Gaussian Target Tracking With Direction-of-Arrival von Mises–Fisher Measurements

Abstract

This is the accepted version of the original article published by IEEE.Abstract-This paper proposes a novel algorithm for target tracking with direction-of-arrival measurements, modelled by von Mises-Fisher distributions. The algorithm makes uses of the assumed density framework with Gaussian distributions, in which the posterior probability density of the target state is approximated by a Gaussian density. A key component of this algorithm is that the proposed Bayesian model of the measurements takes into account the specific characteristics of angular measurements by using a von Mises-Fisher distribution. We propose two implementations of the algorithm, one based on first-order Taylor series expansion and another one based on sigma points. Simulation results show the benefits of the proposed algorithms in relation to other Gaussian filters in the literature.

Cooperative Localization with Angular Measurements and Posterior Linearization

“…(x, 0) : x < 0. Instead of modeling the angular measurements by VMF distribution, as [14] has done, we adopt a simple adhoc correction from [22], which is described in Appendix A.…”

“…Achieving a ranging accuracy lower than 10 m by TOA/TDOA is very challenging in vehicular environments [10]. In contrast, AoA is readily available when the receiver is equipped with an antenna array [11]- [14]: [13] has investigated the performance of vehicle-to-vehicle (V2V) relative positioning using AoA measurements from multiple receiving arrays on the vehicle, and the achieved positioning accuracy met requirements of 5G New Radio (NR) vehicle-toeverything (V2X) standardization. While AoA measurements are attractive from a practical point of view, the integration in MP is non-trivial.…”

“…While the increasing number of particles gives better approximation performance, it also increases the computation complexity. To address this problem, [14] uses a von Mises-Fisher (VMF) model for the measurement likelihood and performs posterior linearization belief propagation (PLBP) [18], for a scenario with unknown positions but known orientation.…”

Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements

“…Other relevant approaches in the literature include, among other works: applications to sensor network localization [19]; algorithms based on direction-of-arrival measurements, modeled by von Mises-Fisher distributions [20]; pseudolinear estimators for 3D target motion analysis by a single moving ownship collecting azimuth and elevation angle measurements [21]; a methodology based on a bank of batch maximum a posteriori (MAP) estimators as a general estimation framework that provides the relinearization of the entire state trajectory, multihypothesis tracking and an efficient hypothesis generation scheme [22]; an approach based on Newton-Raphson methods and Particle Swarm Optimization [23]; a methodology to combine target motion compensation and track-before-detect methods within passive radar based on global navigation satellite systems (GNSS) for the detection of maritime targets [24]; TMA from cosines of conical angles acquired by a towed array [25]; and a new pseudolinear filter for bearings-only tracking without the requirement of bias compensation [26].…”

Cooperative Localization with Angular Measurements and Posterior Linearization

“…Besides, achieving a ranging accuracy lower than 10 m with TOA/TDOA is very challenging in vehicular environments [7]. In contrast, AoA is readily available when the receiver is equipped with an antenna array [8]- [11]: [10] has investigated the performance of vehicle-to-vehicle (V2V) relative positioning using AoA measurements from multiple receiving arrays on the vehicle, and the achieved positioning accuracy met requirements of 5G New Radio (NR) vehicle-to-everything (V2X) standardization.…”