Work

Heavy-tailed noise modeling versus probabilistic data association for robust filtering

Year: 2025

Type: article

Abstract: A popular approach to robust filtering is to replace the standard Gaussian distribution for measurement noise with a heavier-tailed distribution. A recent example of this approach is presented and compared via simulations with the benchmark probabilistic data association filter. While both filters often perform similarly and clearly outperform the standard Kalman filter when outliers are present, the heavy-tailed noise modeling approach provides better consistency performance, is less prone to diverge, and is less sensitive to poor initialization in the majority of examples considered.

Source: Digital Signal Processing

DOI: 10.1016/j.dsp.2025.105712

Author: Michael J. Walsh

Institution: NUWC Newport Division

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,580

Funder: Naval Undersea Warfare Center