CN114545454A - Fusion navigation system integrity monitoring method for automatic driving - Google Patents

Fusion navigation system integrity monitoring method for automatic driving Download PDF

Info

Publication number
CN114545454A
CN114545454A CN202210136863.6A CN202210136863A CN114545454A CN 114545454 A CN114545454 A CN 114545454A CN 202210136863 A CN202210136863 A CN 202210136863A CN 114545454 A CN114545454 A CN 114545454A
Authority
CN
China
Prior art keywords
fault
navigation system
carrier
error
distance
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
Application number
CN202210136863.6A
Other languages
Chinese (zh)
Inventor
孙蕊
邓思瑜
邱明
陈含智
蒋磊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Aeronautics and Astronautics
Original Assignee
Nanjing University of Aeronautics and Astronautics
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Nanjing University of Aeronautics and Astronautics filed Critical Nanjing University of Aeronautics and Astronautics
Priority to CN202210136863.6A priority Critical patent/CN114545454A/en
Publication of CN114545454A publication Critical patent/CN114545454A/en
Pending legal-status Critical Current

Links

Images

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
    • G01S19/23—Testing, monitoring, correcting or calibrating of receiver elements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
    • G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
    • 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
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • 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
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial

Abstract

The invention provides an integrity monitoring method for an automatic driving-oriented fusion navigation system, which comprises the following steps: step 1, carrying out scene matching by using a carrier camera to obtain a reference position, and inverting the distance from a carrier to a characteristic point; step 2, utilizing carrier laser radar observation information and a machine learning algorithm to assist a global satellite navigation system to correct pseudo range errors; step 3, tightly coupling the camera observed quantity and the global satellite navigation system observed quantity to construct an observation equation; step 4, calculating a least square residual error, and constructing test statistics for fault detection; step 5, calculating to obtain a standard deviation of the predicted positioning error, fitting a normal distribution of the positioning error and calculating a user protection level in the horizontal direction; and 6, comparing the obtained user protection level with a protection limit value, and finishing the integrity monitoring of the multi-sensor assisted global satellite navigation system facing automatic driving.

