KalmanFilterinRealTimeURBIS卡尔曼滤波器在实时雅邦规划的设计课件

1、Kalman Filter in Real Time URBISRichard Kranenburg06-01-2019第1页，共28页。IntroductionTNO Technisch Natuurwetenschappelijk OnderzoekKerngebied Bouw en OndergrondBusiness Unit Milieu en LeefomgevingIntroduction - Uncertainty analysis - Kalman filter - Application on population第2页，共28页。IntroductionAccompan

2、istsMichiel Roemer (TNO)Jan Duyzer (TNO)Arjo Segers (TNO)Kees Vuik (TUDelft)Introduction - Uncertainty analysis - Kalman filter - Application on population第3页，共28页。ProblemCurrent situationReal Time URBIS model gives one value for the concentration NOx in the DCMR areaWanted situationUncertainty inte

3、rval for the concentration NOxUncertainty interval dependent of the place in the domainIntroduction - Uncertainty analysis - Kalman filter - Application on population第4页，共28页。DCMR-areaIntroduction - Uncertainty analysis - Kalman filter - Application on population第5页，共28页。URBIS model11 emission sourc

4、esTrafficCARZone CardsRoad nearRoad farBackgroundAbroadRest of the NetherlandsDCMR-areaShippingShip seaShip inlandIndustryIndustryRestWinddirectionsNorthEastSouthWestWind speeds1.5 m/s5.5 m/sTotal 88 standard concentration fields for the concentration NOxIntroduction - Uncertainty analysis - Kalman

5、filter - Application on population第6页，共28页。Real Time URBISGives for every hour an expected concentration NOx for the whole DCMR-area, based on input parametersWind direction ()Wind speed (v)Temperature (T)Month (m)Weekday (d)Hour (h)State equation:Introduction - Uncertainty analysis - Kalman filter

6、- Application on population第7页，共28页。Measurement locationsDCMR-StationsSchiedamHoogvlietMaassluisOverschieRidderkerkRotterdam NoordRIVM-StationsSchiedamsevestVlaardingenBentinckpleinIntroduction - Uncertainty analysis - Kalman filter - Application on population第8页，共28页。Uncertainty Real Time URBISComp

7、are Real Time URBIS simulations with the observations on the nine measurement locationsBoth observations and model simulations have a log-normal distributionIntroduction - Uncertainty analysis - Kalman filter - Application on population第9页，共28页。Log-normal distributionsIntroduction - Uncertainty anal

8、ysis - Kalman filter - Application on population第10页，共28页。Correction of the modelDifferences between model and measurements plotted with respect to 6 input parameters h: hour of the day: wind directionIntroduction - Uncertainty analysis - Kalman filter - Application on population第11页，共28页。Uncertaint

9、y of the modelStandard deviation of the differences between the corrected model and the observations v: wind speedIntroduction - Uncertainty analysis - Kalman filter - Application on population第12页，共28页。Result of uncertainty analysisIntroduction - Uncertainty analysis - Kalman filter - Application o

10、n population第13页，共28页。Kalman filterSmooth random errors in the model of a dynamical systemIn a real time application, measurements on time k are directly available to filter the state on time k. Two results after applicationNew expected concentration NOxUncertainty interval for the concentration NOx

11、 New state equation:Introduction - Uncertainty analysis - Kalman filter - Application on population第14页，共28页。Kalman filter equationsForecast Analysis Introduction - Uncertainty analysis - Kalman filter - Application on population第15页，共28页。Kalman filter on emission source BackgroundIn the vector all

12、values are equal to zero, except the entries corresponding with the source background Linearization of the Kalman filter equationsMatrix A estimated with measurements in Schipluiden and WestmaasMatrix R estimated with measurements at BentinckpleinIntroduction - Uncertainty analysis - Kalman filter -

13、 Application on population第16页，共28页。Kalman filter on emission source BackgroundScreening criterion:Pfabs,k : Model uncertainty after forecast stepRabs,k : Uncertainty of the measurementsIntroduction - Uncertainty analysis - Kalman filter - Application on population第17页，共28页。Kalman filter on emission

14、 source Background Introduction - Uncertainty analysis - Kalman filter - Application on population第18页，共28页。Kalman filter on all emission sourcesState equation:Screening CriterionIntroduction - Uncertainty analysis - Kalman filter - Application on population第19页，共28页。Kalman filter on all emission so

15、urcesMatrix A estimated with measurements in Schipluiden, Westmaas, Overschie, Ridderkerk, Maassluis, VlaardingenMatrix R estimated with measurements at BentinckpleinIntroduction - Uncertainty analysis - Kalman filter - Application on population第20页，共28页。Kalman filter on all emission sourcesIntroduc

16、tion - Uncertainty analysis - Kalman filter - Application on population第21页，共28页。Connection with populationEach grid cell, every hour a certainty intervalAnnual mean of the width of these intervals per grid cellAmount of large widths of these intervals per grid cellNumber of postal zipcodes per grid

17、 cellPopulation density 1.99 people per grid cellNumber of people per grid cellIntroduction - Uncertainty analysis - Kalman filter - Application on population第22页，共28页。Connection with populationIntroduction - Uncertainty analysis - Kalman filter - Application on population第23页，共28页。Connection with p

18、opulationIntroduction - Uncertainty analysis - Kalman filter - Application on population第24页，共28页。Connection with populationIntroduction - Uncertainty analysis - Kalman filter - Application on population第25页，共28页。Connection with populationIntroduction - Uncertainty analysis - Kalman filter - Application on population第26页，共28页。ConclusionsThe differences between the observations and the model simulation are not only caused by inaccuracies in the backgroundUncertainty interval has large width in the industrial region around Pernis and on the main roadsThe application of the Kal