Comparison of Expectation-Maximization based parameter estimation using Particle Filter, Unscented and Extended Kalman Filtering techniques

We show that a hierarchical Bayesian modeling approach allows us to perform regularization in sequential learning. We identify three inference levels within this hierarchy: model selection, parameter estimation, and noise estimation. In environments where data arrive sequentially, techniques such as cross validation to achieve regularization or model selection are not possible. The Bayesian approach, with extended Kalman filtering at the parameter estimation level, allows for regularization within a minimum variance framework. A multilayer perceptron is used to generate the extended Kalman filter nonlinear measurements mapping. We describe several algorithms at the noise estimation level that allow us to implement on-line regularization. We also show the theoretical links between adaptive noise estimation in extended Kalman filtering, multiple adaptive learning rates, and multiple smoothing regularization coefficients.

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Complex step-based low-rank extended Kalman filtering for state-parameter estimation in subsurface transport models

Journal of Hydrology ◽

10.1016/j.jhydrol.2013.12.004 ◽

2014 ◽

Vol 509 ◽

pp. 588-600 ◽

Cited By ~ 13

Author(s):

M.E. Gharamti ◽

I. Hoteit

Keyword(s):

Parameter Estimation ◽

Kalman Filtering ◽

State Parameter ◽

Low Rank ◽

Extended Kalman Filtering ◽

Transport Models ◽

Subsurface Transport

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COMPARISON OF GPS SMOOTHENING METHODS BETWEEN EXTENDED KALMAN FILTER AND PARTICLE FILTER FOR UAV

Journal of Applied Science, Engineering and Technology for Development ◽

10.33803/jasetd.2017.2-2.4 ◽

2017 ◽

Keyword(s):

Numerical Simulation ◽

Kalman Filter ◽

Particle Filter ◽

Extended Kalman Filter ◽

Kalman Filtering ◽

Particle Filtering ◽

State Vector ◽

The State ◽

Gps Data ◽

Extended Kalman Filtering

This paper presents a method for smoothing GPS data from a UAV using Extended Kalman filtering and particle filtering for navigation or position control. A key requirement for navigation and control of any autonomous flying or moving robot is availability of a robust attitude estimate. Consider a dynamic system such as a moving robot. The unknown parameters, e.g., the coordinates and the velocity, form the state vector. This time dependent vector may be predicted for any instant time by means of system equations. The predicted values can be improved or updated by observations containing information on some components of the state vector. The whole procedure is known as Kalman filtering. On the other hand, the particle filtering algorithm is to perform a recursive Bayesian filter by Monte Carlo simulations. The key is to represent the required posterior density function by a set of random samples, which is called particles with associated weights, and to compute estimates based on these samples as well as weights. We compare the two GPS smoothening methods: Extended Kalman Filter and Particle Filter for mobile robots applications. Validity of the smoothing methods is verified from the numerical simulation and the experiments. The numerical simulation and experimental results show the good GPS data smoothing performance using Extended Kalman filtering and particle filtering.

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