Incremental Nonlinear System Identification and Adaptive Particle Filtering Using Gaussian Process
This work addresses the challenge of real-time nonlinear system identification for applications requiring adaptive filtering, though it appears incremental as it builds on existing Gaussian process and particle filter techniques.
The paper tackled the problem of identifying nonlinear Gaussian state space models by proposing an incremental online state dynamic learning method that embeds stochastic variational sparse Gaussian processes within a particle filter framework, resulting in significantly improved state estimation performance compared to batch learning methods.
An incremental/online state dynamic learning method is proposed for identification of the nonlinear Gaussian state space models. The method embeds the stochastic variational sparse Gaussian process as the probabilistic state dynamic model inside a particle filter framework. Model updating is done at measurement sample rate using stochastic gradient descent based optimization implemented in the state estimation filtering loop. The performance of the proposed method is compared with state-of-the-art Gaussian process based batch learning methods. Finally, it is shown that the state estimation performance significantly improves due to the online learning of state dynamics.