Before revision:  Then the problem of concern is formalized as a task of nonlinear non-Gaussian state filtering under model uncertainty, which is addressed by a dynamic model averaging  based particle filter algorithm . Experimental results show that the proposed solution outperforms remarkably state-of-the-art methods.
After revision:  Then the problem of concern is formalized as a task of nonlinear non-Gaussian state filtering under model uncertainty, which is addressed by a dynamic model averaging based particle filter (PF) algorithm. This DMA algorithm employs 2^n models, while all models share the same state-transition function and a unique set of particle values. That makes the computational complexity of this algorithm only slightly larger than a single model based PF algorithm, especially for scenarios in which n is small . Experimental results show that the proposed solution outperforms remarkably state-of-the-art methods.