Description

Fusion navigation system integrity monitoring method for automatic driving
Technical Field
The invention relates to a method for monitoring the integrity of a fusion navigation system, in particular to a method for monitoring the integrity of a fusion navigation system for automatic driving.
Background
Autopilot is an ultimate goal of intelligent traffic development, which has progressed rapidly in recent years. The navigation positioning technology can provide position, speed and time information for the vehicle, and is a basic technology for supporting automatic driving. As an application with safety and responsibility as a concern, the automatic driving not only needs a navigation positioning system of a vehicle to provide high-precision positioning information for the automatic driving, but also has a very high requirement on the reliability of the positioning information. The integrity is used as an important index for evaluating the reliability, and the integrity is mainly used for monitoring and removing faults of the navigation system and providing timely warning service for a user so as to enhance the reliability of the navigation system. Currently, a Global Navigation Satellite System (GNSS) is used as the most competitive positioning and Navigation technology, can provide continuous and high-precision Navigation positioning information for a vehicle, can realize integrated Navigation with other sensors such as inertial Navigation and a visual odometer, and is widely applied to research and test of an automatic driving vehicle. However, in order to ensure the safety of the autopilot, the integrity of the GNSS needs to be monitored effectively. Particularly, in a complex urban environment, when satellite signals are easily shielded or reflected by buildings, the situations of insufficient number of visible satellites and serious multipath errors are caused, and further application of the GNSS in automatic driving is limited.
The research on GNSS integrity originally originated in the field of aviation, and user terminal integrity monitoring was the integrity of GNSS positioning results determined from the user receiver itself, with key factors being available resources and monitoring algorithms. The assistance according to the existence of other navigation resources can be divided into receiver autonomous integrity monitoring and user-assisted integrity monitoring.
Receiver Autonomous Integrity Monitoring (RAIM) is a method for checking and judging the validity of a positioning result through a redundancy constraint relation between different observed quantities. The main content comprises two parts: the method comprises the following steps that firstly, a fault detection and identification algorithm is used for effectively detecting and eliminating faults affecting positioning accuracy; the second is an integrity availability determination method, which mainly determines whether the integrity risk borne by the current epoch positioning system is out of limit, that is, whether the current epoch time integrity algorithm is available. In the first part of research on satellite fault detection algorithms, the typical algorithms mainly include a pseudo-range comparison method, a least square residual method and a parity vector method. Lee proposes a pseudo-range comparison method (refer to Lee Y C. analysis of range and position compatibility methods as a means to provide GPS integration in the user receiver [ C ]/procedures of the 42nd Annual Meeting of the institute of navigation.1986, 1-4.), which is not based on any gross-difference detection model, and performs fault detection only by comparing a predicted observed value calculated from redundant data with an actual observed value, but cannot identify a faulty satellite. Parkinson proposes a least square residual method (refer to Parkinson B W, Axelar P. Autonomous GPS integration Using the pseudo random residual [ J ] in 1988, 35 (2): 255) 274.), the least square residual method firstly solves the least square solution of the user position according to a GNSS linearized observation equation, then calculates a residual pseudo range as a test statistic, and judges whether the system has a fault or not by comparing the statistic with a threshold value. On the basis of the least square residual method, Sturza proposes a parity vector method (refer to Sturza M. A. Navigation system integration using vector measures [ J ]. Navigation, 1988, 35 (4): 483-501.), wherein the parity vector method is to construct the test statistic by expressing the rough difference of the fault satellite as a parity vector after orthogonal triangular decomposition of the coefficient matrix of the linear observation equation. In the second part of the study on the integrity determination method, the determination of the availability of integrity is performed by calculating whether the integrity risk of each epoch exceeds the alarm limit. Since the calculation of the integrity risk for each epoch is related to the unknown magnitude of the satellite failure probability, indirect integrity determination methods have emerged. Brown proposes a method of calculating the level of protection against horizontal positioning errors of the system by means of mathematical analysis for usability determination (reference: R.G. Brown and et al. Comparison of FDE and FDI RAIM errors for GPS. proceedings of ION NTM-94, 1994: 51-60.), the protection level calculated at each epoch instant being used for continuous comparison with an alarm limit, and if the protection level exceeds the alarm limit, an alarm being issued to the user, and the navigation system being declared unusable until the protection level falls below the alarm limit.
The user-assisted integrity monitoring refers to a method for estimating and judging the integrity of the GNSS by the GNSS user using the navigation resources available to the user or the surroundings as the auxiliary redundant information, together with the GNSS measurement or navigation solution. The method has the advantages that integrity monitoring is assisted by a pressure gauge in aviation application, and inertial navigation assisted integrity monitoring, vision assisted integrity monitoring and the like are gradually developed at present. The common algorithm is to combine the barometric altimeter or inertial navigation with the GNSS observation information in a Kalman filter framework to obtain an integrity enhancement algorithm under a dynamic condition. An inertial navigation auxiliary satellite fault detection and isolation method based on inspection is researched by the great, Zhao Ziyang, Li Xingfei, Zhang Jiachuan, and an inertial/satellite tight coupling system fault detection and isolation method [ J ] fire and command control, 2016, 41 (02): 169 and 172 ] based on chi-2 inspection, and the method can automatically identify single satellite fault by using a fault detection function and remove fault stars in real time to automatically realize observation information reconstruction. Chenvina proposes an airborne autonomous integrity monitoring algorithm based on the assistance of an air pressure altimeter (refer to: Chenvina, Yang faith, West, Wang.navigation and control, 2020, 19 (03): 115 and 121.), and establishes a system observation equation of the airborne autonomous integrity monitoring based on the air pressure altimeter by using the height information provided by the air pressure altimeter, so that the fault satellite can be detected and identified when the number of visible satellites is 5. However, the Kalman filtering algorithm not only uses the observation information of the current time, but also uses the observation information of the historical time, and the calculation amount and the data storage amount are large. The accuracy of the height measurement of the air pressure altimeter is greatly influenced by the air flow, the error of inertial navigation can be dispersed along with time, and the reliability of redundant information can influence the integrity of the GNSS when the navigation resource is utilized to assist the integrity monitoring of the GNSS.
In a complex urban environment, the performance of a single RAIM algorithm cannot meet the requirements of automatic driving navigation performance due to the reduced quality of satellite signals. The method for detecting the fault under the Kalman filtering framework by using navigation observation information assisted by other sensors has the problem that the RAIM result is possibly polluted by errors caused by other sensors.
Because the traditional user terminal integrity monitoring algorithm is mainly oriented to aviation users, the pseudo-range observation noise is generally regarded as obeying Gaussian distribution, and the multipath error is estimated by using a model. However, in an urban canyon environment, due to signal shielding or reflection, observation noise cannot completely follow Gaussian distribution, and multipath errors are difficult to model and estimate.
The technology which is closer to the method is a fault detection method by utilizing a residual error vector method and a method for assisting satellite integrity monitoring by utilizing inertial navigation under a Kalman frame. However, the performance of the RAIM algorithm based on the residual vector is reduced when the number of visible satellites is insufficient in the urban environment, and the integrity monitoring performance of the integrity monitoring method based on the inertial navigation assistance is reduced due to the accumulated error of the inertial navigation.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to solve the technical problem of providing an automatic driving-oriented fused navigation system integrity monitoring method aiming at the defects of the prior art.
