse detection RAIM algorithm has very important significance for improving the GNSS positioning accuracy and reliability in the complex environment
The coarse detection in the prior art is generally based on correlation analysis of post-inspection residual vectors, and under the condition that a plurality of coarse errors exist, the residual vectors are comprehensively influenced by the plurality of coarse errors, the phenomenon of reduced correlation with coarse-error observation characteristic vectors is shown, under the condition that a fault exists, the residual vectors are easily influenced by abnormal values, and the coarse-error detection distortion is caused by the condition that missing detection or false detection is easy to occur, so that the problems of low detection probability and high false alarm probability of fault identification are generated.
Disclosure of Invention
The invention aims to provide a receiver autonomous integrity monitoring method, equipment and medium based on weighted estimation, which are used for optimizing a prediction model by introducing a least square method to obtain an estimated value as an estimated initial value of the prediction model, reducing the influence of abnormal points on a result, constructing a weighted matrix to determine an robust estimation result, improving the robustness, effectively inhibiting the influence of the abnormal value on the fault detection rate and improving the robust monitoring precision.
The invention is realized by the following technical scheme:
the invention provides a receiver autonomous integrity monitoring method based on weighted estimation, which comprises the following specific steps:
obtaining observation data, constructing a prediction model, and optimizing the prediction model based on a least square method;
performing robust estimation by adopting the optimized prediction model, and determining an initial value of robust estimation;
constructing a weighting matrix, and determining an robust estimation result based on an initial value of robust estimation;
and monitoring satellite fault data according to the robust estimation result.
According to the method, the observation data are obtained, the prediction model is constructed, the least square method is introduced to optimize the prediction model, the estimated value is obtained and used as an estimated initial value of the prediction model, the influence of abnormal points on the result can be reduced, the weighting matrix is constructed, the robust performance of the robust estimation result is improved based on the initial value of robust estimation, the influence of the abnormal value on the fault detection rate is effectively restrained, the rough difference monitoring precision is improved, the problem that the estimated value is unreliable in the least square method and deviates too far from the true value is solved, and the reliability of the robust estimation result is improved.
Further, the constructing the prediction model specifically includes:
and acquiring the pseudo-range observed quantity of the ith observation satellite, determining the difference value of the observed quantity according to the pseudo-range observed quantity of the ith observation satellite, and constructing a prediction model.
Further, the optimizing the prediction model specifically includes:
acquiring a pseudo-range residual error and a measurement error, and determining an error vector between the pseudo-range residual error and the measurement error;
and optimizing the prediction model according to the error vector.
Further, the obtaining the pseudo-range residual specifically includes:
and obtaining a predicted value and a true value of a pseudo range from the receiver to the ith observation satellite, obtaining a pseudo range measurement error based on dynamic difference, and determining a pseudo range residual by combining residual parameters.
Further, the performing robust estimation by using the optimized prediction model specifically includes:
constructing a linearization observation matrix according to the observation data of the ith observation satellite;
extracting fault prior probability of the ith observed quantity based on the observation matrix, constructing a probability density function, and determining a dangerous misleading probability value;
determining fault deviation of the ith observed quantity in the prediction model according to the dangerous misleading probability value;
and eliminating fault deviation in the prediction model, and determining an initial value of robust estimation by adopting a least square method.
Further, the determining the robust estimation result specifically includes:
constructing a covariance matrix according to the initial value of robust estimation, and taking the inverse matrix of the covariance matrix as a weighting matrix;
acquiring a weighting item corresponding to the ith observation satellite according to the weighting matrix, and constructing a weight function;
comparing the weight function with the pseudo-range residual error to obtain a weight factor;
and combining the initial value of the robust estimation with the weight factor to obtain the robust estimation result.
Further, the monitoring the satellite fault data according to the robust estimation result specifically includes:
acquiring a set threshold value, and judging whether the robust estimation result is smaller than the set threshold value:
if not, the observed data is re-acquired, the robust estimation result is updated until the robust estimation result is smaller than the set threshold value, and the iteration is ended.
