Approximate Chernoff fusion of Gaussian mixtures for ballistic target tracking in the re-entry phase

Abstract

A track-to-track fusion method to combine local estimates modelled with Gaussian mixture model is proposed for tracking a re-entry ballistic vehicle. An arbitrary power of a Gaussian mixture distribution is approximated with Gaussian mixture model using first order expansion approximation, which leads to an analytical fusion equation for approximate Chernoff fusion. In the end, we verify the effectiveness of the proposed fusion algorithm with a series of Monte Carlo simulations.

Distributed track‐to‐track fusion for non‐linear systems with Gaussian mixture noise

“…This section provides a numerical example to evaluate the performance of the proposed GM fusion method when applied to a ballistic target tracking problem [29]. We consider a distributed filtering scenario with respect to the non‐linear non‐Gaussian system, in which the disturbances are modelled with GM noises.…”

Gaussian process‐based Bayesian non‐linear filtering for online target tracking

“…Estimating the internal state of a dynamic system from noisy observations is a common problem in the field of statistical signal processing [1–3], which finds various applications in navigation and guidance system such as radar tracking [4], sonar ranging [5] and satellite localisation [6]. One of the most popular techniques for state estimation are the Bayesian filtering methods in the context of stochastic dynamics systems, which computes the posterior probability density of the target system state recursively based on the Bayes’ theorem [7].…”

Trust-based distributed Kalman filtering for target tracking under malicious cyber attacks

“…Covariance intersection was proposed in [20] for fusion without knowing correlations. Since then, lots of variants have been proposed in [21,22,23,24] and the computational complexity is further reduced in [25,26,27,28]. Recently, ellipsoidal intersection is presented in [29], it provides smaller covariances than the bounds obtained with covariance intersection.…”