Conditioning diffusions on a terminal bearing
Abstract
We study sampling of a diffusion on [0,T] conditioned on its terminal position lying on a prescribed ray, i.e. on an exactly observed bearing. For a linear drift we derive closed-form expressions for the Doob h-function and its gradient. For a broad class of tracking models admitting a Kronecker block structure, the guiding term is obtained from a single row of a matrix exponential, and the conditioned process decomposes into independent radial and angular components. For a nonlinear drift, we construct a guided process using the h-function of the linear model and prove absolute continuity of the law of the true conditioned process with respect to the law of the guided process, with an explicit likelihood ratio. This result is used to devise an auxiliary particle filter for bearings observed sequentially in time without noise. Numerical experiments show that the proposed filter approximates the filtering distribution and estimates the log-likelihood considerably more accurately than the shifted Rayleigh filter, at the cost of a higher computing time.
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BibTeX
@misc{meulen2026conditioning,
title = {{Conditioning diffusions on a terminal bearing}},
author = {Frank van der Meulen},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.13659},
eprint = {2610.13659},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.13659},
}
RIS
TY - UNPB
TI - Conditioning diffusions on a terminal bearing
AU - Frank van der Meulen
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.13659
UR - https://arxiv.org/abs/2610.13659
AB - We study sampling of a diffusion on [0,T] conditioned on its terminal position lying on a prescribed ray, i.e. on an exactly observed bearing. For a linear drift we derive closed-form expressions for the Doob h-function and its gradient. For a broad class of tracking models admitting a Kronecker block structure, the guiding term is obtained from a single row of a matrix exponential, and the conditioned process decomposes into independent radial and angular components. For a nonlinear drift, we construct a guided process using the h-function of the linear model and prove absolute continuity of the law of the true conditioned process with respect to the law of the guided process, with an explicit likelihood ratio. This result is used to devise an auxiliary particle filter for bearings observed sequentially in time without noise. Numerical experiments show that the proposed filter approximates the filtering distribution and estimates the log-likelihood considerably more accurately than the shifted Rayleigh filter, at the cost of a higher computing time.
ER -