Further, the setting threshold determining step includes:
acquiring historical data, and determining the omission factor and the false alarm rate according to the historical data;
constructing a cost function according to the omission factor and the false alarm rate;
and deriving a cost function, and determining an optimal set threshold value.
A second aspect of the invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a receiver autonomous integrity monitoring method based on weighted estimation when executing the program.
A third aspect of the present invention provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method of receiver autonomous integrity monitoring based on weighted estimation.
Compared with the prior art, the invention has the following advantages and beneficial effects:
and optimizing the prediction model by introducing a least square method to obtain an estimated value as an estimated initial value of the prediction model, reducing the influence of abnormal points on the result, constructing a weighting matrix to determine an robust estimation result, improving the robustness, effectively inhibiting the influence of the abnormal value on the fault detection rate, and improving the coarse-scale monitoring precision.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are needed in the examples will be briefly described below, it being understood that the following drawings only illustrate some examples of the present invention and therefore should not be considered as limiting the scope, and that other related drawings may be obtained from these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a flow chart of a method according to an embodiment of the invention.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
Example 1
As shown in fig. 1, a first aspect of the present embodiment provides a method for monitoring autonomous integrity of a receiver based on weighted estimation, including the following specific steps:
obtaining observation data, constructing a prediction model, and optimizing the prediction model based on a least square method;
performing robust estimation by adopting the optimized prediction model, and determining an initial value of robust estimation;
constructing a weighting matrix, and determining an robust estimation result based on an initial value of robust estimation;
and monitoring satellite fault data according to the robust estimation result.
According to the method, the observation data are obtained, the prediction model is constructed, the least square method is introduced to optimize the prediction model, the estimated value is obtained and used as an estimated initial value of the prediction model, the influence of abnormal points on the result can be reduced, the weighting matrix is constructed, the robust performance of the robust estimation result is improved based on the initial value of robust estimation, the influence of the abnormal value on the fault detection rate is effectively restrained, the rough difference monitoring precision is improved, the problem that the estimated value is unreliable in the least square method and deviates too far from the true value is solved, and the reliability of the robust estimation result is improved.
In some possible embodiments, gross error refers to a series of observations made under the same observation conditions, one of the categories of measurement errors, generally referring to an observation error that is greater than 3 times the error in absolute value, including errors due to carelessness in the inside and outside industries. Robust estimation (Robust Estimation), in fact, in the case of unavoidable coarse errors, an appropriate estimation method is chosen to minimize the influence of coarse errors on the unknown quantity estimation, and to obtain the best estimation in normal mode.
In some possible embodiments, constructing the prediction model specifically includes:
and acquiring the pseudo-range observed quantity of the ith observation satellite, determining the difference value of the observed quantity according to the pseudo-range observed quantity of the ith observation satellite, and constructing a prediction model.
In some possible embodiments, optimizing the prediction model specifically includes:
acquiring a pseudo-range residual error and a measurement error, and determining an error vector between the pseudo-range residual error and the measurement error;
and optimizing the prediction model according to the error vector.
In some possible embodiments, obtaining the pseudorange residuals specifically includes:
and obtaining a predicted value and a true value of a pseudo range from the receiver to the ith observation satellite, obtaining a pseudo range measurement error based on dynamic difference, and determining a pseudo range residual by combining residual parameters.
In some possible embodiments, performing robust estimation using the optimized prediction model specifically includes:
constructing a linearization observation matrix according to the observation data of the ith observation satellite;
extracting fault prior probability of the ith observed quantity based on the observation matrix, constructing a probability density function, and determining a dangerous misleading probability value;
determining fault deviation of the ith observed quantity in the prediction model according to the dangerous misleading probability value;
and eliminating fault deviation in the prediction model, and determining an initial value of robust estimation by adopting a least square method.
In some possible embodiments, determining the robust estimate result specifically includes:
constructing a covariance matrix according to the initial value of robust estimation, and taking the inverse matrix of the covariance matrix as a weighting matrix;
acquiring a weighting item corresponding to the ith observation satellite according to the weighting matrix, and constructing a weight function;
comparing the weight function with the pseudo-range residual error to obtain a weight factor;
and combining the initial value of the robust estimation with the weight factor to obtain the robust estimation result.