In order to solve the technical problem, the invention discloses an integrity monitoring method of an automatic driving-oriented fusion navigation system, which comprises the following steps of:
step 1, carrying out scene matching by using a carrier camera to obtain a reference position, and inverting the distance from a carrier to a characteristic point from the reference position;
step 2, utilizing carrier laser radar observation information and a machine learning algorithm to assist a global satellite navigation system to correct pseudo range errors;
step 3, tightly coupling the camera observed quantity and the global satellite navigation system observed quantity to construct an observation equation;
step 4, calculating a least square residual error, and constructing test statistics for fault detection;
step 5, calculating to obtain a standard deviation of the predicted positioning error, fitting a normal distribution of the positioning error and calculating a user protection level in the horizontal direction;
and 6, comparing the obtained user protection level with a protection limit value, and finishing the integrity monitoring of the fusion navigation system facing the automatic driving.
In the present invention, step 1 comprises: sensing and detecting the surrounding environment by using a carrier camera, matching an observation result with a prior three-dimensional high-precision point cloud map, and estimating the position of a carrier under a local coordinate system; and taking the building boundary point and the road boundary point as feature points, and inverting the distance from the carrier reference position to the feature points according to the coordinate information of the feature points.
In the invention, the step 2 comprises the following steps:
step 2-1, a global satellite navigation system receiver in a carrier and a laser radar are adopted to acquire data in an urban canyon, and a satellite signal carrier-to-noise ratio, a satellite altitude angle, the reflection intensity of the surface of a building and the distance from the carrier to the building, which are related to multipath error comparison, are adopted as input characteristics;
2-2, selecting pseudo-range errors as labels of each sample, and constructing a historical training data set;
and 2-3, excavating the relation among the carrier-to-noise ratio of the input characteristic satellite signal, the satellite altitude, the reflection intensity of the building surface and the distance from the carrier to the building and the output variable pseudo-range error by using a deep neural network algorithm, constructing a pseudo-range error prediction rule, and correcting the pseudo-range error.
In the present invention, step 3 comprises:
step 3-1, converting the northeast coordinates of the known characteristic points into geocentric coordinates;
and 3-2, positioning and solving by combining the observed pseudo range of the global satellite navigation system and the distance inverted by the camera.
In the present invention, step 3-1 comprises:
the northeast coordinates of the known characteristic points are converted into geocentric coordinates, and the rotation matrix is as follows:
Figure BDA0003505187340000041
wherein L is longitude and B is latitude.
In the invention, in step 3-2, the process of positioning and solving by combining the observed pseudo range of the global satellite navigation system and the distance inverted by the camera is as follows:
Figure BDA0003505187340000042
where ρ isG＝R+δGFor the global satellite navigation system observation equation, ρC＝R+δCIs a camera observation equation; where ρ isGObserving a pseudo range for the global satellite navigation system after pseudo range error correction predicted according to the deep neural network; rhoCThe distance to the feature point is inverted according to the camera positioning; r is the distance from the satellite or the characteristic point to the carrier; deltaGThe pseudo range errors of other uncompensated pseudo range errors are obtained; deltaCA camera range error;
estimating a global satellite navigation system observation equation by taking a carrier three-dimensional coordinate and a receiver clock error as unknown parameters; estimating three-dimensional coordinates of the carrier by using a camera observation equation; and expanding the geometric distance R according to Taylor series to obtain:
Figure BDA0003505187340000051
in the formula, R0The approximate geometric distance from the satellite or the characteristic point to the carrier; (x)i，yi，zi) Coordinates of the ith satellite or characteristic point; (x)0，y0，z0) Approximate coordinates of the carrier; (dx, dy, dz) is the increment of the vector coordinates; neglecting the non-linear error term yields:
Figure BDA0003505187340000052
where E (-) is the mathematical expectation operator; after the Taylor series linearization is utilized, the joint positioning problem of the close coupling of the global satellite navigation system observation data and the camera observation data is approximately converted into a linear problem, and the linear system is as follows:
ΔR＝A·dX
wherein Δ R ═ R-R0Is an observation from a global satellite navigation system or camera; dX ═ dX, dy, dz, dt]TDx, dy and dz are increment of three-dimensional coordinates of the carrier; dt is a clock error parameter of a global satellite navigation system receiver; design matrix a is as follows:
Figure BDA0003505187340000053
wherein (x)0，y0，z0) Is the approximate coordinates of the carrier; (x)1，y1，z1) To (x)m，ym，zm) Coordinates of 1 to m satellites; r1To RmIs the distance from 1 to m satellites to the carrier; (x)1，y1，z1) To (x)n，yn，zn) Coordinates of 1 to n feature points; r1To RnThe distances from 1 to n characteristic points to the carrier; the correspondingly solved 4 parameters dx, dy, dz, dt are the increment of the three-dimensional coordinates of the carrier and the clock error of the global satellite navigation system receiver.
In step 4, calculating a least square residual error, and constructing test statistics to perform a fault detection method, wherein the fault detection method comprises fault detection and fault identification;
the fault detection method comprises the following steps:
the least squares solution of the equation in step 3-2 is:
Figure BDA0003505187340000061
wherein the content of the first and second substances,
Figure BDA0003505187340000062
carrier coordinate increment and receiver clock difference estimation;
the distance residual vector obtained by calculation is as follows:
z＝(I-A(ATA)-1AT)ΔR
wherein I is an identity matrix; z is a distance residual vector; calculating a distance residual vector through the design matrix A, and after obtaining the distance residual vector, obtaining the error in the posterior unit weight of the distance residual square sum as follows:
Figure BDA0003505187340000063
wherein SSE is the sum of squares of the distance residuals; adopting the error sigma in the unit weight after the test as the statistic of fault detection; when no fault exists, the distance observation errors of the satellite and the camera follow normal distribution of independent distribution, the mean value is 0, and the variance is
Figure BDA0003505187340000064
Is obtained by the theory of statistical distribution,
Figure BDA0003505187340000065
obeying chi-square distribution with the degree of freedom of m + n-4; when there is a fault in the presence of a fault,
Figure BDA0003505187340000066
obeying chi-square distribution with the degree of freedom of m + n-4 and the non-centering parameter of lambda;
when the system has no fault, the system alarm is false alarm according to the given false alarm rate
Figure BDA0003505187340000067
Obtaining:
Figure BDA0003505187340000068
wherein H0Representing a no-fault assumption; t is an actual detection value; p { { T < T2}|H0Represents the probability that the system itself is fault-free and no fault is detected;
Figure BDA0003505187340000069
is chi with the degree of freedom of m + n-42Distribution probability density function, determining
Figure BDA00035051873400000610
Is detected by2By detecting the threshold value T2Further obtain the detection threshold value sigma of sigmaTComprises the following steps:
Figure BDA00035051873400000611
if σ > σTThen there is erroneous observations in the current gnss and camera observations.
In step 4 of the invention, the least square residual error is calculated, and the method for detecting the fault by constructing the test statistic comprises fault detection and fault identification;
the fault identification method comprises the following steps:
fault identification inspection quantity d constructed based on least square residual vectori：
Figure BDA0003505187340000071
Wherein z isiDistance residual, Q, representing the ith observationziiRepresenting elements corresponding to the ith observation in the distance residual error covariance matrix, wherein the statistic obeys standard normal distribution when no fault exists; averagely distributing the false alarm rate to m + n observation quantities, and obtaining a detection threshold value of the fault identification statistic as follows:
Figure BDA0003505187340000072
wherein, P { di＞TdDenotes the fault identification statistic diGreater than a threshold value TdThe probability of (d);
Figure BDA0003505187340000073
