CN109490916A - A kind of GNSS receiver autonomous integrity monitoring method - Google Patents
A kind of GNSS receiver autonomous integrity monitoring method Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/23—Testing, monitoring, correcting or calibrating of receiver elements
Abstract
The invention discloses a kind of GNSS receiver autonomous integrity monitoring methods, are related to receiver autonomous integrity monitoring technology, belong to the technical field of measurement test.This method uses the monitoring that GNSS receiver autonomous integrity is realized based on CKF and new breath extrapolation, the test statistics at moment of extrapolating each time is constructed by newly ceasing the new breath generated in extrapolation process and its variance-covariance battle array, small gradual pseudorange biases can be examined, dynamic updates the Mishap Database for generating machine learning rule while differentiated and classified using test statistics of the machine learning algorithm to each extrapolation moment, the judging rules of the unmodeled fault type of on-line study, it is less with calculation amount, precision is suitable, the advantages of suitable for nonlinear system and capable of monitoring small gradual pseudorange biases to high confidence level.
Description
Technical field
The invention discloses a kind of GNSS receiver autonomous integrity monitoring methods, are related to receiver autonomous integrity monitoring
Technology belongs to the technical field of measurement test.
Background technique
To RAIM (Receiver Autonomous Integrity Monitoring, receiver autonomous integrity monitoring)
Research start from last century the mid-80.In navigation association, the U.S. in 1986, Y.C.Lee proposes Agonists by Distance Comparison Method,
That is, when making integrity decision every time only with the observed quantity of current epoch, according to the weighted sum of five satellite distance errors
Failure is judged whether there is, this method, which is only used for detection failure, not can determine that fault satellites but.Next year, R.M.Kalafus is just
Minimum satellite vehicle number needed for formula proposes the concept of RAIM and demonstrates fault-finding and Fault Isolation.1988, Bradford
W.Parkinson proposes least square method by carrying out processing to residual error, and this method handles pseudorange by least-squares estimation
After obtaining residual error, the probability density characteristics according to residual error carry out decision；The same year, Mark A.Sturzar propose parity vector
Noise error is placed in the progress fault detection of odd even space by dexterously carrying out QR decomposition to observation battle array by method, verified, minimum
Square law and parity vector method are equivalent in result.In view of the suggestiveness of parity vector method computationally, this method becomes
The recommendation RAIM algorithm of intact sex service in GPS application.
The design pattern of Agonists by Distance Comparison Method, least square method and odd_even adjudgement rule can be included in instant method, also be " snapshot
The scope of method " carries out the influence that not will receive the conception of history measured data when decision every time.Work as since the judgement to integrity is based only on
The observation at preceding moment, the obvious fault occurred suddenly can be detected in traditional RAIM algorithm, in stationary random process
Failure is then not sensitive enough.
In order to improve the sensitivity of RAIM algorithm, scholar is made that more effort.Wang Lingxuan et al. proposes general using priori
Rate model is to fault modeling, by minimum risk cost arbitrary target Functional Analysis residual error statistic, to optimize threshold value to mention
It is highly sensitive.Li Chao et al. then devises RAIM algorithm according to least-square residuals method, and using Beidou satellite navigation system into
Verifying is gone.Ran Jianhua etc. establishes the RAIM algorithm based on polynary accumulation sum, which has carried out place to parity vector residual error
Reason not only can detect biggish saltant type failure with a step, can also detect the accumulation type failure of moderate, but for larger
Odd even residual error average drifting then lose the ability further detected.Wang Shitai proposes the RAIM algorithm based on M estimation, should
The method that the robust characteristic that algorithm is estimated using M devises fault-finding and Fault Isolation, and pass through simulating, verifying algorithm
Performance is substantially better than least square method.It is devised for the larger problem of Multipath Errors, tension et al. under city and canyon environment
Adaptive RAIM algorithm tests to positioning accuracy before and after troubleshooting using precision filtered device is excluded, so that enhancing is fixed
The accuracy of position.
With BDS (BeiDou Navigation Satellite System, Chinese Beidou satellite navigation system),
The global position systems such as GLONASS (Global Navigation Satellite System, Global Navigation Satellite System)
It emerges and opens, more constellation RAIM algorithms also result in the attention of some scientific research personnel.Chen Ting is in paper by single constellation
With the performance evaluation of RAIM algorithm under more constellation combinations, it is indicated that the RAIM performance under more constellation combinations is significantly stronger than single constellation feelings
Condition.Wang Haijun proposes more epoch accumulation double star failure methods, which increases and improve with epoch number, to micro-
Poor, middle difference and rough error can be detected.Lu Deqian et al. proposes the RAIM algorithm dynamically distributed based on integrity index, leads to
Crossing dynamic allocation omission factor reduces vertical protection class, to improve the availability of RAIM.Wang Ershen et al., which gives, to be based on
BDS/GPS positioning weighted average solution algorithm, the algorithm to optimal weighting be averaged resolving Algorithm weighted factor optimize from
And test statistics is designed by positioning solution, it is finally debugged using weighted least-squares method, which can detecte same
Double star failure of the Shi Fasheng in various constellations.
Traditional RAIM algorithm is carried out after by system linearization, with the proposition and application of various algorithm, people
Be also to develop the RAIM algorithm of suitable nonlinear system to make efforts.Song Jiancai et al. is proposed to be filtered based on spreading kalman
The RAIM algorithm of wave, the algorithm carry out recursion using instant data and historical data simultaneously, can take into account time-domain information, in addition,
The step of handling observed quantity using proper orthogonal decomposition in paper, eliminating finding the inverse matrix effectively reduces calculation amount, and by imitative
Really demonstrate the ability of its fault detection.The RAIM that Wang Ershen et al. proposes the particle filter based on chaotic particle swarm optimization is calculated
Method, the algorithm can detect and debug under conditions of coloured noise system, they also proposed based on hereditary grain
The RAIM algorithm of son filtering improves particle filter accuracy come the problem coped with sample degeneracy with sample exhaustion to enhance RAIM calculation
The detection performance of method.
In addition, various enhancing RAIM algorithms more attract attention.Shi Yibing gives the RAIM algorithm of clock auxiliary, the party
Method predicts receiver clock deviation using discrete Grey System Model, and the receiver clock-offsets sequence of prediction is equivalent to
One satellite, to promote the performance of RAIM in the case of few star.Sun Minghan et al. is also to three kinds of judgements of RAIM algorithm availability
Method (δ Hmax, ARP and HPL method) carries out analysis comparison, and demonstrates the equivalence of δ Hmax and HPL method.
On the whole, according to the analysis of above-mentioned domestic and foreign literature it is found that existing RAIM technology has the disadvantages that (1) passes
RAIM algorithm of uniting is established on the basis of linearizing observational equation, and precision is still improved space because rounding error；(2) traditional
RAIM algorithm is concerned only with current time information, is not concerned with time-domain information, therefore can ignore the control information in time domain.Traditional
RAIM algorithm calculates be independently of each historical data every time, therefore then reacts not sensitive enough to the failure in stationary random process, that is,
Traditional RAIM algorithm cannot find the failure that error is gradually increased at any time in time, to unmodeled in fault satellites exclusion process
Fault type is difficult to be detected.
Summary of the invention
Goal of the invention of the invention is the deficiency for above-mentioned background technique, and it is independently intact to provide a kind of GNSS receiver
Property monitoring method, by combine volume Kalman filtering algorithm and information extrapolation on-line study fault type, in terms of less
Calculation amount and suitable precision preferably monitor small gradual pseudorange biases, realize the detection to slow unmodeled failure, solve
Integrity monitoring is because being difficult to extract the technical issues of feature of every kind of satellite failure is without adapting to fault detection demand.
The present invention adopts the following technical scheme that for achieving the above object
A kind of GNSS receiver autonomous integrity monitoring method filters the shape of algorithm prediction navigation system using volume Kalman
State estimated value simultaneously updates measurement information, according to before volume Kalman's Navigation Filter several epoch it is new breath and its variance-association
Variance matrix carries out the test statistics at new breath extrapolation building each extrapolation moment, collect the test statistics at multiple extrapolation moment with
Integrated database is constructed, the test statistics at current extrapolation moment is differentiated using machine learning method and then determines that fault satellites are compiled
Number, when machine learning method differentiates current extrapolation moment failure, the Mishap Database in integrated database is updated, from updated
The rule for being used for machine learning next time is extracted in integrated database.
Scheme is advanced optimized as GNSS receiver autonomous integrity monitoring method, is calculated using volume Kalman filtering
The state estimation of method prediction navigation system simultaneously updates measurement information comprising time update and measures update:
Time updates:
To the variance-covariance matrix P of k epoch state errorkCholesky is carried out to decompose to obtain k epoch state error
The lower triangular matrix S of variance-covariance matrixk, according to expression formula:Choose the of k epoch state estimation
I volume point For the state estimation of k epoch, ξiFor the weight parameter of i-th of volume point,2n is volume point sum, k epoch state estimation
I-th of volume pointI-th of volume point of the k+1 epoch state estimation transmitted through state transition functionForF () is the state transition function of navigation system, wk、ΓkFor the system noise and its drive of k epoch
Dynamic matrix, according to the state estimation of the k+1 epoch of k epoch status predicationForK+1 is gone through
The variance-covariance battle array P of first state errork+1/kFor
QkFor the system noise variance matrix of k epoch；
It measures and updates:
To the variance-covariance matrix P of k+1 epoch state errork+1/kCholesky is carried out to decompose to obtain k+1 epoch state
The lower triangular matrix S of the variance-covariance matrix of errork+1/k, i-th of volume point of k+1 epoch state estimationForI-th of volume point of k+1 epoch state estimationFunction passes are measured to obtain
K+1 epoch measure i-th of volume point of estimated valueForH () is the amount of navigation system
Survey function, vkFor the measurement noise of k epoch, the measurement estimated value of k+1 epochForK+1 epoch
State estimationForFor k+1 epoch measuring value and
Measure estimated value, Kk+1For the filtering gain of k+1 epoch, For k+1 epoch prediction it is mutual
Variance-covariance battle array is closed,
As the further prioritization scheme of GNSS receiver autonomous integrity monitoring method, navigated according to volume Kalman
New breath and its variance-covariance battle array before filter several epoch carry out the inspection system at new breath extrapolation building each extrapolation moment
The expression formula of metering are as follows: Tavg,tIt is outer
Push away the test statistics of moment t, vk-m、Ak-mIt is newly ceased for k-m epoch and its variance-covariance battle array,
Aavg -1The sum average value of variance-covariance inverse matrix is newly ceased for k-m epoch,Rk-mFor the measuring noise square difference battle array of k-m epoch.
Scheme is advanced optimized as GNSS receiver autonomous integrity monitoring method, is worked as using the differentiation of machine learning method
The method of the test statistics at preceding extrapolation moment and then determining fault satellites number is to be determined using machine learning rule current outer
When pushing away moment failure, newly ceasing the corresponding satellite number of greatest member on variance matrix-covariance matrix leading diagonal is fault satellites
Number.
As the scheme that advanced optimizes of GNSS receiver autonomous integrity monitoring method, the event in integrated database is updated
The method of barrier database is when the fault data at current extrapolation moment Mishap Database least significant end moment is replaced with current extrapolation
The test statistics at quarter.
As the scheme that advanced optimizes of GNSS receiver autonomous integrity monitoring method, measurement information is the pseudorange of GPS
Observation.
A kind of computer readable storage medium, is stored thereon with computer program, realization when which is executed by processor
Above-mentioned GNSS receiver autonomous integrity monitoring method.
A kind of GNSS receiver, comprising: the meter that memory, processor and storage are run on a memory and on a processor
Calculation machine program, processor realize above-mentioned GNSS receiver autonomous integrity monitoring method when executing computer program.
The present invention by adopting the above technical scheme, has the advantages that
(1) present invention is using the monitoring for realizing GNSS receiver autonomous integrity based on CKF and new breath extrapolation, by newly ceasing
The new variance-covariance battle array for ceasing and newly ceasing generated in extrapolation process constructs the test statistics at each extrapolation moment, can examine
Small gradual pseudorange biases, dynamic is updated for generating machine while differentiating each epoch test statistics using machine learning method
The Mishap Database of learning rule, the judging rules of the unmodeled fault type of on-line study, the faulty model without establishing,
Without extracting, faulty feature, that is, be suitable for the requirement of complete detection failure.
(2) it can adapt to the non-linear behavior of navigation system using volume Kalman filtering algorithm estimating system state, no
It only avoids in EKF algorithm and the first approximation of nonlinear system is handled, and estimated accuracy can be made to be promoted to three ranks, be suitable for
The UAV system of high dynamic.
Detailed description of the invention
Fig. 1 is the flow chart of GNSS receiver autonomous integrity monitoring method disclosed in the present application.
Specific embodiment
It is described in detail below with reference to technical solution of the Fig. 1 to invention.
Core content of the present invention is the RAIM algorithm based on CKF and new breath extrapolation, and is produced according in new breath extrapolation process
New breath and its variance-covariance battle array before raw several epoch establish the test statistics at extrapolation moment, with test statistics
For the database of fault verification, real-time update dicision rules while whether determining failure, with calculation amount, less, precision is fitted
Preferably, the advantages of monitoring small gradual pseudorange biases suitable for nonlinear system and energy high confidence level.
CKF (Cubature Kalman Filter, volume Kalman filtering) algorithm relies on gaussian filtering frame, according to three
Rank spherical surface-phase diameter volume rule, Posterior Mean and variance-association side from the angle approximate non-linear function passes of numerical integration
Difference.By the priori mean value and variance-covariance of state, volume point is chosen by volume rule, volume point is through nonlinear function
Transmitting, then do weighting processing and come approximation state Posterior Mean and variance-covariance.
Core of the invention content is filter building, considers following non-linear Gaussian system:
xk=f (xk-1)+Γk-1wk-1(1),
Zk=h (xk)+vk(2),
Wherein, xkFor system mode vector；ZkTo measure vector；F () and h () is respectively the state of nonlinear system
Transfer function and measurement function；Γk-1For the driving matrix of system noise；wk-1And vkRespectively system noise and measurement noise,
It is white Gaussian noise and uncorrelated.
(1) CKF algorithm
Time updates:
Known k moment system mode vector xkStatistical property beTo this, the state vector at moment is missed
The variance-covariance battle array P of differencekCarrying out Cholesky decomposition has: Pk=SkSk T, enable ξ1, ξ2..., ξ2nFor the point of 2n equal weight,
Choose volume point are as follows:
The volume point transmitted by system are as follows:
Obtain the status predication value at k+1 moment:
QkFor the system noise variance matrix at k moment, the State error variance-covariance matrix at k+1 moment are as follows:
It measures and updates:
To Pk+1/kCarrying out Cholesky decomposition has: Pk+1/k=Sk+1/kSk+1/k T, calculating volume point has:
Have by measuring function passes volume point:
Obtain the observation predicted value at k+1 moment:
Obtain the error in measurement variance-covariance battle array at k+1 moment:
K+1 moment one-step prediction cross-correlation variance-covariance battle array are as follows:
The filtering gain at k+1 moment are as follows:
The state estimation at k+1 moment are as follows:
The State error variance at k+1 moment-covariance estimated value are as follows:
(2) system modelling
1. determining state equation:
Building system mode vector is xk=[r τ d τ]T, wherein r is no-manned plane three-dimensional spatial position vector, and τ is to receive
Machine clock deviation, d τ are receiver clock frequency drift value.
At this point, first-order system state-transition matrix are as follows:
2. determining measurement equation:
zk=h (xk)+vk(15),
Measuring value is the Pseudo-range Observations of GPS, it may be assumed that
Wherein, ρiFor Pseudo-range Observations, (Xi, Yi, Zi) it is position of i-th moonscope moment in space；(X, Y, Z)
Moment is observed in the position in space for receiver；VionAnd VtropRespectively Ionospheric delay correcting number and tropospheric delay correction
Number；C is the light velocity；WithRespectively receiver clock-offsets and satellite clock correction；εiTo measure noise.
3. determining test statistics based on new breath extrapolation:
For small gradual pseudorange biases, the present invention improves traditional new breath detection method, using fusion engineering
The new breath extrapolation practised carries out the monitoring of receiver consistency.In newly breath extrapolation, using two volume Kalman filter,
One is Navigation Filter: under system non-failure conditions, whole measurement informations are updated for state, and filter result is only used for leading
Boat；Secondly being Recursive Filtering device: the state recursion before collecting T epoch of Navigation Filter obtains the system shape of current time k
State information saves the new breath generated in recursive process.
By the system state estimation before T epochWith State error variance-covariance matrix Pk-TWhen being extrapolated to current
K is carved, the state estimation and State error variance-covariance matrix generated in extrapolation process is denoted asWithWherein, 1≤m≤
T。
When system fault-free, Normal Distribution, v are newly ceasedk~N (0, Vk), VkFor the variance matrix of multiple normal distribution.Newly
BreathVariance-covariance matrix be Ak-m, convolution (10) has,
Provide the test statistics of extrapolation moment t
Wherein:
4. the realization of the fault detection based on machine learning method:
Present invention determine that the method for failure is different from carrying out the conventional method of fault verification using threshold value comparison, using machine
Device learning method determines whether there is failure, and the fault type new based on big data on-line study, has lower false detection rate
With higher confidence level.
1) Primary Stage Data prepares: the unmanned plane during flying of (such as urban canyons) is corresponded under scene according to application scenarios pre-acquired
Data newly cease v according to above-mentioned volume kalman filter method building systemk, collect the test statistics T at each extrapolation momentAvg, t,
Establish data warehouse under initial line huge enough；
2) data markers and training: according to the satellite health status parameter decision satellite in satellite ephemeris with the presence or absence of event
Barrier, label failure extrapolate test statistics corresponding to the moment as " 1 ", and test statistics corresponding to the fault-free extrapolation moment is
" 0 " is marked to construct integrated database D0 database under initial line, wherein including initial Mishap Database D1
(each data markers of the inside are " 1 ") and normal data library D2 (each data markers of the inside are " 0 ")；
3) it fault verification: completes primary new breath extrapolation every and is currently extrapolated the test statistics T of moment tAvg, tAfterwards, it adopts
With the subordinating degree function based on fuzzy logic to the test statistics T of current extrapolation moment tAvg, tIt is marked, works as inspection statistics
Measure TAvg, tWhen being marked as " 0 ", then it is judged to fault-free, when being marked as " 1 ", is then judged to failure, is marked as the inspection system of " 1 "
Metering will be used for the real-time update of integrated database, be extracted from updated integrated database using neural metwork training device
Out 4) fuzzy rule for next extrapolation moment fault verification, the update of integrated database are detailed in.It is new to cease when system fault-free
The equal Normal Distribution of each element in sequence.When a fault has occurred, the variance of corresponding residual error error increases.Therefore, work as system
In there are when failure, it is new that cease the corresponding satellite number of greatest member on variance-covariance battle array leading diagonal be that fault satellites are compiled
Number.Failure determine after, system by so that complete paired fault alarm.
4) real-time update is used for the Mishap Database of neural metwork training: will currently extrapolate moment Mishap Database D1tMost
The fault data at end moment replaces with the data that " 1 " is labeled as by the fuzzy rule that currently extrapolation moment neural network extracts,
Obtain updated Mishap Database D1t+1, then merge the normal data library D2 remained unchanged and rebuild for neural network instruction
Practice the integrated database D0 that device is further trainedt+1, to realize the online updating of failure tranining database.
Claims (8)
1. a kind of GNSS receiver autonomous integrity monitoring method, which is characterized in that filter algorithm prediction using volume Kalman and lead
The state estimation of boat system simultaneously updates measurement information, according to before volume Kalman's Navigation Filter several epoch it is new breath and
Its variance-covariance battle array carries out the test statistics at new breath extrapolation building each extrapolation moment, collects the inspection at multiple extrapolation moment
The test statistics and then determining event that statistic is tested to construct integrated database, using the machine learning method differentiation current extrapolation moment
Hinder satellite number, when machine learning method differentiates current extrapolation moment failure, updates the Mishap Database in integrated database, from
The rule for being used for machine learning next time is extracted in updated integrated database.
2. a kind of GNSS receiver autonomous integrity monitoring method according to claim 1, which is characterized in that use volume card
The state estimation of Kalman Filtering algorithm prediction navigation system simultaneously updates measurement information comprising time update and measures update:
Time updates:
To the variance-covariance matrix P of k epoch state errorkCholesky is carried out to decompose to obtain the variance-of k epoch state error
The lower triangular matrix S of covariance matrixk, according to expression formula:Choose i-th of appearance of k epoch state estimation
Plot pointFor the state estimation of k epoch, ξiFor the weight parameter of i-th of volume point,2n is volume point sum, k epoch state estimation
I-th of volume pointI-th of volume point of the k+1 epoch state estimation transmitted through state transition functionForF () is the state transition function of navigation system, wk、ΓkFor k epoch system noise and
It drives matrix, according to the state estimation of the k+1 epoch of k epoch status predicationFork
The variance-covariance battle array P of+1 epoch state errork+1/kForQkFor the system noise variance matrix of k epoch；
It measures and updates:
To the variance-covariance matrix P of k+1 epoch state errork+1/kCholesky is carried out to decompose to obtain k+1 epoch state error
Variance-covariance matrix lower triangular matrix Sk+1/k, i-th of volume point of k+1 epoch state estimationForI-th of volume point of k+1 epoch state estimationFunction passes are measured to obtain
K+1 epoch measure i-th of volume point of estimated valueForH () is the amount of navigation system
Survey function, vkFor the measurement noise of k epoch, the measurement estimated value of k+1 epochForK+1 epoch
State estimationFor For the measuring value and amount of k+1 epoch
Survey estimated value, Kk+1For the filtering gain of k+1 epoch, For k+1 epoch prediction it is mutual
Variance-covariance battle array is closed,
3. a kind of GNSS receiver autonomous integrity monitoring method according to claim 2, which is characterized in that according to volume card
New breath and its variance-covariance battle array before Germania Navigation Filter several epoch carry out new breath extrapolation and construct each extrapolation moment
Test statistics expression formula are as follows:
TAvg, tFor the test statistics for the moment t that extrapolates, vk-m、Ak-mIt is newly ceased for k-m epoch and its variance-covariance battle array,Aavg -1The sum average value of variance-covariance inverse matrix is newly ceased for k-m epoch,Rk-mFor the measuring noise square difference battle array of k-m epoch.
4. a kind of GNSS receiver autonomous integrity monitoring method according to claim 1, which is characterized in that use engineering
Habit method differentiates the test statistics at current extrapolation moment and then the method for determining fault satellites number is, using machine learning rule
When determining current extrapolation moment failure, newly ceasing the corresponding satellite number of greatest member on variance matrix-covariance matrix leading diagonal is
For fault satellites number.
5. a kind of GNSS receiver autonomous integrity monitoring method according to claim 1, which is characterized in that update comprehensive number
Method according to the Mishap Database in library is to replace with the fault data at current extrapolation moment Mishap Database least significant end moment
The test statistics at current extrapolation moment.
6. a kind of GNSS receiver autonomous integrity monitoring method according to claim 1, which is characterized in that the measurement letter
Breath is the Pseudo-range Observations of GPS.
7. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is held by processor
GNSS receiver autonomous integrity monitoring method described in claim 1 is realized when row.
8. a kind of GNSS receiver, comprising: the calculating that memory, processor and storage are run on a memory and on a processor
Machine program, which is characterized in that the processor realizes GNSS receiver described in claim 1 when executing the computer program
Autonomous integrity monitoring method.
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