Increasing State Estimation Accuracy in the Inference Algorithm on a Hybrid Factor Graph Model

Abstract

We consider an intelligent agent that receives a continuous input from a signal-generating system and aims for estimating the discrete latent state of that system. The agent includes a switching linear dynamical system modeled as a hybrid factor graph for performing state estimation by determining the inference algorithm. We investigate the agent’s performance in relation to two Gaussian mixture reduction methods restricting Gaussian mixture growing while message passing, namely, the naive pruning implemented in the past and the realization of the Kullback-Leibler (KL) discrimination based approach defined by Runnalls (2007). For evaluation, we use simulated data provided with various signal-to-noise ratios. Reviewing the metrics, the agent reaches an improvement in state estimation accuracy by using the KL discrimination based method with minimally increasing computational effort. The findings of our work let us take one step towards establishing intelligent systems for modeling real-world systems with switching behavior capable to analyze noisy data sets.