a probability density function that is a standard normal distribution; when a fault exists, if di＞TdAnd indicating that the ith observation is faulty.
In step 5, calculating pseudo range error standard deviation of m satellites predicted by current epoch and horizontal precision factor values of satellite distribution, and multiplying to obtain standard deviation sigma of predicted positioning errorxAnd calculating the user protection level in the horizontal direction by fitting a normal distribution of the positioning error, wherein the method comprises the following steps:
step 5-1, under the condition of no fault:
the integrity risk is calculated as follows:
PHMI，0＝P{|Δx|＞HPL|H0}P{σ＜σT|H0}P{H0}
wherein, PHMI，0Integrity risks assigned to a no fault condition; HPL is the user protection level in the horizontal direction; h0No fault is assumed; Δ x is the horizontal positioning error; p { |. DELTA.x| > HPL | H0The probability that the positioning error exceeds the user protection level under the assumption of no fault; p { sigma < sigmaT|H0Is the probability that the failure detection statistic does not exceed the detection threshold under the no-failure assumption, i.e.
Figure BDA0003505187340000077
Obtaining the probability P { | delta x | > HPL | H that the positioning error exceeds the user protection level under the assumption of no fault0The method is as follows:
Figure BDA0003505187340000074
calculating the level of protection by determining the probability distribution of horizontal positioning errors, using the standard deviation sigma of the predicted positioning errorsxAs a normal of positioning errorThe standard deviation of the distribution, i.e., Δ x to N (0, σ)x) The user protection level under the assumption of no fault is obtained as follows:
Figure BDA0003505187340000075
wherein, HPL0The user protection level under the assumption of no fault;
Figure BDA0003505187340000076
1-alpha of standard normal distribution1A 2-point of the molecule is,
Figure BDA0003505187340000081
step 5-2, in case of a fault:
the calculation formula of the integrity risk is as follows:
PHMI，1＝P{|Δx|＞HPL|H1}P{σ＜σT|H1}P{H1}
in the formula, PHMI，1To assign to the integrity risk in case of a fault; h1Is a faulty assumption; p { |. DELTA.x| > HPL | H1The probability that the positioning error exceeds the user protection level under the assumption of failure; p { sigma < sigma [ [ alpha ] ]T|H1Is the probability that the fault detection statistic does not exceed the detection threshold under the assumption of fault, i.e. the miss rate PMD(ii) a When a fault occurs, the probability distribution of the horizontal positioning error is delta x-N (mu, sigma)x) Mu is fault deviation, and the minimum detectable coarse difference value under the condition that the fault detection rate is greater than or equal to 99.9% is used as the estimation value of mu.
The probability that the positioning error exceeds the user protection level under the assumption of a fault is obtained:
Figure BDA0003505187340000082
the user protection level when containing a fault is then:
Figure BDA0003505187340000083
in the formula, HPL1A user protection level under the assumption of failure;
Figure BDA0003505187340000084
1-alpha of standard normal distribution2A/2 split site, wherein
Figure BDA0003505187340000085
Figure BDA0003505187340000086
Ai，1Is the ith row and the 1 st column of the matrix A; a. thei，2Is the ith row and the 2nd column of the matrix A.
In the present invention, step 6 comprises:
calculating to obtain the user protection level in the horizontal direction, wherein the method comprises the following steps:
HPL＝max{HPL0，HPL1}
comparing the user protection level with a protection limit value, and if the user protection level does not exceed the protection limit value, the integrity is available; and if the protection level of the user exceeds the protection limit value, giving an alarm to the user, and finally completing the integrity monitoring of the multi-sensor assisted global satellite navigation system facing the automatic driving.
Has the advantages that:
the invention provides a method for monitoring integrity of a GNSS (global navigation satellite system) based on multi-sensor assistance, aiming at solving the problem that when an original satellite integrity monitoring algorithm is used in a complex environment, redundant information is reduced to cause insufficient algorithm performance. The method comprises the steps that a camera and 3D city map navigation information are utilized to assist a GNSS to detect satellite faults, the possible unreliability of the observed quantity of a redundant sensor is considered, a fault detection method of a bidirectional sensor is designed, and errors of other sensors are prevented from being transmitted to integrity monitoring of the GNSS; and calculating the protection level based on the pseudo-range error variance predicted in real time, and constructing a protection level calculation method which is more fit with the positioning error in the urban environment to realize effective integrity monitoring.
Aiming at the integrity requirement of automatic driving in urban environment, the invention designs a GNSS integrity monitoring method based on multi-sensor assistance, which effectively controls the observation quality of GNSS signals by using redundant information of other sensors and a machine learning algorithm, and ensures the reliability of using satellite positioning results for automatic driving.
According to the method, double fault detection of the satellite and the visual feature point is carried out on the simultaneous linear equation of the GNSS pseudo range observation and the camera inversion distance, the DNN algorithm is used for predicting the pseudo range error, the standard deviation of the pseudo range error is used for approximately fitting the probability distribution of the positioning error so as to solve the specific implementation process of the user protection level, and meanwhile the problems that the performance is reduced when the number of visible satellites is insufficient in the urban environment and the integrity monitoring performance is reduced due to the accumulated error of inertial navigation are solved.
Drawings
The foregoing and/or other advantages of the invention will become further apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
FIG. 1 is a general flow diagram of the present invention.
Detailed Description
The core content of the method is a specific implementation process of performing double fault detection on satellites and visual feature points by using a simultaneous linear equation of GNSS pseudo range observation and camera inversion distance, predicting pseudo range errors by using a Deep Neural Network (DNN) algorithm, and approximately fitting positioning error probability distribution by using standard deviation of the pseudo range errors so as to solve user protection level.
An autodrive-oriented fused navigation system integrity monitoring method is shown in fig. 1:
the first step is as follows: and carrying out scene matching by using a camera to obtain a reference position, and inverting the distance from the carrier to the characteristic point from the reference position. Sensing and detecting the surrounding environment by using a camera, matching an observation result with a priori 3D high-precision point cloud map, and estimating the position of the carrier under a local coordinate system; and taking the building boundary point and the road boundary point as feature points, and inverting the distance from the carrier reference position to the feature points according to the coordinate information of the feature points. A
The second step is that: pseudo-range error correction is performed by using LiDAR (Light detection and ranging) observation information and a machine learning algorithm to assist the GNSS.
1) The off-line part firstly collects data in urban canyons by using a GNSS receiver and LiDAR, and adopts satellite signal carrier-to-noise ratio, satellite altitude angle, reflection intensity of building surface and distance from a carrier to a building, which are related to multipath error comparison, as input characteristics.
2) After the input features are determined, the pseudorange errors are selected as labels for each sample to construct a historical training data set.
3) And excavating the relation among the carrier-to-noise ratio of the input characteristic satellite signals, the satellite altitude, the reflection intensity of the building surface, the distance from the carrier to the building and the output variable pseudo-range error by using a deep neural network algorithm to construct a pseudo-range error prediction rule.
The third step: tightly coupling the camera observed quantity and the GNSS observed quantity to construct an observation equation.
1) The northeast coordinates of the known characteristic points are converted into geocentric coordinates, and the rotation matrix is
Figure BDA0003505187340000101
Wherein L is longitude; and B is latitude.
2) The positioning solving process combining the GNSS observation pseudo-range and the camera inversion distance comprises the following steps:
Figure BDA0003505187340000102
in the formula, ρG＝R+δGFor GNSS observation equation, ρC＝R+δCIs a camera observation equation; ρ is a unit of a gradientGThe pseudo range is a GNSS observation pseudo range after pseudo range error correction predicted according to the deep neural network; ρ is a unit of a gradientCThe distance to the feature point is inverted according to the camera positioning; r is satellite orThe distance from the feature point to the carrier; deltaGThe pseudo range errors of other uncompensated pseudo range errors are obtained; deltaCIs the camera range error.
