VSE: Variational state estimation of complex model-free process
This work addresses state estimation for complex processes without known models, which is a problem for applications like tracking, though it appears incremental as it builds on existing variational and RNN-based approaches.
The authors tackled state estimation of complex model-free dynamical processes by designing a variational state estimation (VSE) method using recurrent neural networks (RNNs) to provide a closed-form Gaussian posterior, and demonstrated it on a stochastic Lorenz system tracking application, showing competitive performance against a particle filter and a recent data-driven method.
We design a variational state estimation (VSE) method that provides a closed-form Gaussian posterior of an underlying complex dynamical process from (noisy) nonlinear measurements. The complex process is model-free. That is, we do not have a suitable physics-based model characterizing the temporal evolution of the process state. The closed-form Gaussian posterior is provided by a recurrent neural network (RNN). The use of RNN is computationally simple in the inference phase. For learning the RNN, an additional RNN is used in the learning phase. Both RNNs help each other learn better based on variational inference principles. The VSE is demonstrated for a tracking application - state estimation of a stochastic Lorenz system (a benchmark process) using a 2-D camera measurement model. The VSE is shown to be competitive against a particle filter that knows the Lorenz system model and a recently proposed data-driven state estimation method that does not know the Lorenz system model.