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GLM Observations from the Hunga Tonga Eruption on 15 January 2022 and thoughts on lightning data fusion
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Data Fusion of In-Flight Aerothermodynamic Heating Measurements Using Kalman Filtering
On February 18th, 2021, the Mars 2020 entry system successfully delivered the Perseverance rover to the surface of Mars at Jezero Crater. The entry capsule carried instrumentation installed on the heatshield and backshell, named the Mars Entry, Descent, and Landing Instrumentation 2. The instruments included pressure transducers, thermocouples, heat flux gauges, and a radiometer to measure the aerodynamic and aerothermodynamic performance of the entry vehicle. Three of these sensors, a thermocouple plug, heat flux gauge, and a radiometer, were co-located on the backshell. The sensors were exposed to roughly the same aerodynamic heating, but measured these environments in different ways, each with its own set of modeling and measurement error complications. This paper develops a method for blending each of these measurements together in a single algorithm to produce estimates of the aerothermodynamic environments at that backshell location. The approach makes use of the Kalman-Schmidt filter/smoother methodology, where systematic measurement error parameters are modeled as multiplicative states that are estimated by the filter along with the aerothermal states. The results indicate peak convective and radiative heating values of 0.86 and 5.16 W/cm2, respectively, compared to the filter predictive model values of 0.67 and 4.83 W/cm2.
Data Fusion of In-Flight Aerothermodynamic Heating Measurements Using Kalman Filtering
On February 18th, 2021, the Mars 2020 entry system successfully delivered the Perseverance rover to the surface of Mars at Jezero Crater. The entry capsule carried instrumentation installed on the heatshield and backshell, named the Mars Entry, Descent, and Landing Instrumentation 2. The instruments included pressure transducers, thermocouples, heat flux gauges, and a radiometer to measure the aerodynamic and aerothermodynamic performance of the entry vehicle. Three of these sensors, a thermocouple plug, heat flux gauge, and a radiometer, were co-located on the backshell. The sensors were exposed to roughly the same aerodynamic heating, but measured these environments in different ways, each with its own set of modeling and measurement error complications. This paper develops a method for blending each of these measurements together in a single algorithm to produce estimates of the aerothermodynamic environments at that backshell location. The approach makes use of the Kalman-Schmidt filter/smoother methodology, where systematic measurement error parameters are modeled as multiplicative states that are estimated by the filter along with the aerothermal states. The results indicate peak convective and radiative heating values of 0.86 and 5.16 W/cm$^2$, respectively, compared to the filter predictive model values of 0.67 and 4.83 W/cm$^2$.
Data-Fusion of In-Flight Aerothermodynamic Heating Measurements - Kalman Filtering
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Supporting Local Government Public Health and Air Quality Decision-Making with A Sub-City Scale Air Quality Forecasting System from Data Fusion of Models, Satellite, In-Situ Measurements, and Low-Cost Sensors
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Data Fusion for Urban Air Quality Assessment & Forecasting
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Air Sensors as Input to an Air Quality Data Fusion System
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AeroFusion: Data Fusion and Uncertainty Quantification for Entry Vehicles
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Air Quality Data Fusion with Sensors, Satellites, and Models
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Supporting Local Government Public Health and Air Quality Decision-Making With A Sub-City Scale Air Quality Forecasting System From Data Fusion of Models, Satellite, in-Situ Measurements, and Low-Cost Sensors
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Data Fusion for Sub-City Scale Air Quality Forecasting: Preliminary Results for Rio de Janeiro
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Air Quality, NASA Open Data, Google Earth Engine Data Fusion
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Monitoring the 2021 Cumbre Vieja Volcanic Eruption Using Satellite Multisensor Data Fusion
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Improving global land cover characterization through data fusion
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