Estimating a GNSS observation equation, a user three-dimensional coordinate and a receiver clock error as unknown parameters; the camera observation equation does not need to estimate the clock error, but only needs to estimate the three-dimensional coordinates of the user. Expanding the geometric distance R according to Taylor series to obtain:
Figure BDA0003505187340000103
in the formula, R0The approximate geometric distance from the satellite or the characteristic point to the carrier; (x)i，yi，zi) Coordinates of the ith satellite or characteristic point; (x)0，y0，z0) Is the approximate coordinates of the carrier; (dx, dy, dz) is the increment of the vector coordinates. Neglecting the non-linear error term yields:
Figure BDA0003505187340000104
in the formula, E (-) is the mathematical expectation operator. After the taylor series linearization is utilized, the joint positioning problem of tightly coupling the GNSS observation signal and the camera observation data can be approximately converted into 1 linear problem, and the linear system is as follows:
ΔR＝A·dX
wherein Δ R ═ R-R0As observations from a GNSS or camera; dX ═ dX, dy, dz, dt]TAnd dt is a GNSS receiver clock error parameter. Design matrix a is as follows:
Figure BDA0003505187340000111
in the formula (x)0，y0，z0) Is the approximate coordinates of the carrier; (x)1，y1，z1) To (x)m，ym，zm) Coordinates of 1 to m satellites; r1To RmIs the distance from 1 to m satellites to the carrier; (x)1，y1，z1) To (x)n，yn，zn) Coordinates of 1 to n feature points; r1To RnIs the distance from 1 to n characteristic points to the carrier. The correspondingly solved 4 parameters dx, dy, dz, dt are the user three-dimensional coordinate increment and the GNSS receiver clock error.
The fourth step: and calculating least square residual errors, and constructing test statistics for fault detection.
1) Fault detection
The least squares solution of the above equation is:
Figure BDA0003505187340000112
in the formula (I), the compound is shown in the specification,
Figure BDA0003505187340000113
user coordinate increments and receiver clock offset estimates.
The distance residual vector obtained by calculation is as follows:
z＝(I-A(ATA)-1AT)ΔR
wherein z is a distance residual vector; and I is an identity matrix. The distance residual vector can be directly calculated as long as the design matrix A is known, and a least square solution does not need to be calculated. After the distance residual vector is obtained, the error in the posterior unit weight of the sum of squares of the obtained distance residual is as follows:
Figure BDA0003505187340000114
in the formula, SSE is the sum of squares of the distance residuals. And adopting the error sigma in the unit weight after the test as the statistic of fault detection. When the system is in failure, the distance observation errors of the satellite and the camera are considered to be subjected to normal distribution of independent distribution, the mean value of the distance observation errors is 0, and the variance of the distance observation errors is
Figure BDA0003505187340000121
As can be seen from the theory of statistical distribution,
Figure BDA0003505187340000122
chi-square distribution with the obedience degree of freedom of m + n-4 is carried out; and when there is a failure, the system will,
Figure BDA0003505187340000123
one would obey a chi-square distribution with a degree of freedom of m + n-4 and a non-centering parameter of λ.
When the system has no fault, the system alarm is false alarm, and the false alarm rate is given according to the user integrity requirement
Figure BDA0003505187340000124
Then there are:
Figure BDA0003505187340000125
in the formula, H0Representing a no-fault assumption; p { { T < T2}|H0Represents the probability that the system itself is fault-free and no fault is detected;
Figure BDA0003505187340000126
is chi with the degree of freedom of m + n-42Distribution probability density function determined by the above equation
Figure BDA0003505187340000127
Is detected by2The detection threshold for σ can be found as:
Figure BDA0003505187340000128
if σ > σTIf so, it indicates that there is erroneous observation information in the current GNSS and camera observations, and fault identification and elimination are required.
2) Fault detection
Fault identification inspection quantity d constructed based on least square residual vectori：
Figure BDA0003505187340000129
In the formula, ziDistance residual, Q, representing the ith observationziiAnd representing elements corresponding to the ith observation in the distance residual error covariance matrix, wherein the statistic obeys standard normal distribution when no fault exists. And averagely distributing the false alarm rate to m + n observation quantities, wherein the detection threshold value of the obtained fault identification statistic is as follows:
Figure BDA00035051873400001210
wherein P { d > TdIndicates the probability that the fault identification statistic is greater than the threshold;
Figure BDA00035051873400001211
is a probability density function of a standard normal distribution. Therefore, if d is present when a fault existsi＞TdIf so, the ith observation is judged to be faulty, and the fault is required to be eliminated.
The fifth step: calculating pseudo-range error standard deviation of m satellites predicted by the epoch and a horizontal Precision factor (HDOP) value of satellite distribution, multiplying to obtain standard deviation of predicted positioning error, and fitting normal distribution of the positioning error to calculate user protection level in the horizontal direction.
1) No fault condition:
the health risk is calculated as:
PHMI，0＝P{|Δx|＞HPL|H0}P{σ＜σT|H0}P{H0}
in the formula, PHMI，0Integrity risks assigned to a no fault condition; HPL is the user protection level in the horizontal direction; h0No fault is assumed; Δ x is the horizontal positioning error; p { |. DELTA.x| > HPL | H0The probability that the positioning error exceeds the user protection level under the assumption of no fault; p { sigma < sigmaT|H0Is the probability that the failure detection statistic does not exceed the detection threshold under the no-failure assumption, i.e.
Figure BDA0003505187340000131
The probability that the positioning error exceeds the user protection level under the fault-free assumption can be obtained:
Figure BDA0003505187340000132
for calculating the protection level, the probability distribution of the horizontal positioning error is determined, using the standard deviation sigma of the predicted positioning errorxThe standard deviation of the normal distribution of the positioning error is Δ x to N (0, σ)x) The available user protection levels under the no-fault assumption are as follows:
Figure BDA0003505187340000133
in the formula, HPL0The user protection level under the assumption of no fault;
Figure BDA0003505187340000134
1-alpha of standard normal distribution1A 2-point of the molecule is,
Figure BDA0003505187340000135
2) a fault condition:
the calculation formula of the integrity risk is as follows:
PHMI，1＝P{|Δx|＞HPL|H1}P{σ＜σT|H1}P{H1}
in the formula, PHMI，1To assign to the integrity risk in case of a fault; h1Is a faulty assumption; p { |. DELTA.x| > HPL | H1The probability that the positioning error exceeds the user protection level under the assumption of failure; p { sigma < sigmaT|H1Is the probability that the fault detection statistic does not exceed the detection threshold under the assumption of fault, i.e. the miss rate PMD. When a fault occurs, the probability distribution of the horizontal positioning error is delta x-N (mu, sigma)x) Mu is faultAnd the deviation is estimated by using the minimum detectable coarse difference value under the condition that the fault detection rate is greater than or equal to 99.9%.
The probability that the positioning error exceeds the user protection level under the assumption of a fault can be derived:
Figure BDA0003505187340000136
the user protection level when containing a fault is then:
Figure BDA0003505187340000141
in the formula, HPL1A user protection level under the assumption of failure;
Figure BDA0003505187340000142
1-alpha of standard normal distribution2A 2-point of the molecule is,
Figure BDA0003505187340000143
Ai，1is the ith row and the 1 st column of the matrix A; a. thei，2Is the ith row and the 2nd column of the matrix A.
In summary, the user protection level in the horizontal direction is HPL ═ max { HPL0，HPL1}. Comparing the calculated user protection level with a protection limit value, and if the user protection level does not exceed the protection limit value, indicating that the integrity is available; and if the user protection level exceeds the protection limit value, warning the user.
The invention provides a thought and a method for an automatic driving oriented fusion navigation system integrity monitoring method, and a method and a way for realizing the technical scheme are many, the above description is only a preferred embodiment of the invention, and it should be noted that, for a person skilled in the art, a plurality of improvements and decorations can be made without departing from the principle of the invention, and the improvements and decorations should also be regarded as the protection scope of the invention. All the components not specified in this embodiment can be implemented by the prior art.