In some possible embodiments, monitoring satellite fault data according to robust estimation results specifically includes:
acquiring a set threshold value, and judging whether the robust estimation result is smaller than the set threshold value:
if not, the observed data is re-acquired, the robust estimation result is updated until the robust estimation result is smaller than the set threshold value, and the iteration is ended.
In some possible embodiments, the step of setting the threshold determination comprises:
acquiring historical data, and determining the omission factor and the false alarm rate according to the historical data;
constructing a cost function according to the omission factor and the false alarm rate;
and deriving a cost function, and determining an optimal set threshold value.
A second aspect of the present embodiment provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing a method for receiver autonomous integrity monitoring based on weighted estimation when executing the program.
A third aspect of the present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a receiver autonomous integrity monitoring method based on weighted estimation.
Example 2
The present embodiment provides a reception based on weighted estimation the method for monitoring the integrity of the machine comprises the following steps of specifically calculating;
satellite observation data are obtained, the observed quantity received by the receiver is resolved, the pseudo-range observed quantity of the ith observation satellite is obtained, the difference value of the observed quantity is determined according to the pseudo-range observed quantity of the ith observation satellite, and a prediction model is constructed:
X＝(H T H) -1 H T y
wherein y represents the difference between the pseudo-range observed quantity of the ith observed satellite and the observed quantity, and the specific y=ax+epsilon is represented by satellite observed coordinates received by the receiver, epsilon represents the pseudo-range observed error, H represents an observation matrix, and the observation matrix is obtained by resolving the satellite position and the receiver position through the difference between the pseudo-range observed quantity observed by using the navigation message and the observed quantity calculated by using the ground receiver and the satellite.
Obtaining a pseudo-range residual error and a measurement error, determining an error vector between the pseudo-range residual error and the measurement error, and optimizing a prediction model according to the error vector to obtain an optimized prediction model:wherein v represents an error vector between the pseudorange residual and the measurement error;
obtaining a predicted value b' and a true value b of a pseudo range from a receiver to an ith observation satellite, obtaining a pseudo range measurement error based on dynamic difference, and combining a residual parameter u i Determination of pseudorange residuals
Constructing a linearization observation matrix L=AX+sigma according to the observation data of the ith observation satellite, wherein X is an m×1-dimensional vector, A is the observation matrix and is m×n-dimensional, and sigma obeys N n (0,∑ σ ) The distribution of the particles is carried out,
extracting the ith based on the observation matrixFault prior probability p= (H) of observables i ) I=0, 1,..n, constructing a probability density functionWherein x is v Is the observed data resolving component, based on f (x v |H i L) determining a dangerous misleading probability value, judging whether fault deviation exists on the ith observed quantity according to the dangerous misleading probability value, if H i If so, determining that the fault deviation exists on the ith observed quantity, eliminating the fault deviation of the ith observed quantity from the prediction model, and determining the initial value of robust estimation by adopting a least square method on the prediction model with the fault deviation eliminated.
The influence of error factors in the navigation positioning process is not considered in the least square RAIM algorithm, and the error factors are used as weighting factors for various errors, so the scheme constructs the weighting factors: let the observed noise variance of the ith visible star be xi 2 And consider that the noise vector corresponding to each visible star under the current observation is not associated with any correlation, then under n visible stars, the covariance matrix of the observation noise can be constructed according to the initial value of the robust estimation, namely the covariance matrix isIn the case of covariance matrix determination, C will be -1 As a weighting matrix W, the noise ζ is observed i 2 When the value is closer to the actual value, the effect of the weighting algorithm is better, a weighting item corresponding to the ith observation satellite is obtained according to the weighting matrix, and a weight function is constructed;
comparing the weighting function with the pseudo-range residualWherein W is i * Representative weight factors, which are weight factors obtained by comparing weight functions with pseudo-range residuals, specifically represent ionosphere, troposphere, multipath, receiver thermal noise variance and the like to form a part of the weight factors, and delta represents the pseudo-range residuals; robust estimationCombining the initial value of (a) with the weight factor to obtain an robust estimation result. The main objective of the weighting study is the choice of weighting factors, which are diagonal elements of the covariance matrix in the measurement equation, by conventional weighting algorithms. And then constructing weighted test statistics by using a similar method, calculating the square sum of pseudo-range residual errors after obtaining new constructed test statistics, comparing the new constructed test statistics with a comparison detection threshold, and constructing a weighting matrix W in the fault detection and identification process of the weighted least square algorithm, so that the robustness of the weighted matrix W can be improved, the influence of abnormal values on the fault detection rate can be effectively restrained, and the RAIM usability can be improved.
