A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Image-enhanced multiple model tracking
Automatica (Journal of IFAC)
A multiple model multiple hypothesis filter for Markovian switching systems
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
In this paper we present a framework in which the general hybrid filtering or state estimation problem can be formulated. The problem of joint tracking and classification can be formulated in this framework as well as the problem of multiple model filtering with additional mode observations. In this formulation the state vector is decomposed into a continuous (kinematic) component and a discrete (mode and/or class) component. We also suppose that there are two types of measurements. Measurements that are related to the continuous part of the state (e.g. bearing and range measurements in a radar application) and measurements that are related to the discrete part of the state (e.g. radar cross-section measurements). We will derive an optimal filter for this problem and will show how this filter can be implemented numerically.