Robust Wave Origin Detection from Sensor Array Data via Randomized Hough Transform and Model Fitting
Abstract
The robustness of methods for characterizing the origin of wave-like propagating signals is often challenged by the heterogeneity of transmitting media and sensor network. Detecting an individual wave under such noisy conditions remains particularly difficult. In this paper, we propose a robust framework to estimate wave origins from spatiotemporal data collected by a sensor array. First, a Butterworth filter is designed to extract relevant signals from each sensor channel. Next, a 3D randomized Hough transform is introduced to identify candidate signals originating from the same wave front. Finally, a wave propagation model is fitted to the identified signals using the least-squares method. By combining the iterative voting mechanism of the randomized Hough transform with least-squares optimization, our proposed method demonstrates superior robustness and computational efficiency in both simulation studies and real-world data analyses.
Related
- A Unified Framework for Randomized Row-Action Methods for Structured Data Fitting and Constraint Geometry
- A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints
- Transform before linearizing: robust Newton methods for singular p-Laplace and p-Stokes equations
- T-ARC: Topology-Aware Randomized Clustering via Distributionally Robust Stochastic Block Models
- Robust Quickest Change Detection with Cost-Efficient Experiment Design and Unknown Post-Change Model
BibTeX
@misc{fan2026robust,
title = {{Robust Wave Origin Detection from Sensor Array Data via Randomized Hough Transform and Model Fitting}},
author = {Sicheng Fan and Jiayi Lu and Xiaodan Fan},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.13248},
eprint = {2609.13248},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.13248},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Robust Wave Origin Detection from Sensor Array Data via Randomized Hough Transform and Model Fitting
AU - Sicheng Fan
AU - Jiayi Lu
AU - Xiaodan Fan
PY - 2026
DA - 2026/09/04
PB - arXiv
DO - 10.48550/arxiv.2609.13248
UR - https://arxiv.org/abs/2609.13248
AB - The robustness of methods for characterizing the origin of wave-like propagating signals is often challenged by the heterogeneity of transmitting media and sensor network. Detecting an individual wave under such noisy conditions remains particularly difficult. In this paper, we propose a robust framework to estimate wave origins from spatiotemporal data collected by a sensor array. First, a Butterworth filter is designed to extract relevant signals from each sensor channel. Next, a 3D randomized Hough transform is introduced to identify candidate signals originating from the same wave front. Finally, a wave propagation model is fitted to the identified signals using the least-squares method. By combining the iterative voting mechanism of the randomized Hough transform with least-squares optimization, our proposed method demonstrates superior robustness and computational efficiency in both simulation studies and real-world data analyses.
ER -