The intercomparison between different atmospheric monitoring systems is key for instrument calibration and validation. Common cases involve satellites, radiosonde model outputs. Since instruments and/or measures are not perfectly collocated, miss-collocation uncertainty must be considered in related budgets. This paper motivated by the comparison of GNSS-RO, Global Navigation Satellite System R...

Dual estimation refers to the problem of simultaneously estimating the state of a dynamic system and the model which gives rise to the dynamics. Algorithms include expectation-maximization (EM), dual Kalman filtering, and joint Kalman methods. These methods have recently been explored in the context of nonlinear modeling, where a neural network is used as the functional form of the unknown mode...

Noncausal estimation algorithms, which involve smoothing, can be used for off-line identification of nonstationary systems. Since smoothing is based on both past and future data, it offers increased accuracy compared to causal (tracking) estimation schemes, incorporating past data only. It is shown that efficient smoothing variants of the popular exponentially weighted least squares and Kalman ...

We propose a new method for reconstruction of images in single photon emission computed tomography (SPECT), when the activity distribution of the object is time-varying. The activity evolution is modeled with the first-order Markov model, and linear observation model is used to characterize the measurement system. The state-space representation of the measurement sequence reduces to an ill-cond...

Numerical weather prediction systems contain model errors related to missing and simplified physical processes, limited resolution. While it has been widely recognized that these need be included in the data assimilation formulation, providing prior estimates of their spatio-temporal characteristics is a hard problem. We follow systematic path estimate parameters error specifically time-correla...

We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This implies that novel filters and smoothers can be derived straightforwardly by providing methods for computing...