Claims (10)

1. An integrity monitoring method for an automatic driving-oriented fusion navigation system is characterized by comprising the following steps:
step 1, carrying out scene matching by using a carrier camera to obtain a reference position, and inverting the distance from a carrier to a characteristic point from the reference position;
step 2, utilizing carrier laser radar observation information and a machine learning algorithm to assist a global satellite navigation system to correct pseudo range errors;
step 3, tightly coupling the camera observed quantity and the global satellite navigation system observed quantity to construct an observation equation;
step 4, calculating a least square residual error, and constructing test statistics for fault detection;
step 5, calculating to obtain a standard deviation of the predicted positioning error, fitting a normal distribution of the positioning error and calculating a user protection level in the horizontal direction;
and 6, comparing the obtained user protection level with a protection limit value, and finishing the integrity monitoring of the fusion navigation system facing the automatic driving.
2. The autopilot-oriented fused navigation system integrity monitoring method as set forth in claim 1, wherein step 1 includes: sensing and detecting the surrounding environment by using a carrier camera, matching an observation result with a prior three-dimensional high-precision point cloud map, and estimating the position of a carrier under a local coordinate system; and taking the building boundary point and the road boundary point as feature points, and inverting the distance from the carrier reference position to the feature points according to the coordinate information of the feature points.
3. The autopilot-oriented fused navigation system integrity monitoring method as set forth in claim 2, wherein step 2 includes:
step 2-1, a global satellite navigation system receiver in a carrier and a laser radar are adopted to acquire data in an urban canyon, and a satellite signal carrier-to-noise ratio, a satellite altitude angle, the reflection intensity of the surface of a building and the distance from the carrier to the building, which are related to multipath error comparison, are adopted as input characteristics;
2-2, selecting pseudo-range errors as labels of each sample, and constructing a historical training data set;
and 2-3, excavating the relation between the input characteristic satellite signal carrier-to-noise ratio, the satellite altitude angle, the reflection intensity of the building surface and the distance from the carrier to the building and the output variable pseudo-range error by using a deep neural network algorithm, constructing a pseudo-range error prediction rule, and correcting the pseudo-range error.
4. The autopilot-oriented fused navigation system integrity monitoring method as recited in claim 3 wherein step 3 includes:
step 3-1, converting the northeast coordinates of the known characteristic points into geocentric coordinates;
and 3-2, positioning and solving by combining the observed pseudo range of the global satellite navigation system and the distance inverted by the camera.
5. The autopilot-oriented fused navigation system integrity monitoring method as recited in claim 4 wherein step 3-1 comprises:
the northeast coordinates of the known characteristic points are converted into geocentric coordinates, and the rotation matrix is as follows:
Figure FDA0003505187330000021
wherein L is longitude and B is latitude.
6. The method for monitoring the integrity of the fusion navigation system facing the automatic driving as claimed in claim 5, wherein in the step 3-2, the process of positioning and solving by combining the observed pseudorange of the global navigation satellite system and the distance inverted by the camera is as follows:
Figure FDA0003505187330000022
where ρ isG＝R+δGFor the global satellite navigation system observation equation, ρC＝R+δCIs a camera observation equation; wherein ρGObserving a pseudo range for the global satellite navigation system after pseudo range error correction predicted according to the deep neural network; rhoCThe distance to the feature point is inverted according to the camera positioning; r is the distance from the satellite or the characteristic point to the carrier; deltaGThe pseudo range errors of other uncompensated pseudo range errors are obtained; deltaCA camera range error;
estimating a global satellite navigation system observation equation by taking a carrier three-dimensional coordinate and a receiver clock error as unknown parameters; estimating three-dimensional coordinates of the carrier by using a camera observation equation; and expanding the geometric distance R according to Taylor series to obtain:
Figure FDA0003505187330000023
in the formula, R0The approximate geometric distance from the satellite or the characteristic point to the carrier; (x)i，yi，zi) Coordinates of the ith satellite or characteristic point; (x)0，y0，z0) Is the approximate coordinates of the carrier; (dx, dy, dz) is the increment of the vector coordinates; neglecting the non-linear error term yields:
Figure FDA0003505187330000024
where E (-) is the mathematical expectation operator; after the Taylor series linearization is utilized, the joint positioning problem of the close coupling of the global satellite navigation system observation data and the camera observation data is approximately converted into a linear problem, and the linear system is as follows:
ΔR＝A·dX
wherein Δ R ═ R-R0Is an observation from a global satellite navigation system or camera; dX ═ dX, dy, dz, dt]TDx, dy and dz are increment of three-dimensional coordinates of the carrier; dt is a clock error parameter of a global satellite navigation system receiver; design matrix a is as follows:
Figure FDA0003505187330000031
wherein (x)0，y0，z0) Is the approximate coordinates of the carrier; (x)1，y1，z1) To (x)m，ym，zm) Coordinates of 1 to m satellites; r1To RmIs the distance from 1 to m satellites to the carrier; (x)1，y1，z1) To (x)n，yn，zn) Coordinates of 1 to n feature points; r is1To RnThe distances from 1 to n characteristic points to the carrier; the correspondingly solved 4 parameters dx, dy, dz, dt are the increment of the three-dimensional coordinates of the carrier and the clock error of the global satellite navigation system receiver.
7. The method for monitoring the integrity of the fusion navigation system for automatic driving as claimed in claim 6, wherein in step 4, the least square residual is calculated, and the test statistic is constructed to perform the fault detection method including fault detection and fault identification;
the fault detection method comprises the following steps:
the least squares solution of the equation in step 3-2 is:
Figure FDA0003505187330000032
wherein the content of the first and second substances,
Figure FDA0003505187330000033
carrier coordinate increment and receiver clock difference estimation;
the distance residual vector obtained by calculation is as follows:
z＝(I-A(ATA)-1AT)ΔR
