In nearly allmodern 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 thesis 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.

<p>In nearly allmodern 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.</p><p>A modular and general-purpose state estimation functionality for filtering provides a profound basis for simulating most modern tracking systems, which in this thesis 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.</p><p>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.</p>