# Riemann-Langevin Particle Filtering in Track-Before-Detect

## TL;DR

This paper introduces the Riemann-Langevin particle filter, a novel approach that leverages Langevin Monte Carlo sampling to improve online state estimation in track-before-detect scenarios, especially under non-linear measurement models.

## Contribution

It presents the first application of Riemann-Langevin particle filtering to track-before-detect, enhancing proposal distributions by incorporating measurement information.

## Key findings

- Improved state estimation accuracy in low-noise scenarios.
- Enhanced particle filter performance by using measurement-informed proposals.
- Demonstrated effectiveness over traditional methods in challenging conditions.

## Abstract

Track-before-detect (TBD) is a powerful approach that consists in providing the tracker with sensor measurements directly without pre-detection. Due to the measurement model non-linearities, online state estimation in TBD is most commonly solved via particle filtering. Existing particle filters for TBD do not incorporate measurement information in their proposal distribution. The Langevin Monte Carlo (LMC) is a sampling method whose proposal is able to exploit all available knowledge of the posterior (that is, both prior and measurement information). This letter synthesizes recent advances in LMC-based filtering to describe the Riemann-Langevin particle filter and introduces its novel application to TBD. The benefits of our approach are illustrated in a challenging low-noise scenario.