wherein I is an identity matrix; z is a distance residual vector; calculating a distance residual vector through the design matrix A, and after obtaining the distance residual vector, obtaining the error in the posterior unit weight of the distance residual square sum as follows:
Figure FDA0003505187330000034
wherein SSE is the sum of squares of distance residuals; adopting the error sigma in the unit weight after the test as the statistic of fault detection; when no fault exists, the distance observation errors of the satellite and the camera follow normal distribution of independent distribution, the mean value is 0, and the variance is
Figure FDA0003505187330000041
The method is obtained by the theory of statistical distribution,
Figure FDA0003505187330000042
obeying chi-square distribution with the degree of freedom of m + n-4; when there is a failure in the presence of a fault,
Figure FDA0003505187330000043
obeying chi-square distribution with the degree of freedom of m + n-4 and the non-centering parameter of lambda;
when the system has no fault, the system alarm is false alarm according to the given false alarm rate
Figure FDA0003505187330000044
Obtaining:
Figure FDA0003505187330000045
wherein H0Representing a no-fault hypothesis; t is an actual detection value; p { { T < T2}|H0Represents the probability that the system itself is fault-free and no fault is detected;
Figure FDA0003505187330000046
is chi with the degree of freedom of m + n-42Distribution probability density function, determining
Figure FDA0003505187330000047
Is detected by2By detecting the threshold value T2Further obtain the detection threshold value sigma of sigmaTComprises the following steps:
Figure FDA0003505187330000048
if σ>σTThen there is erroneous observations in the current gnss and camera observations.
8. The method for monitoring the integrity of the fusion navigation system for automatic driving as claimed in claim 7, wherein in step 4, the least square residual is calculated, and the test statistic is constructed to perform the fault detection method including fault detection and fault identification;
the fault identification method comprises the following steps:
fault identification inspection quantity d constructed based on least square residual vectori：
Figure FDA0003505187330000049
Wherein z isiDistance residual, Q, representing the ith observationziiRepresenting elements corresponding to the ith observed quantity in the distance residual error covariance matrix, wherein the statistic obeys standard normal distribution when no fault exists; averagely distributing the false alarm rate to m + n observation quantities, and obtaining a detection threshold value of the fault identification statistic as follows:
Figure FDA00035051873300000410
wherein, P { di>TdDenotes failure identification statisticsQuantity diGreater than a threshold value TdThe probability of (d);
Figure FDA00035051873300000411
a probability density function that is a standard normal distribution; when a fault exists, if di>TdAnd indicating that the ith observation is faulty.
9. The method as claimed in claim 8, wherein in step 5, the pseudorange error standard deviation of m satellites predicted by the current epoch and the horizontal accuracy factor value of satellite distribution are calculated and multiplied to obtain the standard deviation σ of predicted positioning errorxAnd calculating the user protection level in the horizontal direction by fitting a normal distribution of the positioning error, wherein the method comprises the following steps:
step 5-1, under the condition of no fault:
the integrity risk is calculated as follows:
PHMI，0＝P{|Δx|>HPL|H0}P{σ＜σT|H0}P{H0}
wherein, PHMI，0Integrity risks assigned to a no fault condition; HPL is the user protection level in the horizontal direction; h0No fault is assumed; Δ x is the horizontal positioning error; p { | Δ x>HPL|H0The probability that the positioning error exceeds the user protection level under the assumption of no fault; p { sigma < sigmaT|H0Is the probability that the failure detection statistic does not exceed the detection threshold under the no failure assumption, i.e.
Figure FDA0003505187330000055
Obtaining the probability P { | delta x | that the positioning error exceeds the user protection level under the assumption of no fault>HPL|H0The method is as follows:
Figure FDA0003505187330000051
by determining horizontal orientationCalculating the protection level by using the probability distribution of errors, and calculating the standard deviation sigma of the predicted positioning errorsxThe standard deviation of the normal distribution of the positioning error is Δ x to N (0, σ)x) The user protection level under the assumption of no fault is obtained as follows:
Figure FDA0003505187330000052
wherein, HPL0The user protection level under the assumption of no fault;
Figure FDA0003505187330000053
1-alpha of standard normal distribution1A 2-point of the molecule is,
Figure FDA0003505187330000054
step 5-2, in case of a fault:
the calculation formula of the integrity risk is as follows:
PHMI，1＝P{|Δx|>HPL|H1}P{σ＜σT|H1}P{H1}
in the formula, PHMI，1To assign to the integrity risk in case of a fault; h1Is a faulty assumption; p { | Δ x>HPL|H1The probability that the positioning error exceeds the user protection level under the assumption of failure; p { sigma < sigmaT|H1Is the probability that the fault detection statistic does not exceed the detection threshold under the assumption of fault, i.e. the miss rate PMD(ii) a When a fault occurs, the probability distribution of the horizontal positioning error is delta x-N (mu, sigma)x) Mu is fault deviation, and the minimum detectable coarse difference value under the condition that the fault detection rate is greater than or equal to 99.9 percent is used as the estimated value of mu;
the probability that the positioning error exceeds the user protection level under the assumption of a fault is obtained:
Figure FDA0003505187330000061
the user protection level when containing a fault is then:
Figure FDA0003505187330000062
in the formula, HPL1A user protection level under the assumption of failure;
Figure FDA0003505187330000063
1-alpha of standard normal distribution2A/2 split site, wherein
Figure FDA0003505187330000064
4i，1Is the ith row and the 1 st column of the matrix A; a. thei，2Is the ith row and 2nd column of matrix a.
10. The autopilot-oriented fused navigation system integrity monitoring method as set forth in claim 9, wherein step 6 includes:
calculating to obtain the user protection level in the horizontal direction, wherein the method comprises the following steps:
HPL＝max{HPL0，HPL1}
comparing the user protection level with a protection limit value, and if the user protection level does not exceed the protection limit value, enabling integrity to be available; and if the user protection level exceeds the protection limit value, giving an alarm to the user, and finally completing the integrity monitoring of the fusion navigation system facing the automatic driving.
CN202210136863.6A 2022-02-15 2022-02-15 Fusion navigation system integrity monitoring method for automatic driving Pending CN114545454A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210136863.6A CN114545454A (en) 2022-02-15 2022-02-15 Fusion navigation system integrity monitoring method for automatic driving

