Sequential Monte Carlo Methods for System Identification
This work addresses system identification problems for researchers and engineers dealing with nonlinear models, but it is incremental as it builds on established SMC methods.
The paper tackles the challenge of identifying nonlinear and non-Gaussian state space models by using Sequential Monte Carlo methods, such as particle filters, to estimate system states and combine them with identification techniques for effective solutions.
One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo (SMC) methods, such as the particle filter (introduced more than two decades ago), provide numerical solutions to the nonlinear state estimation problems arising in SSMs. When combined with additional identification techniques, these algorithms provide solid solutions to the nonlinear system identification problem. We describe two general strategies for creating such combinations and discuss why SMC is a natural tool for implementing these strategies.