Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this study,  investigate online Bayesian estimation of the measurement noise density of a given state space model using particle filters and Dirichlet process mixtures. Dirichlet processes are widely used in statistics for nonparametric density estimation. In the proposed method, the unknown noise is modeled as a Gaussian mixture with unknown number of components. The joint estimation of the state and the noise density is done via particle filters. Furthermore, the number of components and the noise statistics are allod to vary in time. An extension of the method for the estimation of time varying noise characteristics is also introduced.
['Emre Ozkan', 'Saikat Saha', 'Fredrik Gustafsson', 'Vaclav Smidl']
I am writing a research paper on a idea Non-parametric bayesian measurement noise density estimation in non-linear filtering
[{"from": "human", "value": "I am writing a research paper on a idea Non-parametric bayesian measurement noise density estimation in non-linear filtering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this study,  investigate online Bayesian estimation of the measurement noise density of a given state space model using particle filters and Dirichlet process mixtures. Dirichlet processes are widely used in statistics for nonparametric density estimation. In the proposed method, the unknown noise is modeled as a Gaussian mixture with unknown number of components. The joint estimation of the state and the noise density is done via particle filters. Furthermore, the number of components and the noise statistics are allod to vary in time. An extension of the method for the estimation of time varying noise characteristics is also introduced."}]