Modular General-Purpose Data Filtering for Tracking (Electrical/Electronics Project)
In nearly all modern tracking systems, signal processing is an important part with state estimation as the fundamental component. To evaluate and to reassess different tracking systems in an affordable way, simulations that are in accordance with reality are largely used. Simulation software that is composed of many different simulating modules, such as high level architecture (HLA) standardized software, is capable of simulating very realistic data and scenarios.
A modular and general-purpose state estimation functionality for ﬁltering provides a profound basis for simulating most modern tracking systems, which in this work is precisely what is created and implemented in an HLA-framework. Some of the most widely used estimators, the iterated Schmidt extended Kalman ﬁlter, the scaled unscented Kalman ﬁlter, and the particle ﬁlter, are chosen to form a toolbox of such functionality.
An indeed expandable toolbox that offers both unique and general features of each respective ﬁlter is designed and implemented, which can be utilized in not only tracking applications but in any application that is in need of fundamental state estimation. In order to prepare the user to make full use of this toolbox, the ﬁlters’ methods are described thoroughly, some of which are modiﬁed with adjustments that have been discovered in the process.
Furthermore, to utilize these ﬁlters easily for the sake of user-friendliness, a linear algebraic shell is created, which has very straight-forward matrix handling and uses BOOST UBLAS as the underlying numerical library. It is used for the implementation of the ﬁlters in C++, which provides a very independent and portable code.
Source: Linköping University
Author: Čirkić, Mirsad
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