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The particle filter is one of the most successful methods for state inferenceand identification of general non-linear and non-Gaussian models. However,standard particle filters suffer from degeneracy of the particle weights, inparticular for high-dimensional problems. We propose a method for improving theperformance of the particle filter for certain challenging state space models,with implications for high-dimensional inference. First we approximate themodel by adding artificial process noise in an additional state update, then wedesign a proposal that combines the standard and the locally optimal proposal.This results in a bias-variance trade-off, where adding more noise reduces thevariance of the estimate but increases the model bias. The performance of theproposed method is empirically evaluated on a linear-Gaussian state space modeland on the non-linear Lorenz'96 model. For both models we observe a significantimprovement in performance over the standard particle filter.

Improving the particle filter in high dimensions using conjugate artificial process noise