SPS Feed

This paper presents a neural-enhanced probabilistic model and corresponding factor graph-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The introduced hybrid probabilistic model consists of physics-based and data-driven measurement models capturing the information contained in both, the line-of-sight (LOS) component as well as in multipath components (NLOS components). The physics-based and data-driven models are embedded in a joint Bayesian framework allowing to derive from first principles a factor graph-based algorithm that fuses the information of these models.

In this article, we consider using time-of-arrival (TOA) measurements from a single moving receiver to locate a moving target at constant velocity that emits a periodic signal with unknown signal period. First, we give the TOA measurement model and deduce the Cram e´ r-Rao lower bounds (CRLB). Then, we formulate a nonlinear least squares (NLS) problem to estimate the unknown parameters. We use semidefinite programming (SDP) techniques to relax the nonconvex NLS problem.