Particle Flows for Source Localization in 3-D Using TDOA Measurements

TL;DR

This paper introduces a Bayesian particle flow method for 3-D source localization using TDOA measurements, effectively handling nonlinearities, data association uncertainty, and unknown source count.

Contribution

The paper presents a novel particle flow localization algorithm that improves accuracy and robustness in complex TDOA-based 3-D source localization scenarios.

Findings

Accurately determines the number of sources.

Provides precise 3-D location estimates.

Stochastic flow outperforms deterministic flow in accuracy.

Abstract

Localization using time-difference of arrival (TDOA) has myriad applications, e.g., in passive surveillance systems and marine mammal research. In this paper, we present a Bayesian estimation method that can localize an unknown number of static sources in 3-D based on TDOA measurements. The proposed localization algorithm based on particle flow (PFL) can overcome the challenges related to the highly nonlinear TDOA measurement model, the data association (DA) uncertainty, and the uncertainty in the number of sources to be localized. Different PFL strategies are compared within a unified belief propagation (BP) framework in a challenging multisensor source localization problem. In particular, we consider PFL-based approximation of beliefs based on one or multiple Gaussian kernels with parameters computed using deterministic and stochastic flow processes. Our numerical results demonstrate that the proposed method can correctly determine the number of sources and provide accurate location estimates. The stochastic flow demonstrates greater accuracy compared to the deterministic flow when using the same number of particles.