Acquiring historical data, and determining the omission factor C according to the historical data MD And false alarm rate C FA ；
Constructing cost function according to miss detection rate and false alarm rateWherein, the liquid crystal display device comprises a liquid crystal display device, HAL represents a horizontal alarm limit, HDOP represents a horizontal precision factor change limit, and the horizontal alarm limit and the horizontal precision factor change limit are used to ensure the reliability and effectiveness of RAIM.
The minimum detection threshold of (2) is the best set threshold, due to +.>The method comprises the steps of calculating an optimal set threshold value by deriving a cost function, and judging whether an robust estimation result is smaller than the set threshold value according to the optimal set threshold value, wherein the integral upper limit function is related to a detection threshold:
if not, determining whether satellite fault data exist according to the judging result, if so, eliminating the satellite fault data at the moment, re-acquiring the observed data, updating the robust estimation result until the robust estimation result is smaller than the set threshold value, and ending iteration.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. The method for monitoring the autonomous integrity of the receiver based on the weighted estimation is characterized by comprising the following specific steps:
obtaining observation data, constructing a prediction model, and optimizing the prediction model based on a least square method;
performing robust estimation by adopting the optimized prediction model, and determining an initial value of robust estimation;
constructing a weighting matrix, and determining an robust estimation result based on an initial value of robust estimation;
and monitoring satellite fault data according to the robust estimation result.
2. The method for monitoring receiver autonomous integrity based on weighted estimation according to claim 1, wherein said constructing a predictive model specifically comprises:
and acquiring the pseudo-range observed quantity of the ith observation satellite, determining the difference value of the observed quantity according to the pseudo-range observed quantity of the ith observation satellite, and constructing a prediction model.
3. The method for monitoring receiver autonomous integrity based on weighted estimation according to claim 1, wherein said optimizing a predictive model specifically comprises:
acquiring a pseudo-range residual error and a measurement error, and determining an error vector between the pseudo-range residual error and the measurement error;
and optimizing the prediction model according to the error vector.
4. The method for receiver autonomous integrity monitoring based on weighted estimation of claim 3, wherein the obtaining pseudo-range residuals specifically comprises:
and obtaining a predicted value and a true value of a pseudo range from the receiver to the ith observation satellite, obtaining a pseudo range measurement error based on dynamic difference, and determining a pseudo range residual by combining residual parameters.
5. The method for monitoring receiver autonomous integrity based on weighted estimation according to claim 4, wherein said performing robust estimation using an optimized predictive model specifically comprises:
constructing a linearization observation matrix according to the observation data of the ith observation satellite;
extracting fault prior probability of the ith observed quantity based on the observation matrix, constructing a probability density function, and determining a dangerous misleading probability value;
determining fault deviation of the ith observed quantity in the prediction model according to the dangerous misleading probability value;
and eliminating fault deviation in the prediction model, and determining an initial value of robust estimation by adopting a least square method.
6. The method for monitoring receiver autonomous integrity based on weighted estimation according to claim 5, wherein said determining robust estimation results specifically comprises:
constructing a covariance matrix according to the initial value of robust estimation, and taking the inverse matrix of the covariance matrix as a weighting matrix;
acquiring a weighting item corresponding to the ith observation satellite according to the weighting matrix, and constructing a weight function;
comparing the weight function with the pseudo-range residual error to obtain a weight factor;
and combining the initial value of the robust estimation with the weight factor to obtain the robust estimation result.
