# Moving Horizon Estimation for ARMAX process with t-Distribution Noise

## TL;DR

This paper introduces a robust moving horizon estimator for ARMAX processes with t-distribution noise, improving outlier resistance and computational efficiency over traditional methods.

## Contribution

It proposes a novel recursive estimator based on influence functions for ARMAX models with t-distribution noise, enhancing robustness and speed.

## Key findings

- Estimator has smaller variance than least-squares
- More robust to outliers than traditional methods
- Significantly faster than particle filters

## Abstract

In this paper, instead of the usual Gaussian noise assumption, $t$-distribution noise is assumed. A Maximum Likelihood Estimator using the most recent N measurements is proposed for the Auto-Regressive-Moving-Average with eXogenous input (ARMAX) process with this assumption. The proposed estimator is robust to outliers because the `thick tail' of the t-distribution reduces the effect of large errors in the likelihood function. Instead of solving the resulting nonlinear estimator numerically, the Influence Function is used to formulate a computationally efficient recursive solution, which reduces to the traditional Moving Horizon Estimator when the noise is Gaussian. The formula for the variance of the estimate is derived. This formula shows explicitly how the variance of the estimate is affected by the number of measurements and noise variance. The simulation results show that the proposed estimator has smaller variance and is more robust to outliers than the Moving Window Least-Squares Estimator. For the same accuracy, the proposed estimator is an order of magnitude faster than the particle filter.