The mobile robot localization problem is decomposed and treated as a two-stage iterative process. The attitude is estimated rst and is then used for position estimation. The innovation of our method presented in the sequel is the incorporation of a smoother, in the attitude estimation loop that outperforms in estimate accuracy any other Kalman ter based technique. The smoother is ideally suited...

The ensemble Kalman smoother (EnKS) is used as a linear least-squares solver in the Gauss–Newton method for the large nonlinear least-squares system in incremental 4DVAR. The ensemble approach is naturally parallel over the ensemble members and no tangent or adjoint operators are needed. Furthermore, adding a regularization term results in replacing the Gauss–Newton method, which may diverge, b...

This paper addresses the joint path delay and time-varying complex gain estimation for continuous phase modulation (CPM), over a time-selective slowly varying Rayleigh flat fading channel. We propose two estimation methods: an expectation-maximization (EM) algorithm for path delay estimation in a Kalman framework, and a Maximum a Posteriori (MAP) method for joint path delay and complex gain est...

A time-varying parametric spectrum estimation method for analysing non-stationary heart rate variability signals is presented. As a case study, the dynamics of heart rate variability during an orthostatic test is examined. In this method, the non-stationary signal is first modelled with a time-varying autoregressive model and the model parameters are estimated recursively with a Kalman smoother...

In problems of enhancing a desired signal in the presence of noise, multiple sensor measurements will typically have components from both the signal and the noise sources. When the systems that couple the signal and the noise to the sensors are unknown, the problem becomes one of joint signal estimation and system identification. In this paper, we specifically consider the two-sensor signal enh...

The ensemble Kalman smoother (EnKS) is introduced to the data assimilation system, ASTI, based on integrated transport simulation code, TASK3D. We use EnKS estimate state variables composed of electron and ion temperature, density, numerical factors turbulent models neutral beam injection (NBI) heat deposition. time series plasma temperature density profiles are assimilated into estimation perf...

Abstract Volcanic lakes often capture a significant amount of volcanic heat emission and thus provide unique opportunity to monitor changes inside the volcano. We present Bayesian inversion method automatically infer in over time at base lake from monitoring data using non-linear Kalman Smoother. Our accounts for the, sometimes large, uncertainties observations underlying physics-based model ge...