Atmospheric Chemistry and Physics, vol. 12, issue 24 (2012) pp. 11781-11793
Extended Kalman Filter (EKF) is used to esti-mate particle size distributions from observations. The fo-cus here is on the practical application of EKF to simulta-neously merge information from different types of experi-mental instruments. Every 10 min, the prior state estimate is updated with size-segregating measurements from Differen-tial Mobility Particle Sizer (DMPS) and Aerodynamic Parti-cle Sizer (APS) as well as integrating measurements from a nephelometer. Error covariances are approximate in our EKF implementation. The observation operator assumes a constant particle density and refractive index. The state es-timates are compared to particle size distributions that are a composite of DMPS and APS measurements. The impact of each instrument on the size distribution estimate is studied. Kalman Filtering of DMPS and APS yielded a temporally consistent state estimate. This state estimate is continuous over the overlapping size range of DMPS and APS. Inclusion of the integrating measurements further reduces the effect of measurement noise. Even with the present approximations, EKF is shown to be a very promising method to estimate par-ticle size distribution with observations from different types of instruments.
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