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210136863.6A CN114545454A (en) 2022-02-15 2022-02-15 Fusion navigation system integrity monitoring method for automatic driving

Publications (1)

Publication Number Publication Date
CN114545454A true CN114545454A (en) 2022-05-27

Family

ID=81676267

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210136863.6A Pending CN114545454A (en) 2022-02-15 2022-02-15 Fusion navigation system integrity monitoring method for automatic driving

Country Status (1)

Country Link
CN (1) CN114545454A (en)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114867022A (en) * 2022-06-07 2022-08-05 电子科技大学 FDI attack detection method in wireless sensor network positioning process
CN115112126A (en) * 2022-08-30 2022-09-27 交信北斗(北京)信息科技有限公司 GNSS/INS combined navigation system protection level inversion method
CN115265594A (en) * 2022-07-18 2022-11-01 北京航空航天大学 Multi-level autonomous integrity monitoring method and system for multi-source PNT information elastic fusion navigation
CN115291253A (en) * 2022-08-02 2022-11-04 东北大学 Vehicle positioning integrity monitoring method and system based on residual error detection
CN115390086A (en) * 2022-10-31 2022-11-25 智道网联科技(北京)有限公司 Fusion positioning method and device for automatic driving, electronic equipment and storage medium
CN115933356A (en) * 2023-01-09 2023-04-07 北京航空航天大学 High-precision time synchronization system and method of virtual atomic clock
CN116736339A (en) * 2023-08-11 2023-09-12 浙江中裕通信技术有限公司 Beidou autonomous monitoring and early warning method for control of forbidden navigation areas
CN115265594B (en) * 2022-07-18 2024-04-19 北京航空航天大学 Multi-source PNT information elastic fusion navigation multi-level autonomous integrity monitoring method and system

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114867022A (en) * 2022-06-07 2022-08-05 电子科技大学 FDI attack detection method in wireless sensor network positioning process
CN115265594A (en) * 2022-07-18 2022-11-01 北京航空航天大学 Multi-level autonomous integrity monitoring method and system for multi-source PNT information elastic fusion navigation
CN115265594B (en) * 2022-07-18 2024-04-19 北京航空航天大学 Multi-source PNT information elastic fusion navigation multi-level autonomous integrity monitoring method and system
CN115291253A (en) * 2022-08-02 2022-11-04 东北大学 Vehicle positioning integrity monitoring method and system based on residual error detection
CN115291253B (en) * 2022-08-02 2023-12-05 东北大学 Vehicle positioning integrity monitoring method and system based on residual error detection
CN115112126A (en) * 2022-08-30 2022-09-27 交信北斗(北京)信息科技有限公司 GNSS/INS combined navigation system protection level inversion method
CN115112126B (en) * 2022-08-30 2022-11-18 交信北斗(北京)信息科技有限公司 GNSS/INS combined navigation system protection level inversion method
CN115390086A (en) * 2022-10-31 2022-11-25 智道网联科技(北京)有限公司 Fusion positioning method and device for automatic driving, electronic equipment and storage medium
CN115933356A (en) * 2023-01-09 2023-04-07 北京航空航天大学 High-precision time synchronization system and method of virtual atomic clock
CN115933356B (en) * 2023-01-09 2023-08-22 北京航空航天大学 High-precision time synchronization system and method for virtual atomic clock
CN116736339A (en) * 2023-08-11 2023-09-12 浙江中裕通信技术有限公司 Beidou autonomous monitoring and early warning method for control of forbidden navigation areas
CN116736339B (en) * 2023-08-11 2023-11-03 浙江中裕通信技术有限公司 Beidou autonomous monitoring and early warning method for control of forbidden navigation areas

Similar Documents

Publication Publication Date Title
CN114545454A (en) Fusion navigation system integrity monitoring method for automatic driving
US9146322B2 (en) Hybrid system and device for calculating a position and for monitoring its integrity
US11143765B2 (en) Reducing bias impact on GNSS integrity
Jiang et al. A fault-tolerant tightly coupled GNSS/INS/OVS integration vehicle navigation system based on an FDP algorithm
CN110007317B (en) Star-selection optimized advanced receiver autonomous integrity monitoring method
US20160040992A1 (en) Positioning apparatus and global navigation satellite system, method of detecting satellite signals
CN111060133B (en) Integrated navigation integrity monitoring method for urban complex environment
CN103454650B (en) Method for monitoring satellite integrity with vision as auxiliary
CN101395443A (en) Hybrid positioning method and device
CN101833101A (en) Completeness or adequateness monitoring method and device based on local area augmentation system (LAAS)
Al Hage et al. Localization integrity for intelligent vehicles through fault detection and position error characterization
CN109471143B (en) Self-adaptive fault-tolerant train combined positioning method
JP2011517770A (en) Apparatus and method for real-time monitoring of satellite navigation system integrity
CN110879407B (en) Satellite navigation observed quantity innovation detection method based on integrity risk model
Won et al. GNSS carrier phase anomaly detection and validation for precise land vehicle positioning
EP4024087A2 (en) Gnss signal spoofing detection via bearing and/or range sensor observations
CN110941000A (en) Method for monitoring integrity of precise single-point positioning
CN114235007B (en) Positioning and integrity monitoring method and system for APNT service
EP3508884B1 (en) Intelligent satellite exclusion for multipath mitigation based on line of sight
CN111812680A (en) Integrity monitoring of primary and derived parameters
CN114265090A (en) Receiver autonomous integrity monitoring method based on Bayesian inspection
US20230341563A1 (en) System and method for computing positioning protection levels
Al Hage et al. High integrity localization with multi-lane camera measurements
CN115561782B (en) Satellite fault detection method in integrated navigation based on odd-even vector projection
CN116299599A (en) INS-assisted GNSS pseudo-range coarse difference detection method

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