# Damped Posterior Linearization Filter

arXiv: https://arxiv.org/abs/1704.01113 · HTML: https://mlsanity.com/paper/1704.01113/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2017.

## Built on by (most cited first, 1 of 1)

- [Recursive Entropic Variational Inference for Nonlinear State-Space Models](https://mlsanity.com/paper/2511.15409/) (2025)

## Abstract (arXiv)

The iterated posterior linearization filter (IPLF) is an algorithm for Bayesian state estimation that performs the measurement update using iterative statistical regression. The main result behind IPLF is that the posterior approximation is more accurate when the statistical regression of measurement function is done in the posterior instead of the prior as is done in non-iterative Kalman filter extensions. In IPLF, each iteration in principle gives a better posterior estimate to obtain a better statistical regression and more accurate posterior estimate in the next iteration. However, IPLF may diverge. IPLF's fixed- points are not described as solutions to an optimization problem, which makes it challenging to improve its convergence properties. In this letter, we introduce a double-loop version of IPLF, where the inner loop computes the posterior mean using an optimization algorithm. Simulation results are presented to show that the proposed algorithm has better convergence than IPLF and its accuracy is similar to or better than other state-of-the-art algorithms.