Since the landmark work of RE Kalman in the 1960s, considerable efforts have been
devoted to time series state space models for a large variety of dynamic estimation …

A Gaussian mixture filter is proposed for the state estimation of dynamical systems with
nonlinear measurements. The filter is derived by solving an assumed density filtering …

We provide high-probability sample complexity guarantees for exact structure recovery of
tree-structured graphical models, when only noisy observations of the respective vertex …

We provide high-probability sample complexity guarantees for exact structure recovery and
accurate predictive learning using noise-corrupted samples from an acyclic (tree-shaped) …

Since the landmark work of the Kalman filter in the 1960s, considerable effort has been
devoted to a variety of novel filters for nonlinear estimation. Among them, the class of …

This paper presents an approach for state tracking in scenarios where motion is highly
uncertain. The proposed approach improves on traditional Kalman filters by integrating …

A filtering method called Grid Filtration Filter (GFF) is proposed based on Bayesian
inference. First, we select the high-probability region of the current state according to the …

We propose a novel algorithm, state propagation based dynamic compressed sensing (SP-
DCS), that uses a target dynamic model in dynamic compressed sensing (DCS) to track a …

The confidence partitioning sampling filter (CPSF) method proposed in this paper is a novel
approach for solving the generic nonlinear filtering problem. First, the confidence probability …

This paper conveys attitude and rate estimation without rate sensors by performing a critical
comparison, validated by extensive simulations. The two dominant approaches to facilitate …