CN106644951B - A kind of telemetering motor vehicle tail equipment calibration method - Google Patents
A kind of telemetering motor vehicle tail equipment calibration method Download PDFInfo
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Abstract
The telemetering motor vehicle tail equipment calibration method based on telemetry that the present invention relates to a kind of, provides the state-space model of tail gas telemetering process first, then hinders poor kalman filter method using self-regulation and is estimated.Using Noise statistics extimators and gross error determining device, which is capable of handling noise statistics time-varying and there is the case where measurement gross error, to be corrected to tail gas remote-measuring equipment, provides relatively accurate tail gas measurement telemetry.
Description
Technical field
The present invention relates to automotive emission relevant issues in environmental monitoring field, and in particular to a kind of motor-vehicle tail-gas
Remote-measuring equipment bearing calibration.
Background technique
Along with the paces of Developing Urbanization, Chinese vehicle guaranteeding organic quantity is in cumulative year after year.According to Traffic Administration Bureau, the Ministry of Public Security
Statistics, in by the end of June, 2016 by, national vehicle guaranteeding organic quantity is up to 2.85 hundred million, wherein 1.84 hundred million, automobile.Institute in vehicle exhaust
The substance for including mainly has carbon monoxide, nitrogen oxides, hydrocarbon and fine particulates etc., these pollutants are strong to human body
Health has potential permanent harm, causes directly to pollute and destroy to ecological environment.Wherein, nitrogen oxides runs up to certain journey
Degree, can lead to the generation of acid rain, stronger to the building destruction in city, and the ozone in atmosphere also can be by different degrees of destruction.
And hydrocarbons can skin to people, eyes generate strong impulse.Show to be in automobile exhaust pollution ring for a long time according to the study
It is also easy to increase the disease incidence of cardiovascular and cerebrovascular disease in border.In addition, the fine particulates in tail gas are easy to cause haze weather
It generates, can also induce all kinds of traffic accident the problems such as larger to the harm of the respiratory system of people.Due to China's car ownership radix
It is larger, automobile exhaust pollution problem very severe, and government formulates the emission data that corresponding policy needs magnanimity, with
Auxiliary administers policy making.
From the 1970s, successively there are a variety of motor-vehicle tail-gas detection methods.The cost of chassis dynamometer detection method
Put into it is lower, use it is more convenient, automobile maintenance industry, tail gas monitoring station etc. apply very extensive, this kind of detection side
Method must carry out under the situation of vehicle fixed form, and not can truly reflect the real-time emission behaviour of tail gas on road,
It is not able to satisfy the needs of current motor-vehicle tail-gas monitoring；Vehicle mounted tail gas detection method can obtain different automobile types in a relatively short period of time
Exhaust emissions situation in different simulation sections, the precision of calculation of measured data is higher, true, reliable, and can save a large amount of measuring and calculating
Time；And remote sensing monitoring equipment involved in this patent can carry out detection work during usual road surface normally travel in motor vehicle
To make, does not need to contact with tested vehicle, automatization level is high, and detection speed is fast, and it is high-efficient, it will not be to detection vehicle and driver
Member has an impact, and obtained measurement result is true and reliable, has saved the plenty of time and has avoided cheating fraud problem.
It since tail gas remote-measuring equipment will be placed on road, is directly exposed under natural environment, temperature, illumination, is raised wind speed
The precision that the factors such as dirt, wagon flow can all monitor tail gas generates different degrees of influence.It is found by many experiments, telemetry
It is influenced by following two noise: one, since electronic device is there are the noise of some intrinsic rapid jumpings, so detection
As a result it is considered that receiving the influence of additive white noise, and with the variation of the natural causes such as illumination, temperature, it will cause
The variation of electronic device characteristics, and then the statistical property of noise is influenced, in practice we have found that the variance of white noise is slow time-varying
's；Secondly, blocking the way road vehicle it is more when, road may be made to resonate, so change tail gas remote-measuring equipment in laser hair
Optical path between emitter and laser pickoff, so that detected value is very inaccurate, in addition, a large amount of fugitive dust also results in detected value
Failure, in this way one can consider that there are gross errors the moment.Due to when tail gas remote-measuring equipment is when real road works
It will receive the influence of various interference, so certainly will need to be corrected tail gas monitoring device.
However, the frequency spectrum that a large amount of existing random signals do not determine in tail gas remote-measuring equipment, conventional filtering can not extract
Or inhibit signal, it, can be according to the signal and interference noise to be extracted since random signal has the characteristic of determining power spectrum
Power spectrum designs filter.Nineteen sixty-eight Kalman and Bu Xi for the first time introduce the state space thought in modern control theory optimal
Filtering theory proposes optimal Recursive Filtering method, i.e. Kalman filter.Kalman filtering is a kind of method of time domain recursion,
It can be used to handle random signal, it does not repartition noise and useful signal, but estimates all processed objects, referred to as
Optimal estimation is theoretical.There are some problems for the correction for directly carrying out detection data using classical Kalman filter.Firstly, right
The case where there are gross errors in detected value, classical Kalman filter can not be isolated, but as normal detection
Value is utilized, this will introduce very big error, and is difficult to eliminate in a short time.Secondly, for noise statistics time-varying
The case where, classical Kalman filtering also can not automatically adjust its weight coefficient.These can all reduce the correction effect of remote-measuring equipment
Fruit.
From the point of view of existing document, since telemetering motor vehicle tail equipment is a kind of emerging enviromental monitoring equipment, at present
There are no occur being directed to the bearing calibration of the equipment.From the perspective of methodology, there are some improved karrs really at present
Graceful filtering algorithm, but all not applicable this project background.Patent of invention " steady filtering method based on Robust filter " (application
Number: 201010136448.8) for the integrated navigation system of motion carrier propose a kind of improved Kalman filter device；Invention is special
Sharp " a kind of method for estimating attitude angles of rescuing and obstacle-clearing vehicle based on robust filtering " (application number: 201510941293.8) discloses one
Kind method for estimating attitude angles of rescuing and obstacle-clearing vehicle, in the case where the statistical property for the interference signal that can not know for sure, estimation rescue
Pitch angle and angle of heel of the obstacles removing car under great slope rate operating condition；" the empty day based on launch inertial coordinate system flies patent of invention
Row device integrated navigation robust filtering method " (application number: 201410419869.X)；Technical paper is " based on adaptive H ∞ filtering
Combinated navigation method research " (Liu Xiaoguang, Hu Jingtao, Wang He Chinese journal of scientific instrument, 2014,35 (5): 1013-1021) for normal
Rule robust filtering parameter makes filtering have the problem of larger conservative by initially setting, it is proposed that one kind is based on ADAPTIVE ROBUST
The multi-sensor combined navigation method of filtering.Above-mentioned documents give some improved Kalman filters, wherein relating to
And to " robust ", " robust ", the nouns such as " steady " are inherently the translator of Chinese of English word " robust ", and contain this
The improved Kalman filter of a little nouns is not a kind of optimal filter truly, the basic principle is that by increasing by force
Or reduce kalman gain value stablize the error of filter, particularly, in order to make the error of Kalman filtering in the overall situation
On do not dissipate it is necessary to sacrifice global precision, even if not interfered by gross error, the precision of the estimated value at the moment also can be by
It slackens significantly.
In order to realize the correction to tail gas remote-measuring equipment, the concept of " resistance is poor " is proposed in the present invention, utilizes gross error
Gross error is identified and is isolated by determining device, to prevent due to introducing gross error and bring significant errors.Further
Introduce Noise statistics extimators to realize the self-regulation of filtering parameter, finally provide relatively accurate tail gas telemetering knot
Fruit.
Summary of the invention
The technology of the present invention solves the problems, such as: overcoming the deficiencies of the prior art and provide a kind of motor-driven vehicle based on telemetry
Gas remote-measuring equipment bearing calibration is capable of handling noise statistics time-varying and there is the case where measurement gross error, realizes thick
The big identification of error and the self-regulation of filtering parameter, finally provide relatively accurate tail gas measurement telemetry.
The technology of the present invention solution: providing the state-space model of tail gas telemetering process first, then according to use from
The poor kalman filter method of resistance is adjusted to be estimated.Utilize Noise statistics extimators and gross error determining device, the filtering algorithm
It is capable of handling noise statistics time-varying and there is the case where measurement gross error, realize the identification and filtering of gross error
The self-regulation of parameter finally provides relatively accurate tail gas measurement telemetry.
It is implemented as follows:
Step 1: by telemetering motor vehicle tail equipment (mobile, horizontal and rectilinear telemetering motor vehicle tail equipment) cloth
It sets on road, data of the continuous acquisition by pollutant concentration in the tail gas of vehicle；
Step 2: establish the state-space model of telemetering motor vehicle tail process:
System state equation: Xt+1=Xt+Wt
Measurement equation when not breaking down: yt+1=Xt+1+Vt+1
Measurement equation when breaking down: yt+1=Xt+1+Vt+1+gt+1
Wherein: Xt+1For the actual concentration value of pollutant component in tail gas (such as CO, NO, HC), yt+1For pollutant in tail gas
The measured value of ingredient is obtained with tail gas remote-measuring equipment.WtAnd VtIt is the white Gaussian noise that mean value is zero, variance is respectively QWAnd QV,
Wt、VtTwo pairwise uncorrelateds and variance variation it is very slow, i.e., it is believed that the variance yields of t moment is believed that the value phase with the t+1 moment
Deng.gtFailure amplitude when to break down, such failure known to actual conditions infrequently occurs, and is far longer than QV。
Step 3: the correction of tail gas telemetry is carried out using improved Kalman filtering.
Primary condition:
Wherein: E indicates mathematic expectaion, X0The actual concentration value of exhaust pollutant ingredient when indicating initial,It is X0It is optimal
Estimation, P0It is initial estimation error.
Specific step is as follows for algorithm:
1) status predication:
Wherein,It is the predictive estimation at t+1 moment,It is the optimal estimation of t moment.
2) variance of predictive estimation is calculated:
Wherein, Pt,tIt is the optimal estimation variance of t moment, Pt+1,tIt is the predictive estimation variance at t+1 moment,It is QWEstimate
Value.
3) gross error judgement is carried out up to criterion based on Rye, it may be assumed that
H0:I.e.
H1: δtDisobey normal distributionDue to gtIt is far longer than QV, so
Wherein H0Condition when not break down, H1Condition when to break down, according to the two conditions by measuring value
It is divided into normal value and fault value two major classes.δ in assuming thattIt indicates new breath, is the difference of reality output and prediction output, i.e.,
4) if there is no gross error, then:
4.1) kalman gain matrix is calculated:
Wherein, KtIt is the kalman gain of t moment,It is QVValuation.
4.2) optimal estimation:
Wherein,It is the optimal estimation at t+1 moment.
4.3) optimal estimation variance: P is calculatedt+1,t+1=[1-Kt]Pt+1,t
Wherein, Pt+1,t+1It is optimal estimation variance.
5) if there is coarse variance
5.1) optimal estimation:
5.2) optimal estimation variance: P is calculatedt+1,t+1=Pt+1,t
6) Noise Variance Estimation is carried out based on detected value correlation, it may be assumed that
Construct observed difference: Zt+1=yt+1-yt,
Construct statistic a and b:
Wherein n is statistical data number.
Measure noise estimation:
System noise estimation:
The advantages of the present invention over the prior art are that:
(1) the vehicle exhaust emission number that telemetering motor vehicle tail equipment can obtain during motor vehicle normally travel
According to, motor vehicle and driver will not be generated and be significantly affected, effectively prevent a possibility that automobile vendor fakes, but due to certainly
Interference is too many in right environment, so having to be corrected detection data, there is presently no the schools for being directed to tail gas remote-measuring equipment
Correction method, the present invention are able to ascend the precision and reliability of tail gas remote-measuring equipment.
(2) the invention proposes self-regulations to hinder poor kalman filter method, traditional the case where for containing gross error
Robust/robust/robust filtering algorithm is not a kind of optimal filter truly, the basic principle is that by increasing by force or
Reduce kalman gain value stablize the error of filter, particularly, in order to make the error of Kalman filtering in the overall situation
It does not dissipate it is necessary to sacrifice global precision, even if not interfered by gross error, the precision of the estimated value at the moment also can be big
It slackens greatly.The present invention by identification to gross error be isolated, the influence of gross error can be preferably minimized.It simultaneously can be with
In the case where noise statistics are slowly varying, be self-regulated filter factor, effectively improves filtering accuracy, and then improve
The validity of bearing calibration of the present invention.
Detailed description of the invention
Fig. 1 is implementation flow chart of the present invention；
Fig. 2 is the improved kalman filter method implementation flow chart of the present invention；
Fig. 3 is general Kalman filtering effect；
Fig. 4 is improved Kalman filtering effect in the present invention.
Specific embodiment
The following describes the present invention in detail with reference to the accompanying drawings and embodiments.
As shown in Figure 1, the method is specifically implemented by the following steps:
Step 1: by telemetering motor vehicle tail equipment (mobile, horizontal and rectilinear telemetering motor vehicle tail equipment) cloth
It sets on road, data of the continuous acquisition by pollutant concentration in the tail gas of vehicle；
Step 2: establish the state-space model of telemetering motor vehicle tail process:
System state equation: Xt+1=Xt+Wt
Measurement equation when not breaking down: yt+1=Xt+1+Vt+1
Measurement equation when breaking down: yt+1=Xt+1+Vt+1+gt+1
Wherein: Xt+1For the actual concentration value of pollutant component in tail gas (such as CO, NO, HC), yt+1For pollutant in tail gas
The measured value of ingredient is obtained with tail gas remote-measuring equipment.WtAnd VtIt is the white Gaussian noise that mean value is zero, variance is respectively QWAnd QV,
Wt、VtTwo pairwise uncorrelateds and variance variation it is very slow, i.e., it is believed that the variance yields of t moment is believed that the value phase with the t+1 moment
Deng.gtFailure amplitude when to break down, such failure known to actual conditions infrequently occurs, and is far longer than QV。
Step 3: the correction of tail gas telemetry is carried out using improved Kalman filtering, as shown in Fig. 2, specific as follows.
Primary condition:
Wherein: E indicates mathematic expectaion, X0The actual concentration value of exhaust pollutant ingredient when indicating initial,It is X0Most
Excellent estimation, P0It is initial estimation error.
Specific step is as follows for algorithm:
1) status predication:
Wherein,It is the predictive estimation at t+1 moment,It is the optimal estimation of t moment.
2) variance of predictive estimation is calculated:
Wherein, Pt,tIt is the optimal estimation variance of t moment, Pt+1,tIt is the predictive estimation variance at t+1 moment,It is QWEstimate
Value.
3) gross error judges, it may be assumed that
H0:I.e.
H1: δtDisobey normal distributionDue to gtIt is far longer than gV, so
Wherein H0Condition when not break down, H1Condition when to break down, according to the two conditions by measuring value
It is divided into normal value and fault value two major classes.δ in assuming thattIt indicates new breath, is the difference of reality output and prediction output, i.e.,
4) if there is no gross error, then:
4.1) kalman gain matrix is calculated:
Wherein, KtIt is the kalman gain of t moment,It is QVValuation.
4.2) optimal estimation:
Wherein,It is the optimal estimation at t+1 moment.
4.3) optimal estimation variance: P is calculatedt+1,t+1=[1-Kt]Pt+1,t
Wherein, Pt+1,t+1It is optimal estimation variance.
5) if there is coarse variance
5.1) optimal estimation:
5.2) optimal estimation variance: P is calculatedt+1,t+1=Pt+1,t
6) Noise Variance Estimation is carried out based on detected value correlation, it may be assumed that
Construct observed difference: Zt+1=yt+1-yt,
Construct statistic a and b:
Wherein n is statistical data number.
Measure noise estimation:
System noise estimation:
In short, self-regulation proposed by the present invention hinders poor kalman filter method, it is different from the past after obtaining metric data
Filtering method, the present invention carries out classification judgement to data first, rather than is directly filtered to it, effectively avoids coarse
Influence of the error to system accuracy.Secondly, estimating observation noise and system noise in real time, noise statistics can be effectively solved
The problem of time-varying.In Fig. 3, dotted line is true value, and solid line is the result based on classical Kalman filtering, it is seen that is sampled at the 40th
Point, due to the presence of gross error, filter result is not satisfactory, large error occurs；Dotted line is true value in Fig. 4, and solid line is
The result of poor Kalman filtering is hindered based on self-regulation, it is seen that since gross error is removed, do not go out in the 40th sampled point
Existing large error.The present invention by identification to gross error be isolated, the influence of gross error can be preferably minimized.Simultaneously
Can be in the case where noise statistics be slowly varying, be self-regulated filter factor, effectively improves filtering accuracy, Jin Erti
The validity of bearing calibration of the present invention is risen.
Above embodiments are provided just for the sake of the description purpose of the present invention, and are not intended to limit the scope of the invention.This
The range of invention is defined by the following claims.It does not depart from spirit and principles of the present invention and the various equivalent replacements made and repairs
Change, should all cover within the scope of the present invention.
Claims (1)
1. a kind of telemetering motor vehicle tail equipment calibration method, it is characterised in that steps are as follows:
Step 1: telemetering motor vehicle tail equipment being arranged on road, continuous acquisition is by pollutant concentration in the tail gas of vehicle
Data；
Step 2: establishing the state-space model of telemetering motor vehicle tail process；
Step 3: hindering the correction that poor kalman filter method carries out tail gas telemetry using self-regulation；
The tail gas remote-measuring equipment includes mobile, horizontal and rectilinear telemetering motor vehicle tail equipment；
In the step 2, the state-space model of telemetering motor vehicle tail process is as follows:
System state equation: Xt+1=Xt+Wt
Measurement equation when not breaking down: yt+1=Xt+1+Vt+1
Measurement equation when breaking down: yt+1=Xt+1+Vt+1+gt+1
Wherein: Xt+1For the actual concentration value of pollutant component in a certain tail gas, yt+1For the inspection of pollutant component in tail gas
Measured value is obtained, W with tail gas remote-measuring equipmenttAnd VtIt is the white Gaussian noise that mean value is zero, variance is respectively QWAnd QV, Wt、VtTwo-by-two
It is uncorrelated, that is, think that the variance yields of t moment is equal with the value at t+1 moment, gtFailure amplitude when to break down, amplitude are remote
Much larger than QV；
In the step 3, it is as follows that self-regulation hinders poor kalman filter method:
Primary condition:
Wherein: E indicates mathematic expectaion, X0The actual concentration value of exhaust pollutant ingredient when indicating initial,It is X0Optimal estimate
Meter, P0It is initial estimation error；
Specific step is as follows:
1) status predication:
Wherein,It is the predictive estimation at t+1 moment,It is the optimal estimation of t moment；
2) variance of predictive estimation is calculated:
Wherein, PT, tIt is the optimal estimation variance of t moment, PT+1, tIt is the predictive estimation variance at t+1 moment,It is QWValuation；
3) gross error judgement is carried out up to criterion based on Rye；
4) if there is no gross error, then:
4.1) kalman gain matrix is calculated:
Wherein, KtIt is the kalman gain of t moment,It is QVValuation；
4.2) optimal estimation:
Wherein,It is the optimal estimation at t+1 moment；
4.3) optimal estimation variance: P is calculatedT+1, t+1=[1-Kt]PT+1, t
Wherein, PT+1, t+1It is optimal estimation variance；
5) if there is coarse variance
5.1) optimal estimation:
5.2) optimal estimation variance: P is calculatedT+1, t+1=PT+1, t
6) Noise Variance Estimation is carried out based on detected value correlation；
Gross error judgement in the step 3) is as follows:
I.e.
H1: δtDisobey normal distributionDue to gtIt is far longer than QV, so | δt| >
Wherein H0When not break down, that is, be not present gross error the case where, H1When to break down, that is, there is gross error
The case where, measuring value is divided into normal value and fault value two major classes, δ according to the two conditionstIndicate new breath, be reality output with
Predict the difference of output, i.e.,
It is as follows that the step 6) is based on detected value correlation progress Noise Variance Estimation:
Construct observed difference: Zt+1=yt+1-yt,
Construct statistic a and b:
Wherein n is statistical data number,
Measure noise estimation:
System noise estimation:
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| CN108197731B (en) * | 2017-12-26 | 2022-01-11 | 中国科学技术大学 | Motor vehicle exhaust remote measurement and vehicle inspection result consistency method based on co-training |
| CN110243762A (en) * | 2019-06-18 | 2019-09-17 | 深圳大雷汽车检测股份有限公司 | Telemetering motor vehicle tail and supervisory systems and self study high emitter decision algorithm |
| TWI765430B (en) * | 2020-11-24 | 2022-05-21 | 思維環境科技有限公司 | Inspection mechanism optimization method and system for environmental sensing device |
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| CN101059428A (en) * | 2007-05-23 | 2007-10-24 | 浙江大学 | Gas density quantitative analysis instrument based on corrected kalman filtering theory |
| CN101726466A (en) * | 2009-12-01 | 2010-06-09 | 康宇 | Multilane motor vehicle exhaust remote measuring device |
| CN102128802A (en) * | 2010-11-30 | 2011-07-20 | 中国科学技术大学 | Multi-lane motor vehicle tail gas detection system |
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