# Robust Particle Filter by Dynamic Averaging of Multiple Noise Models

arXiv: https://arxiv.org/abs/1609.01336 · HTML: https://mlsanity.com/paper/1609.01336/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2016.

## Built on by (most cited first, 4 of 4)

- [Robust Dynamic Multi-Modal Data Fusion: A Model Uncertainty Perspective](https://mlsanity.com/paper/2105.06018/) (2021)
- [Consistent Relative Confidence and Label-Free Model Selection for Convolutional Neural Networks](https://mlsanity.com/paper/2108.11845/) (2021)
- ["Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us?](https://mlsanity.com/paper/2103.15294/) (2021)
- [Stochastic Expectation Maximization for Robust State-Space Radio Interferometric Imaging](https://mlsanity.com/paper/2606.23944/) (2026)

## Abstract (arXiv)

State filtering is a key problem in many signal processing applications. From a series of noisy measurement, one would like to estimate the state of some dynamic system. Existing techniques usually adopt a Gaussian noise assumption which may result in a major degradation in performance when the measurements are with the presence of outliers. A robust algorithm immune to the presence of outliers is desirable. To this end, a robust particle filter (PF) algorithm is proposed, in which the heavier tailed Student's t distributions are employed together with the Gaussian distribution to model the measurement noise. The effect of each model is automatically and dynamically adjusted via a Bayesian model averaging mechanism. The validity of the proposed algorithm is evaluated by illustrative simulations.