Abstract
A new method for non-linear state space estimation using genetic algorithm is developed. This method is named the Genetic algorithm filter (GA filter). State vactors are coded with genes and their survival rates are decided by a probability density functions. A crowd of genes represents the characteristic of a current state of a system. GA are general techniques for doing numerical optimization and are based on stochastic simulation. Their strength is that they can be applied to problems where an analytical approach is difficult or impossible. GA filter can handle non-linear system equations and measurement equations as well as non-Gaussian and non-additive noise. Implementing the GA requires less analytical work than implementing the extended Kalman filter, but the GA filters are more computer intensive. We will demonstrate effectiveness of the resulting new filters with simple examples.
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