Genetic Algorithm Filter for Non-Linear State Space Estimation.

Comparative Study of Particle Swarm Optimization and Genetic Algorithm Applied for Noisy Non-Linear Optimization Problems

Optimization of noisy non-linear problems plays a key role in engineering and design problems. These optimization problems can't be solved effectively by using conventional optimization methods. However, metaheuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) seem very efficient to approach in these problems and became very popular. The efficiency of these ...

Using the Unscented Kalman Filter and a Non-linear Two-track Model for Vehicle State Estimation

In order to evaluate the driving stability of a motor vehicle, the accurate determination of the vehicle sideslip angle is of significant importance. With the help of the sensor signals in today’s production vehicles, this state can only be determined with limited accuracy. We propose an algorithm for the determination and estimation of the vehicle state based on the Unscented Kalman Filter. In...

State estimation for linear and non-linear equality-constrained systems

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An improved fuzzy Kalman filter for state estimation of non-linear systems

Efficient Simulation and Integrated Likelihood Estimation in Non-Linear Non-Gaussian State Space Models

We propose a generic approach to inference in the non-linear, non-Gaussian state space model. This approach builds on recent developments in precision-based algorithms to estimating general state space models with multivariate observations and states. The baseline algorithm approximates the conditional distribution of the states by a multivariate t density, which is then used for integrated lik...