A Non-minimal State Kalman Filter for Nonlinear Parameter Estimation Applied to Autonomous Compliant Motion
نویسندگان
چکیده
This paper discusses a new Bayesian filter able to estimate the state of static systems (parameter estimation) with any kind of nonlinear measurement equation subject to Gaussian measurement uncertainty. The filter is also applicable to the state estimation for a limited class of dynamic systems. The core idea of the filter is to linearize the process and measurement equation in a higher-dimensional state space. The posterior probability density function (pdf) over the original state represents a subclass of the exponential distributions, hence, the new filter solves a subclass of the estimation problems solved by the Daum filter [1]. Its main advantage compared to the latter is a low computational complexity.
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