7. The method for monitoring receiver autonomous integrity based on weighted estimation according to claim 1, wherein the monitoring satellite fault data according to robust estimation result specifically comprises:
acquiring a set threshold value, and judging whether the robust estimation result is smaller than the set threshold value:
if not, the observed data is re-acquired, the robust estimation result is updated until the robust estimation result is smaller than the set threshold value, and the iteration is ended.
8. The weighted estimation based receiver autonomous integrity monitoring method of claim 7, wherein the set threshold determining step comprises:
acquiring historical data, and determining the omission factor and the false alarm rate according to the historical data;
constructing a cost function according to the omission factor and the false alarm rate;
and deriving a cost function, and determining an optimal set threshold value.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements a receiver autonomous integrity monitoring method based on weighted estimation as claimed in any one of claims 1 to 8 when executing the program.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a receiver autonomous integrity monitoring method based on weighted estimation as claimed in any of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310941810.6A CN116953734A (en) | 2023-07-28 | 2023-07-28 | Receiver autonomous integrity monitoring method, equipment and medium based on weighted estimation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310941810.6A CN116953734A (en) | 2023-07-28 | 2023-07-28 | Receiver autonomous integrity monitoring method, equipment and medium based on weighted estimation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116953734A true CN116953734A (en) | 2023-10-27 |
Family
ID=88452559
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310941810.6A Pending CN116953734A (en) | 2023-07-28 | 2023-07-28 | Receiver autonomous integrity monitoring method, equipment and medium based on weighted estimation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116953734A (en) |
-
2023
- 2023-07-28 CN CN202310941810.6A patent/CN116953734A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20210033735A1 (en) | System and method for gaussian process enhanced gnss corrections generation | |
| KR101574819B1 (en) | Method for protecting a radio navigation receiver user against aberrant pseudo-range measurements | |
| US20170131407A1 (en) | Selected aspects of advanced receiver autonomous integrity monitoring application to kalman filter based navigation filter | |
| US7956802B1 (en) | Integrity-optimized receiver autonomous integrity monitoring (RAIM) for vertical integrity monitoring | |
| Castaldo et al. | P‐RANSAC: An Integrity Monitoring Approach for GNSS Signal Degraded Scenario | |
| CN110646820B (en) | Quality inspection method, device, equipment and storage medium of RTK positioning data | |
| US20230018631A1 (en) | System and method for satellite positioning | |
| CN113138402B (en) | RTK-based ambiguity fixing method and device and storage medium | |
| KR20120026998A (en) | Method for correcting position estimations by selecting pseudo-distance measurements | |
| KR101503001B1 (en) | System and method for determining fault of gnss using estimated value of baseline length and ground facility antenna of multiple gnss | |
| CN110941000B (en) | Precise single-point positioning integrity monitoring method | |
| CN112987039A (en) | Navigation satellite positioning service abnormity monitoring method and device | |
| JIANG et al. | A robust fault detection algorithm for the GNSS/INS integrated navigation systems | |
| CN111142125B (en) | Satellite integrity monitoring method and system | |
| Knowles et al. | Euclidean distance matrix-based rapid fault detection and exclusion | |
| CN115561782B (en) | Satellite fault detection method in integrated navigation based on odd-even vector projection | |
| Tmazirte et al. | Fast multi fault detection & exclusion approach for GNSS integrity monitoring | |
| CN116953734A (en) | Receiver autonomous integrity monitoring method, equipment and medium based on weighted estimation | |
| CN112558112B (en) | GNSS state domain slow-change slope fault integrity monitoring method | |
| Wen et al. | Mitigation of multiple outliers using consistency checking for GNSS standard point positioning in urban areas | |
| CN114355410A (en) | Satellite navigation real-time precise single-point positioning system and method based on parallel computing | |
| CN113848570A (en) | Self-adaptive real-time multipath elimination and robust positioning method based on non-Gaussian distribution | |
| CN116736358B (en) | Long baseline carrier phase differential positioning method suitable for satellite navigation | |
| EP4184217A1 (en) | Method for detecting outliers in measurements of global navigation satellite systems | |
| CN112764059B (en) | Autonomous integrity monitoring method and device for receiver |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |