CN110203254A - The safety detection method of Kalman filter in train positioning system - Google Patents
The safety detection method of Kalman filter in train positioning system Download PDFInfo
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Abstract
The present invention relates to a kind of safety detection methods of Kalman filter in train positioning system, method includes the following steps: 1) carrying out fault diagnosis to sensor；2) data based on the first axle sensor, GNSS and orbital electron map obtain the positioning result of train using extension Kaman's filtering equations；3) Kalman filtering diverging inspection, orbital electron map adjunct test and the second axle sensor adjunct test are carried out to train positioning result；4) after by examining, final position location and the security interval of train positioning result are calculated；5) fault tree is designed based on above-mentioned calculating process, safety analysis is carried out to train system.Compared with prior art, the present invention provides the system-level malfunction tree analysis method of train positioning, have many advantages, such as reliable, stable, safe.
Description
Technical field
The present invention relates to data fusion, data check and failure tree analysis (FTA) fields, position system more particularly, to a kind of train
The safety detection method of Kalman filter in system.
Background technique
Now with the development of Beidou satellite alignment system, it is based on GNSS, wheel speed sensors, the train of orbital electron map
Integrated positioning system is gradualling mature.Compared to traditional train positioning system, which has positioning accuracy height, rail
The advantages that other equipment dependence is low, consecutive tracking, is very suitable to the positioning of low-density route.But satellite-based positioning system is also deposited
In inborn deficiency, positioning is easy to be influenced by the factor of environment, signal shielding, signal interference, the factors such as multipath effect
Caused by spacing wave available risk may bring security risk, especially below overpass or in tunnel, GNSS is fixed
Position is often invalid.Therefore multisensor is used to be combined positioning, to reach precision and stability requirement.
Kalman filter is widely used in the processing of multisensor location data, especially for GNSS and IMU with
And the integrated positioning of wheel speed sensors.But train Positioning Technology of the country based on GNSS is still in research and development at present and experiment is tested
In the card stage, there is no mature research, domestic colleges and universities give cell level for the analysis of stability and safety for system
Fault Tree Analysis, but for integrated positioning, it is based especially on the safety of the multi sensor combination positioning of Kalman filtering
System lacks the effective ways of fault Tree Analysis.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of train positioning systems
The safety detection method of middle Kalman filter.
The purpose of the present invention can be achieved through the following technical solutions:
The safety detection method of Kalman filter in a kind of train positioning system, the sensing in the train positioning system
Device includes GNSS, orbital electron map, the first axle sensor and the second axle sensor, the train integrated positioning system
Fault detection method the following steps are included:
S1: fault diagnosis is carried out to the first axle sensor, the second axle sensor, GNSS and orbital electron map, all
When fault-free, S2 is carried out；Otherwise terminate；
S2: measurement data, GNSS location data and orbital electron map datum based on the first axle sensor utilize expansion
Kalman Filtering equation is opened up, the positioning result of train is obtained, carries out S3；
S3: it to S2 train positioning result obtained, carries out Kalman filtering diverging and examines, if it is decided that go out train positioning
As a result it does not dissipate, then carries out S4, otherwise determine Kalman filtering diverging, do not use filter result currently；
S4: S2 train positioning result obtained is projected into orbital electron map datum, obtains lateral deviation；Set rail
Road electronic map detection threshold value carries out S5, otherwise determines karr if lateral deviation is less than orbital electron map detection threshold value
Graceful filtering error is overproof, does not use filter result currently；
S5: projecting to orbital electron map datum for S2 train positioning result obtained, obtains in positioning according to the time period
Journey；Axle sensor detection threshold value is set, if in each period, positioning mileage and the measurement of the second axle sensor apart from it
Difference is less than axle sensor detection threshold value, then carries out S6, otherwise determines that Kalman Filter Residuals are overproof, current without using filtering knot
Fruit；
S6:；Determine that current Kalman filtered results are available, obtains current safety positioning area using current Kalman filtering
Between；
S7: designing the fault tree of the train integrated positioning system based on Kalman filtering, carries out safety to train system
Property analysis.
Preferably, the detailed process of the step S2 includes:
S201: train k moment actual lateral coordinates are obtained, longitudinal coordinate, lateral velocity, longitudinal velocity, are laterally accelerated
Degree and longitudinal acceleration；
S202: according to the measurement data of the first axle sensor and orbital electron map datum, the acquisition train k moment is detected
Lateral velocity and longitudinal velocity；The cross detected according to GNSS location data and orbital electron map datum, acquisition train k moment
To coordinate and longitudinal coordinate；
S203: being based on S201 data obtained, obtains transverse state space, the longitudinal direction state space of train；It is based on
S202 data obtained obtain the cross measure space and longitudinal measurement space of train；
S204: transverse state space, longitudinal state space, cross measure space and longitudinal measurement space based on train,
Using Extended Kalman filter equation, the horizontal and vertical information sequence of train, the i.e. positioning result of train are obtained.
Preferably, in the step S203, transverse state space Xe(k) expression formula are as follows:
Xe(k)=[de(k),ve(k),ae(k)]
In formula, deIt (k) is train k moment actual lateral coordinates, veIt (k) is train k moment actual lateral velocity, ae
It (k) is train k moment actual transverse acceleration；
The longitudinal direction state space Xn(k) expression formula are as follows:
Xn(k)=[dn(k),vn(k),an(k)]
In formula, dnIt (k) is train k moment actual longitudinal coordinate, vnIt (k) is train k moment actual longitudinal velocity, an
It (k) is train k moment actual longitudinal acceleration；
The cross measure space Ze(k) expression formula are as follows:
In formula,For train k moment GNSS measurement lateral coordinates,For the first wheel shaft of train k moment biography
The lateral velocity of sensor measurement；
Longitudinal measurement space Zn(k) expression formula are as follows:
In formula,For train k moment GNSS measurement longitudinal coordinate,For the first wheel shaft of train k moment biography
The longitudinal velocity of sensor measurement.
Preferably, in the step S204, the expression formula of Extended Kalman filter equation are as follows:
X (k)=AX (k-1)+BU+W (k)
Z (k)=HX (k)+V (k)
In formula, X (k) is the state space at train k moment, and A is state-transition matrix, and U is to input smoothed out acceleration,
B is acceleration transfer matrix, and W is system noise, and Z (k) is the measurement space at train k moment, and H is calculation matrix, and V is that measurement is made an uproar
Sound.
Preferably, in the step S3, the detailed process that Kalman filtering diverging is examined includes:
S301: using inequality of variance criterion, the positioning result horizontal and vertical to train is tested respectively, if meeting
Then carry out S302；Otherwise, it is determined that for diverging；
The expression formula of the inequality of variance criterion are as follows:
In formula, VkFor the innovation sequence at k moment, r >=1 is reserve factor, PkFor the variance matrix of k moment innovation sequence,
S302: using Error Inequation criterion, the positioning result horizontal and vertical to train is tested respectively, if meeting
Then carry out S303；Otherwise, it is determined that for diverging；
The expression formula of the Error Inequation criterion are as follows:
In formula,For the measurement error of k moment innovation sequence, | | Zmin| | it is limited for preset measurement error；
S303: using covariance modular inequality criterion, the positioning result horizontal and vertical to train is tested respectively, if
Meet, is then judged to not dissipating；Otherwise, it is determined that for diverging；
The expression formula of the covariance modular inequality criterion are as follows:
||Pk||≤||Pmin||
In formula, | | Pk| | it is the mould of k moment innovation sequence covariance matrix, | | Pmin| | it is preset covariance mould
Threshold value.
Preferably, in the step S4, the expression formula of orbital electron map detection threshold value basis of design are as follows:
In formula, σkalmanPoor for Kalman filtering localization criteria, λ is threshold coefficient.
Preferably, precision takes 4~4.2 to the λ according to the map.
Preferably, in the step S5, the expression formula of axle sensor detection threshold value basis of design are as follows:
In formula,For the detection threshold value of the second axle sensor in Δ T time section,It is in Δ d mileage difference second
The standard deviation of axle sensor,It is poor for Kalman filtering localization criteria in Δ d mileage difference；
The positioning mileage of each period and the second axle sensor measure the expression formula apart from its difference are as follows:
In formula, d is mileage,For the detection mileage of the second axle sensor in Δ T time section,When for Δ T
Between in section Kalman filtering innovation sequence positioning mileage,For all inspections of the second axle sensor before the current T moment
Mileage is surveyed,For all detection mileages of the second axle sensor before current T moment Δ T time section,For current T
All Kalman filterings position mileage before moment,For Kalman's filter all before current T moment Δ T time section
Wave positions mileage.
Preferably, in the step S6, current safety positions section UsafeThe expression formula of calculating are as follows:
Usafe=[dkalman-ησkalman-β,dkalman+ησkalman+β]
In formula, σkalmanThe variance of mileage is positioned for Kalman filtering innovation sequence, η is by σkalmanThe multiple of amplification, β root
It is chosen according to initial data feature etc., takes 1~1.5 times of σmap, wherein σmapIndicate orbital electron map datum precision.
Compared with prior art, the invention has the following advantages that
(1) inspection of the present invention to security of system is introduced on the basis of train integrated positioning system Kalman filtering algorithm
It tests, it is determined that the security interval of train positioning result and the fault tree of Kalman filtering system can accomplish to improve Kalman's filter
The accuracy for the train positioning result that wave algorithm calculates, the safety for judging train positioning result, and to unsafe train
Positioning result analyzes failure cause, detects to failure, has high reliablity, highly-safe, the high advantage of availability.
(2) present invention first judges the operating status of sensor, it is ensured that meets Kalman filtering condition, improves
Stability of the invention.
(3) present invention to the result of Kalman filtering carried out itself diverging examine, orbital electron map adjunct test and
With filter result three kinds of measuring means of independent second axle sensor adjunct test, positioning result is ensured by multi-level approach
Reliability.
(4) present invention provides the fault tree of train integrated positioning system, realizes to train operation state and actual effect mode
Monitoring, quantitative analysis can be carried out to system, improves the stability and safety of system, the expression that can quantify due to it
The reliability and error of positioning system, substantially increase the availability of the train system based on satellite positioning.
Detailed description of the invention
Fig. 1 is Kalman filtering safety analysis schematic illustration of the present invention；
Fig. 2 is the schematic diagram of the horizontal and vertical Kalman filtering of the present invention and track；
Fig. 3 is the overall flow figure that in the embodiment of the present invention 1 Kalman filtered results are carried out with safety analysis；
Fig. 4 is the fault tree of the train integrated positioning system based on Kalman filtering in the present invention.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
Embodiment 1
In the present embodiment, the sensor in train integrated positioning system includes GNSS, orbital electron map, mutually independent
First axle sensor and the second axle sensor.
The flow diagram of the safety detection method of Kalman filter such as Fig. 1 institute in the present embodiment train positioning system
Show, first according to current sensor operating status, whether judgement currently meets Kalman filtering condition.Later in conjunction with orbital electron
Map, the first axle sensor data and GNSS output obtain two sides as a result, establish east orientation and north orientation Kalman filtering respectively
To filter result.Then Kalman filtered results are diagnosed, including Kalman filtering itself diverging is examined, orbital electron
Map adjunct test and with the independent second axle sensor adjunct test of filter result.Finally, according to each operating status and mistake
Effect mode, constructs the train security positioning system fault tree based on Kalman filtering, and detailed process is as shown in Figure 3, comprising:
1, sensor fault diagnosis
S101: carrying out fault diagnosis to the first axle sensor and the second axle sensor, obtains axle sensor operation
State Sodo。
S102: fault diagnosis is carried out to GNSS, obtains current GNSS operating status SGNSS。
S103: work as SodoAnd SGNSSWhen showing fault-free, continue subsequent step.Otherwise determine that current time is unsatisfactory for making
With the condition of Kalman filtering.
2, Kalman filter equation is established, realizes filtering
Along the east orientation and north orientation of train positioning, established respectively based on GNSS, the first axle sensor, orbital electron map
Kalman filter equation, specifically includes the following steps:
S201: it according to train current location, is tested the speed in conjunction with orbital electron cartographic information and the first axle sensor as a result, obtaining
To k moment train east orientation speedAnd north orientation speed
S202: according to GNSS measurement result, k moment east orientation and north orientation coordinate are obtainedWith
S203: k moment east orientation and north orientation state space are respectively depicted as 3 dimensional vector Xe(k) and Xn(k), measurement space is retouched
It states as 2 dimensional vector Ze(k) and Zn(k).Wherein Xe(k)=[de(k),ve(k),ae(k)], Xn(k)=[dn(k),vn(k),an
(k)], wherein d, v, a respectively indicate train position, train speed and train acceleration, and subscript e and n respectively indicate east orientation and north
To.The measurement space two-dimensional vector of east orientation and north orientation
S204: being utilized respectively Extended Kalman filter equation X (k)=AX (k-1)+BU+W (k) to east orientation and north orientation,
Z (k)=HX (k)+V (k) obtains the moment position train kWherein A is state-transition matrix, and U is that input is flat
Acceleration after cunning, W are system noise, and H is calculation matrix, and V is measurement noise.
3, Kalman filtering diverging is examined
Kalman filtering diverging is examined, and following steps are specifically included
S301: judge whether east orientation and north orientation Kalman filtering meet inequality criterionWherein VkFor k
The innovation sequence at moment, r >=1 are reserve factor.PkFor the variance matrix of k moment innovation sequence,
S302: setting measurement error limit | | Zmin| |, whether in a certain range to calculate evaluated error, i.e. east orientation and north orientation
Whether filtering meetsWhereinFor the measurement error of estimation.
S303: the threshold value of setting covariance mould | | Pmin| |, judge whether the mould of covariance matrix is respectively less than given threshold,
That is whether east orientation and north orientation filtering is all satisfied | | Pk||≤||Pmin||。
S304: if filter result meets all inequality in S301 to S303, Kalman filtering diverging is upchecked, after
Continuous subsequent step；Otherwise determine Kalman filtering diverging, do not use filter result currently.
4, track map assists Kalman filtering product test
Track map assists Kalman filtering product test, and its principle is as shown in Figure 2, specifically includes the following steps:
S401: the positioning result that will be calculated in step S204It projects on track map, calculates horizontal
To deviation bias.
S402: setting map detection threshold valueWherein σkalmanPoor for Kalman filtering localization criteria, λ is
Threshold coefficient, precision takes 4~4.2 according to the map.WhenWhen upcheck and continue subsequent step.Otherwise determine card
Kalman Filtering error is overproof, does not use filter result currently.
5, the second axle sensor auxiliary filter product test
With the independent second axle sensor auxiliary filter product test of Kalman filtering, specifically includes the following steps:
S501: S204 is utilized, the positioning result of each moment train is calculatedAnd it is projected into track map
On, obtain train positioning mileage dkalman。
S502: it calculates in a series of Δ T time sections in the positioning of the measurement distance of the second axle sensor and Kalman filtering
The difference of journey
S503: corresponding detection threshold value of each Δ T period is setWhen being all satisfiedWhen upcheck, otherwise determine Kalman Filter Residuals it is overproof, currently do not use filter result.
6, secure localization section is established
When aforementioned detection passes through, determine that current Kalman filtered results are available.Kalman filtering positioning projection is arrived
Mileage d on trackkalmanVariances sigmakalmanη times of amplification, and it is subject to adequate compensation β, obtain current safety positioning section
[dkalman-ησkalman-β,dkalman+ησkalman+ β], wherein η takes 6；β is chosen according to initial data feature etc..Desirable 1~
1.5 times of σmap, wherein σmapIndicate the accuracy of map.
7, the fault tree of the train integrated positioning system based on Kalman filtering is established
According to above-mentioned 1~5 judgment step, the fault tree for designing Kalman filtering system is as shown in Figure 4.
8, fault detection
Based on the security interval of train positioning result, judge whether positioning result is safe, if dangerous, from based on karr
In the fault tree of the train integrated positioning system of graceful filtering, train fault reason is obtained, to detect to failure；If peace
Entirely, then fault-free.
Specific embodiment
Below by taking tramcar as an example, it is described in detail in conjunction with Fig. 1-4 pairs of the method for the present invention:
Step 1: first determine whether wheel speed sensors A and B, the state of GNSS receiver whether good and line map whether
Success loads, and 2 is entered step if OK, otherwise cut-in stand-by system；
Step 2: in conjunction with the measurement data of wheel detector A, line map data and GNSS location data use karr
The method of graceful filtering resolves position, obtains positioning result；
Step 3: safety analysis being carried out to positioning result, first determines whether the noise of measurement result is less than certain threshold
Value, if it is less than then entering step 4, does not otherwise use Kalman filtering, positions section with section fusion calculation；
Step 4: further judging whether measurement error is less than certain threshold value, if it is less than then entering step 5, otherwise not
Using Kalman filtering, section is positioned with section fusion calculation；
Step 5: judging whether the threshold value of covariance mould is less than certain threshold value, if it is less than then entering step 6, otherwise not
Using Kalman filtering, section is positioned with section fusion calculation；
Step 6: the result that Kalman filtering is obtained is mapped on line map, calculates whether lateral deviation is less than basis
Otherwise the threshold value of accuracy of map setting does not use Kalman filtering if it is less than then entering step 7, fixed with section fusion calculation
Position section；
Step 7: in conjunction with the measurement data of wheel detector B, line map data and GNSS location data use karr
The method of graceful filtering resolves position, obtains positioning result, and project in line map, enters step 8；
Step 8: judging that the measurement data of wheel detector B and Kalman filtering obtain the deviation of result and whether be less than detection
Otherwise threshold value does not use Kalman filtering if it is less than then entering step 9, position section with section fusion calculation；
Step 9: providing the security interval of positioning result, i.e. dkalmanVariances sigmakalman6 times of amplification, and it is subject to adequate compensation
1, obtain current safety positioning section [dkalman-6σkalman-1,dkalman+6σkalman+1]；
Step 10: can analyze to obtain the fault tree of Kalman filtering system according to step 1~8, train system is carried out
Safety analysis.
The preferred embodiment of the present invention has been described in detail above.It should be appreciated that those skilled in the art without
It needs creative work according to the present invention can conceive and makes many modifications and variations.Therefore, all technologies in the art
Personnel are available by logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Technical solution, all should be within the scope of protection determined by the claims.
Claims (9)
- The sensor 1. safety detection method of Kalman filter in a kind of train positioning system, in the train positioning system Including GNSS, orbital electron map, the first axle sensor and the second axle sensor, which is characterized in that the train combination The fault detection method of positioning system the following steps are included:S1: fault diagnosis is carried out to the first axle sensor, the second axle sensor, GNSS and orbital electron map, all without reason When barrier, S2 is carried out；Otherwise terminate；S2: measurement data, GNSS location data and orbital electron map datum based on the first axle sensor utilize expansion card Graceful filtering equations obtain the positioning result of train, carry out S3；S3: it to S2 train positioning result obtained, carries out Kalman filtering diverging and examines, if it is decided that go out train positioning result It does not dissipate, then carries out S4, otherwise determine Kalman filtering diverging, do not use filter result currently；S4: S2 train positioning result obtained is projected into orbital electron map datum, obtains lateral deviation；Set track electricity Sub- map detection threshold value carries out S5 if lateral deviation is less than orbital electron map detection threshold value, otherwise determines Kalman's filter Wave error is overproof, does not use filter result currently；S5: projecting to orbital electron map datum for S2 train positioning result obtained, obtains positioning mileage according to the time period；If Fixed wheel axle sensor detection threshold value, if in each period, positioning mileage and the measurement of the second axle sensor being small apart from its difference In axle sensor detection threshold value, then S6 is carried out, otherwise determines that Kalman Filter Residuals are overproof, do not use filter result currently；S6:；Determine that current Kalman filtered results are available, obtains current safety using current Kalman filtering and position section；S7: designing the fault tree of the train integrated positioning system based on Kalman filtering, carries out safety point to train system Analysis.
- 2. the safety detection method of Kalman filter, feature in a kind of train positioning system according to claim 1 It is, the detailed process of the step S2 includes:S201: obtain train k moment actual lateral coordinates, longitudinal coordinate, lateral velocity, longitudinal velocity, transverse acceleration and Longitudinal acceleration；S202: the cross detected according to the measurement data of the first axle sensor and orbital electron map datum, acquisition train k moment To speed and longitudinal velocity；According to GNSS location data and orbital electron map datum, obtains the transverse direction that the train k moment is detected and sit Mark and longitudinal coordinate；S203: being based on S201 data obtained, obtains transverse state space, the longitudinal direction state space of train；Based on S202 institute The data of acquisition obtain the cross measure space and longitudinal measurement space of train；S204: transverse state space, longitudinal state space, cross measure space and longitudinal measurement space based on train utilize Extended Kalman filter equation obtains the horizontal and vertical information sequence of train, the i.e. positioning result of train.
- 3. the safety detection method of Kalman filter, feature in a kind of train positioning system according to claim 2 It is, in the step S203, transverse state space Xe(k) expression formula are as follows:Xe(k)=[de(k),ve(k),ae(k)]In formula, deIt (k) is train k moment actual lateral coordinates, veIt (k) is train k moment actual lateral velocity, ae(k) it is Train k moment actual transverse acceleration；The longitudinal direction state space Xn(k) expression formula are as follows:Xn(k)=[dn(k),vn(k),an(k)]In formula, dnIt (k) is train k moment actual longitudinal coordinate, vnIt (k) is train k moment actual longitudinal velocity, an(k) it is Train k moment actual longitudinal acceleration；The cross measure space Ze(k) expression formula are as follows:In formula,For train k moment GNSS measurement lateral coordinates,For the first axle sensor of train k moment The lateral velocity of measurement；Longitudinal measurement space Zn(k) expression formula are as follows:In formula,For train k moment GNSS measurement longitudinal coordinate,For the first axle sensor of train k moment The longitudinal velocity of measurement.
- 4. the safety detection method of Kalman filter, feature in a kind of train positioning system according to claim 2 It is, in the step S204, the expression formula of Extended Kalman filter equation are as follows:X (k)=AX (k-1)+BU+W (k)Z (k)=HX (k)+V (k)In formula, X (k) is the state space at train k moment, and A is state-transition matrix, and U is to input smoothed out acceleration, and B is Acceleration transfer matrix, W are system noise, and Z (k) is the measurement space at train k moment, and H is calculation matrix, and V is measurement noise.
- 5. the safety detection method of Kalman filter, feature in a kind of train positioning system according to claim 1 It is, in the step S3, the detailed process that Kalman filtering diverging is examined includes:S301: using inequality of variance criterion, the positioning result horizontal and vertical to train is tested respectively, if meeting into Row S302；Otherwise, it is determined that for diverging；The expression formula of the inequality of variance criterion are as follows:In formula, VkFor the innovation sequence at k moment, r >=1 is reserve factor, PkFor the variance matrix of k moment innovation sequence,S302: using Error Inequation criterion, the positioning result horizontal and vertical to train is tested respectively, if meeting into Row S303；Otherwise, it is determined that for diverging；The expression formula of the Error Inequation criterion are as follows:In formula,For the measurement error of k moment innovation sequence, | | Zmin| | it is limited for preset measurement error；S303: using covariance modular inequality criterion, the positioning result horizontal and vertical to train is tested respectively, if meeting, Then it is judged to not dissipating；Otherwise, it is determined that for diverging；The expression formula of the covariance modular inequality criterion are as follows:||Pk||≤||Pmin||In formula, | | Pk| | it is the mould of k moment innovation sequence covariance matrix, | | Pmin| | it is the threshold of preset covariance mould Value.
- 6. the safety detection method of Kalman filter, feature in a kind of train positioning system according to claim 1 It is, in the step S4, the expression formula of orbital electron map detection threshold value basis of design are as follows:In formula, σkalmanPoor for Kalman filtering localization criteria, λ is threshold coefficient.
- 7. the safety detection method of Kalman filter, feature in a kind of train positioning system according to claim 5 It is, precision takes 4~4.2 to the λ according to the map.
- 8. the safety detection method of Kalman filter, feature in a kind of train positioning system according to claim 1 It is, in the step S5, the expression formula of axle sensor detection threshold value basis of design are as follows:In formula,For the detection threshold value of the second axle sensor in Δ T time section,For the second wheel shaft in Δ d mileage difference The standard deviation of sensor,It is poor for Kalman filtering localization criteria in Δ d mileage difference；The positioning mileage of each period and the second axle sensor measure the expression formula apart from its difference are as follows:In formula, d is mileage,For the detection mileage of the second axle sensor in Δ T time section,For Δ T time section The positioning mileage of interior Kalman filtering innovation sequence,For in all detections of the second axle sensor before the current T moment Journey,For all detection mileages of the second axle sensor before current T moment Δ T time section,For the current T moment Before all Kalman filterings position mileage,It is fixed for Kalman filtering all before current T moment Δ T time section Position mileage.
- 9. the safety detection method of Kalman filter, feature in a kind of train positioning system according to claim 1 It is, in the step S6, current safety positions section UsafeThe expression formula of calculating are as follows:Usafe=[dkalman-ησkalman-β,dkalman+ησkalman+β]In formula, σkalmanThe variance of mileage is positioned for Kalman filtering innovation sequence, η is by σkalmanThe multiple of amplification, β is according to original Beginning data characteristics etc. is chosen, and 1~1.5 times of σ is takenmap, wherein σmapIndicate orbital electron map datum precision.
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| CN201910469722.4A Active CN110203254B (en) | 2019-05-31 | 2019-05-31 | Safety detection method for Kalman filter in train positioning system |
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