| 摘要 |
A general framework for parametric filters is based on belief condensation (BC), which can cope with highly nonlinear and non-Gaussian system models. The methodology exploits the specific structure of the problem and decomposes it in such a way that the linear and Gaussian part can be solved in closed form, while the remaining parts are addressed by an optimization process, referred to as BC. Simulation results show that the performance of the proposed BC filter is close to that of the particle filter, but with a much lower complexity. |
